CN116198522A - Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions - Google Patents

Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions Download PDF

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CN116198522A
CN116198522A CN202310493064.9A CN202310493064A CN116198522A CN 116198522 A CN116198522 A CN 116198522A CN 202310493064 A CN202310493064 A CN 202310493064A CN 116198522 A CN116198522 A CN 116198522A
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controller
control
unmanned
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mining
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CN116198522B (en
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叶青
高超俊
张垚
汪若尘
陈龙
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Jiangsu University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/02—Control of vehicle driving stability
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/26—Wheel slip
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/35—Road bumpiness, e.g. potholes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses an unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions, which comprises the following steps: step 1, establishing an unmanned mine card fourteen-degree-of-freedom vehicle dynamics model as a reference model; step 2, dynamically identifying dynamic parameters of the unmanned mine truck under the complex road working condition of the mine area by using a vehicle-mounted sensor; step 3, carrying out mode division on the driving working conditions according to the road parameters obtained in the step 2 and the real-time dynamic state parameters of the unmanned mining cards; step 4, designing an unmanned mining card transverse and vertical coupling intelligent hierarchical controller aiming at a generalized control target of transverse movement and vertical movement; and 5, updating the vehicle state, evaluating the control effect, and further performing corresponding intervention operation on the unmanned mine card until the path tracking is finished. The unmanned mining card path tracking control method provided by the invention effectively improves the path tracking precision of the unmanned mining card under the working condition of a complex mining area, and improves the efficiency and the safety of the unmanned mining card.

Description

Unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions
Technical Field
The invention relates to the technical field of intelligent vehicle motion control, in particular to an unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions.
Background
The unmanned mining truck is an important transportation device for modern mines, and has important significance for improving the production efficiency and the safety of the mine industry. Path tracking control is a key technology for realizing unmanned mining card. The path tracking research aims at ensuring the running safety and riding comfort, and the vehicle is driven according to a pre-planned path by controlling a steering method. The aim of path tracking is to accurately track the path by eliminating the angular deviation and the transverse deviation of the actual position and the expected position of the vehicle in the running process. The road environment of the mining area is complex, random road surface excitation is more, the road surface adhesion coefficient is easy to mutate, uncertainty is caused, and therefore, the conventional path tracking control method is not suitable for unmanned mining cards, and improvement is required.
At present, related research on unmanned mine card path tracking is mainly performed aiming at working conditions of a fixed route and a single environment, and unmanned mine card research aiming at working conditions of a complex mining area is less. The order of the mining card dynamics model adopted by the existing research is low, and the mining card dynamics model is not applicable because of larger errors under the complex working condition of a mining area.
Disclosure of Invention
In view of the above, in order to solve the technical problems that the unmanned mining card research aiming at the complex mining area working condition in the prior art is less, the adopted mining card dynamics model has lower order, and under the complex mining area working condition, a larger error exists, and the unmanned mining card transversal and vertical coupling hierarchical control method for the complex mining area working condition is provided, the unmanned mining card path tracking precision and the unmanned mining card path tracking stability can be improved, and therefore the unmanned mining card efficiency and the unmanned mining card safety are improved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a transverse and vertical coupling hierarchical control method for unmanned mining cards under complex mining area working conditions comprises the following steps:
step 1), establishing an unmanned mining card fourteen-degree-of-freedom whole vehicle dynamics reference model;
step 2), dynamically identifying dynamic parameters of the unmanned mine truck under the complex road working condition of the mine area by using a vehicle-mounted sensor;
step 3), carrying out mode division on the driving working conditions according to the road parameters in the complex road working conditions and the real-time dynamic state parameters in the dynamic parameters of the unmanned mining cards obtained in the step 2;
step 4), designing an unmanned mining card transverse coupling intelligent hierarchical controller and a vertical coupling intelligent hierarchical controller aiming at generalized control targets of transverse movement and vertical movement;
and 5) updating the vehicle state, evaluating the control effect, and performing corresponding intervention operation on the unmanned mine card until the path tracking is finished.
Preferably, step 1) establishes the unmanned mining card fourteen degrees of freedom whole vehicle dynamics reference model specifically as follows:
preferably, in step 2), a kalman filter observer is used to observe the tire slip rate in the dynamic parameters of the unmanned mine truck and the road surface unevenness power spectral density in the complex road conditions.
Preferably, the estimation of the tire slip rate is as follows:
from the slip definition it follows that:
Figure SMS_1
Figure SMS_5
in (1) the->
Figure SMS_7
For each wheel effective radius, obtained from the tire parameter approximation,
Figure SMS_3
for each tire rotational angular velocity, obtained by a wheel speed sensor, +.>
Figure SMS_4
The longitudinal speed of each wheel center is obtained by a speed sensor; selecting the sliding rate as the state variable of the system, namely +.>
Figure SMS_9
Selecting longitudinal acceleration, lateral acceleration and yaw acceleration of the vehicle body, i.e.)>
Figure SMS_11
Road adhesion coefficient input is ∈>
Figure SMS_2
If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:
Figure SMS_6
In the method, in the process of the invention,
Figure SMS_8
is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
Figure SMS_10
where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), and I is a unitary matrix. Let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.:
w(k)~N(0，Q(k))
v(k)~N(0，R(k))
wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise;
the workflow of the Kalman filter mainly comprises time update (prediction) and measurement update (correction)
The specific state observation process is as follows:
first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
Figure SMS_12
in (1) the->
Figure SMS_13
Predictive value representing the current time (k),>
Figure SMS_14
representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds to
Figure SMS_15
Can be expressed as:
Figure SMS_16
Wherein P (k|k-1) is the one corresponding to +.>
Figure SMS_17
P (k-1|k-1) is corresponding to +.>
Figure SMS_18
Is a covariance of (c).
Then, the predicted value is combined
Figure SMS_21
Observation value +.>
Figure SMS_22
The obtaining of the optimal estimated value at the current time can be expressed as:
Figure SMS_24
Wherein K is g For the Kalman gain, this can be expressed as:
Figure SMS_20
Finally, in order to keep the system state updated, the current time state also needs to be updated +.>
Figure SMS_23
Can be expressed as:
Figure SMS_25
Wherein (1)>
Figure SMS_26
Is an identity matrix. When the system enters (k+1), P (k|k) is +.>
Figure SMS_19
Covariance P (k-1|k-1), so that the filtering algorithm can proceed autoregressively; />
Classical power spectrum estimation is adopted for estimating the power spectrum density of the road surface unevenness, and random signals are input
Figure SMS_27
Is->
Figure SMS_32
Point sample value +.>
Figure SMS_35
Is regarded as an energy-limited signal and fourier transformed to give +.>
Figure SMS_29
On the basis of which the square of the amplitude is taken and +.>
Figure SMS_31
As->
Figure SMS_36
Real power spectrum->
Figure SMS_39
Is an estimate of (1), namely:
Figure SMS_28
The method comprises the steps of carrying out a first treatment on the surface of the Selecting the vehicle body displacement, the tyre dynamic deflection, the vehicle body vertical speed and the wheel axle vertical speed as the state variables of the system, namely
Figure SMS_34
The acceleration of the vehicle body and the vertical jumping acceleration of the tyre are selected as measurement variables, namely
Figure SMS_38
Road surface input is +.>
Figure SMS_40
If the system process noise (w) and the measurement noise are consideredSound (v), the continuous random state equation of which can be expressed as:
Figure SMS_30
In (1) the->
Figure SMS_33
Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
Figure SMS_37
where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix;
let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.: w (k) to N (0, Q (k))
v(k)~N(0，R(k))
Wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise;
the workflow of the Kalman filter mainly comprises two parts of time update (prediction) and measurement update (correction), and the specific state observation process is as follows: first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
Figure SMS_41
in the method, in the process of the invention,
Figure SMS_46
predictive value representing the current time (k),>
Figure SMS_50
representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds to +.>
Figure SMS_43
Can be expressed as:
Figure SMS_47
Wherein P (k|k-1) is the one corresponding to +.>
Figure SMS_52
P (k-1|k-1) is corresponding to +.>
Figure SMS_55
Is a covariance of (2); then, combine the predictive value +.>
Figure SMS_42
Observation value +.>
Figure SMS_48
The obtaining of the optimal estimated value at the current time can be expressed as:
Figure SMS_49
Wherein K is g For the Kalman gain, this can be expressed as:
Figure SMS_54
Finally, in order to keep the system state updated, the current time state also needs to be updated +.>
Figure SMS_44
Can be expressed as:
Figure SMS_45
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Figure SMS_51
Is an identity matrix. When the system enters (k+1), P (k|k) is +.>
Figure SMS_53
Covariance P (k-1|k-1) so that the filtering algorithm continues autoregressively.
Preferably, the current wheel slip rate and the road surface unevenness power spectrum density are obtained by the step 2), and the slip rate coefficient is defined after dimensionless treatment
Figure SMS_56
Road surface unevenness coefficient->
Figure SMS_59
Comparing the control modes with a built-in road working condition parameter identification module to activate different control modes;
Figure SMS_64
，
Figure SMS_57
Wherein (1)>
Figure SMS_61
For the maximum slip rate to be reached,
Figure SMS_62
for minimum slip, +.>
Figure SMS_65
The slip rate at the current moment;
Figure SMS_58
For maximum road power spectral density, +.>
Figure SMS_60
For minimum road power spectral density, < >>
Figure SMS_63
The road power spectral density at the current moment. Preferably, the driving scenario of the unmanned mining card road recognition module is divided into: common road surface, dry-wet mixed road surface, convex road surface and subsided road surface.
Preferably, three basic control stages in the hierarchical control method are respectively an organization stage, a coordination stage and an execution stage; wherein the tissue level is mainly based on the relative position information of the vehicle and the expected track, and the lateral acceleration of the vehicle
Figure SMS_66
And yaw rate>
Figure SMS_67
Road curvature->
Figure SMS_68
And preset the driving speed of the unmanned mine card +.>
Figure SMS_69
Obtaining a front wheel corner expected by a generalized control target of the unmanned mining card transverse and vertical coupling intelligent hierarchical control system>
Figure SMS_70
And control force of active suspension->
Figure SMS_71
The method comprises the steps of carrying out a first treatment on the surface of the The coordination level controller is mainly used for distributing tasks to all subsystems according to a control target of the path tracking control system, the running state of the unmanned mining card and the chassis cooperative control logic; the execution stage receives task targets distributed by the coordination stage, and realizes the requirement of the coordination stage according to the control strategy and the system characteristics of each subsystem, and finally realizes the path tracking of the intelligent automobile.
Preferably, the pre-aiming error model of the unmanned mining card established by the relative position of the unmanned mining card and the expected tracking path receives the transverse speed output by the unmanned mining card dynamics model in the step 1)
Figure SMS_81
And yaw rate>
Figure SMS_73
External input road reference curvature +.>
Figure SMS_78
And unmanned mining truck driving speed->
Figure SMS_75
As input to the pre-aiming error model, the lateral displacement deviation +.>
Figure SMS_79
And lateral orientation deviation->
Figure SMS_83
To direct the control of lateral movement, expressed as follows:
Figure SMS_88
Wherein:
Figure SMS_82
Is the lateral displacement deviation;
Figure SMS_85
Is the transverse speed;
Figure SMS_72
Is the longitudinal vehicle speed;
Figure SMS_77
Is the transverse azimuth deviation;
Figure SMS_84
Is yaw rate;
Figure SMS_87
Is the curvature of the road;
Figure SMS_89
For pretightening distance, & gt>
Figure SMS_91
For the rate of change of the lateral displacement deviation +.>
Figure SMS_80
Is the transverse azimuth deviation change rate; and carrying out dimensionless treatment on the transverse displacement deviation and the azimuth deviation, wherein the dimensionless treatment comprises the following formula:
Figure SMS_86
；
Figure SMS_90
In order to be a deviation of the maximum displacement in the lateral direction,
Figure SMS_92
is the transverse minimum displacement deviation;
Figure SMS_74
For the maximum lateral deviation, +.>
Figure SMS_76
Is the lateral minimum azimuth deviation.
Definition of integrated bias
Figure SMS_103
：
Figure SMS_93
In the method, in the process of the invention,λis a weight coefficient; preferably, the lateral movement controller in the tissue stage outputs the desired front wheel angle +.>
Figure SMS_99
The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller; an equivalent controller; defining a switching function:
Figure SMS_94
Wherein S is a switching function, +.>
Figure SMS_100
Is a sliding mode surface coefficient->
Figure SMS_104
Is the error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the fuzzy synovial membrane controller, the comprehensive consideration selects the index approach rate:
Figure SMS_107
Wherein s is the above-mentioned switching function, sgn(s) is a sign function,/->
Figure SMS_102
，
Figure SMS_108
Is approach rate parameter->
Figure SMS_96
For->
Figure SMS_98
Derivation to obtain equivalent control->
Figure SMS_106
The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller:
Figure SMS_110
Figure SMS_111
For switching the control coefficient function, +.>
Figure SMS_114
For approach rate parameter, ++>
Figure SMS_109
Is a sign function; substituting the vehicle transverse dynamics model to obtain ∈>
Figure SMS_112
The final fuzzy synovial membrane controller is as follows:
Figure SMS_113
In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>
Figure SMS_115
In the control process, the fuzzy control adjusts the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, and accordingly, the control rule is obtained as follows:
Figure SMS_95
After defuzzification, the fuzzy controller is:
Figure SMS_97
Figure SMS_101
Finally, the fuzzy sliding film controller outputs the front wheel angle +.>
Figure SMS_105
。
A vertical motion controller in a tissue stage outputs a desired control force of a force generator of an active suspension
Figure SMS_124
The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller; the equivalent controller defines a switching function:
Figure SMS_118
In->
Figure SMS_120
Is a sliding mode surface coefficient->
Figure SMS_119
For the roll angle error of the car body,
Figure SMS_122
Is the roll angle error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate:
Figure SMS_125
In (1) the->
Figure SMS_129
，
Figure SMS_126
Is approach rate parameter->
Figure SMS_133
For->
Figure SMS_117
Deriving, taking equivalent control in dynamics model>
Figure SMS_121
The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller:
Figure SMS_128
Substituting the vehicle transverse dynamics model to obtain ∈>
Figure SMS_131
The final sliding mode controller is as follows:
Figure SMS_132
Fuzzy controller in the fuzzy sliding mode controller, the input of the fuzzy controller is sliding mode surface +.>
Figure SMS_136
In the control process, the fuzzy control adjusts the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through the fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is maintained, so that the control rule is obtained as follows:
Figure SMS_134
After defuzzification, the fuzzy controller is:
Figure SMS_137
Figure SMS_135
The coordination level cooperatively controls the suspension system and the steering system according to the transverse and vertical coordination control logic of the unmanned mine truck chassis, wherein the control logic is as follows: />
Figure SMS_138
In the table of the present invention,
Figure SMS_116
、
Figure SMS_123
desired front wheel steering angle and desired suspension effort for tissue level output; steering-by-wire system AFS in the execution stage for controlling steering receives the steering angle output of the coordination stage>
Figure SMS_127
The active suspension system CDC controlling the attitude of the vehicle body receives the control force of the force generator of the coordination level +.>
Figure SMS_130
。
Preferably, step 5) is specifically: the curvature is optimized based on a genetic algorithm, the optimization process is to find a target path closest to an expected path for tracking, and the minimum problem is solved, so that the curvature is required to be subjected to scale conversion:
Figure SMS_150
in (1) the->
Figure SMS_142
Evaluation of the index as a fitness functionFThe design process of (2) is as follows:
Figure SMS_144
Wherein ρ is 2 ，ρ 1 D for optimizing path curvature and desired tracking path curvature, respectively e2 In order to correspond to the lateral deviation of the path, the optimized curvature in the fitness function should also meet the boundary condition requirements of the vehicle body stability:
Figure SMS_151
Vehicle body roll angle +.>
Figure SMS_155
Lateral acceleration->
Figure SMS_158
Dynamic vertical load of wheel->
Figure SMS_161
Calculating roll moment, reversely outputting control force through active suspension actuator to enable roll angle of vehicle body>
Figure SMS_147
Decay to approximately 0; wherein the roll moment mainly comprises: suspensionRoll moment due to centrifugal force of the hanging mass>
Figure SMS_154
Roll moment due to gravity of suspended mass>
Figure SMS_140
The vertical load is transferred at the left and right wheel loads at the time of rolling, and a load transfer moment is generated +.>
Figure SMS_143
:
Figure SMS_141
In->
Figure SMS_145
Is sprung mass, < >>
Figure SMS_149
Is centroid lateral acceleration>
Figure SMS_153
Is centroid height +>
Figure SMS_157
For roll angle of car body->
Figure SMS_159
For the track, ->
Figure SMS_156
Gravitational acceleration; wherein (1)>
Figure SMS_160
The method comprises the following steps:
Figure SMS_139
Figure SMS_146
Figure SMS_148
Figure SMS_152
。
In the middle of
Figure SMS_162
For the quality of the whole car, the weight of the whole car is increased>
Figure SMS_163
For centroid to front axis distance +.>
Figure SMS_164
Distance from center of mass to rear axle +.>
Figure SMS_165
Is centroid longitudinal acceleration->
Figure SMS_166
Is centroid lateral acceleration>
Figure SMS_167
Is the centroid height; compared with the prior art, the invention has the following beneficial effects:
1) The invention establishes a high-order mine card dynamics model, and the Kalman filter observer performs parameter identification to improve the model precision, thereby improving the path tracking precision of the complex environment of the mining area;
2) The invention adopts the transverse and vertical coupling intelligent hierarchical controller to realize the transverse and vertical cooperative control, thereby greatly improving the path tracking control effect.
Drawings
FIG. 1 is a flowchart of a Kalman filtering algorithm; FIG. 2 is a pre-aiming error model diagram; FIG. 3 is a logic diagram of a curvature optimization algorithm; fig. 4 is an unmanned mining card path tracking control logic roadmap.
Description of the embodiments
Based on the related research of the existing unmanned mine card path tracking in the prior art, the unmanned mine card path tracking method is mainly used for researching working conditions of a fixed route and a single environment, and the unmanned mine card research on the working conditions of a complex mining area is less. In addition, the order of the mining card dynamics model adopted by the existing research is lower, and under the complex working condition of the mining area, larger error exists,the invention provides an unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions (the following path tracking is an action process, and the expected path is a target to be approximated in the path tracking process), which specifically comprises the following steps: step 1), establishing an unmanned mining card fourteen-degree-of-freedom whole vehicle dynamics reference model, which is specifically as follows: longitudinal movement:
Figure SMS_185
lateral movement:
Figure SMS_190
yaw motion:
Figure SMS_194
roll motion:
Figure SMS_170
pitching motion:
Figure SMS_172
vertical movement of the vehicle body:
Figure SMS_176
Figure SMS_182
Wheel vertical movement:
Figure SMS_186
Figure SMS_189
Wheel rolling:
Figure SMS_195
Figure SMS_198
Wherein:
Figure SMS_193
、
Figure SMS_197
、
Figure SMS_199
、
Figure SMS_200
、
Figure SMS_179
、
Figure SMS_180
、
Figure SMS_187
The longitudinal speed, lateral speed, body roll angle, yaw angle, body pitch angle, front wheel steering angle and yaw rate of the vehicle, respectively.
Figure SMS_191
、
Figure SMS_168
The longitudinal and transverse forces of each tire are respectively determined.
Figure SMS_174
Suspension forces that are the points of attachment of the body to the suspension;
Figure SMS_178
、
Figure SMS_183
、
Figure SMS_171
、
Figure SMS_173
、
Figure SMS_177
、
Figure SMS_181
、
Figure SMS_184
、
Figure SMS_188
、
Figure SMS_192
Vehicle winding>
Figure SMS_196
Axle moment of inertia, vehicle winding->
Figure SMS_169
Axle moment of inertia, vehicle winding->
Figure SMS_175
Axle moment of inertia, roll center height, center of mass to roll center height, pitch center height, center of mass to front axle distance, center of mass to rear axle distance, 1/2 of front and rear wheel track.
Step 2), dynamically identifying the complex road working condition of the mining area and the dynamic parameters of the unmanned mining card by using a vehicle-mounted sensor, and preferably observing the tire slip rate in the dynamic parameters of the unmanned mining card and the road surface unevenness power spectrum density in the complex road working condition by using a Kalman filter observer, wherein the steps are as follows: the tire slip rate is estimated as follows: from the slip definition it follows that:
Figure SMS_211
Figure SMS_202
in (1) the->
Figure SMS_206
For each wheel effective radius, approximately obtained from the tire parameters, ->
Figure SMS_216
For each tire rotational angular velocity, obtained by a wheel speed sensor, +.>
Figure SMS_218
The longitudinal speed of each wheel center is obtained by a speed sensor; selecting a slip ratioFor state variables of the system, i.e.
Figure SMS_215
Selecting longitudinal acceleration, lateral acceleration and yaw acceleration of the vehicle body, i.e.)>
Figure SMS_217
Road adhesion coefficient input is ∈>
Figure SMS_212
If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:
Figure SMS_214
In (1) the->
Figure SMS_203
Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
Figure SMS_208
where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), and I is a unitary matrix. Let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.: w (k) -N (0, Q (k)) v (k) -N (0, R (k)) wherein Q (k) is the covariance of process noise and R (k) is the covariance of measurement noise; the workflow of the Kalman filter mainly comprises two parts of time update (prediction) and measurement update (correction), and the specific state observation process is as follows: first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
Figure SMS_201
in (1) the->
Figure SMS_207
Predictive value representing the current time (k),>
Figure SMS_209
representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds to
Figure SMS_213
Can be expressed as:
Figure SMS_204
Wherein P (k|k-1) is the one corresponding to +.>
Figure SMS_205
P (k-1|k-1) is corresponding to +.>
Figure SMS_210
Is a covariance of (c).
Then, the predicted value is combined
Figure SMS_237
Observation value +.>
Figure SMS_241
The obtaining of the optimal estimated value at the current time can be expressed as:
Figure SMS_245
Wherein K is g For the Kalman gain, this can be expressed as:
Figure SMS_222
Finally, in order to keep the system state updated, the current time state also needs to be updated +.>
Figure SMS_223
Can be expressed as:
Figure SMS_228
wherein (1)>
Figure SMS_231
Is an identity matrix. When the system enters (k+1), P (k|k) is
Figure SMS_248
Xiefang (prescription for harmonizing with Chinese medicine)Poor P (k-1|k-1), so that the filtering algorithm can proceed autoregressively; the road surface unevenness power spectrum density estimation adopts classical power spectrum estimation, and random signals are input +.>
Figure SMS_250
Is->
Figure SMS_252
Point sample value +.>
Figure SMS_254
Is regarded as an energy-limited signal and fourier transformed to give +.>
Figure SMS_249
On the basis of which the square of the amplitude is taken and +.>
Figure SMS_251
As->
Figure SMS_253
Real power spectrum->
Figure SMS_255
Is an estimate of (1), namely:
Figure SMS_236
Selecting the vehicle body displacement, the tyre dynamic deflection, the vehicle body vertical speed and the wheel axle vertical speed as the state variables of the system, namely +.>
Figure SMS_240
The acceleration of the vehicle body and the vertical jumping acceleration of the tyre are selected as measurement variables, namely +.>
Figure SMS_243
Road surface input is +.>
Figure SMS_247
If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:
Figure SMS_220
in (1) the->
Figure SMS_226
Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
Figure SMS_229
Where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix; let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.: w (k) -N (0, Q (k)) v (k) -N (0, R (k)) wherein Q (k) is the covariance of process noise and R (k) is the covariance of measurement noise; the workflow of the Kalman filter mainly comprises two parts of time update (prediction) and measurement update (correction), and the specific state observation process is as follows: first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
Figure SMS_233
in (1) the->
Figure SMS_227
Predictive value representing the current time (k),>
Figure SMS_234
representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds to
Figure SMS_238
Can be expressed as:
Figure SMS_242
Wherein P (k|k-1) is the one corresponding to +.>
Figure SMS_235
P (k-1|k-1) is corresponding to +.>
Figure SMS_239
Is a covariance of (2); then, combine the predictive value +.>
Figure SMS_244
Observation value +.>
Figure SMS_246
The obtaining of the optimal estimated value at the current time can be expressed as:
Figure SMS_219
wherein K is g For the Kalman gain, this can be expressed as:
Figure SMS_224
finally, in order to keep the system state updated, the current time state also needs to be updated +.>
Figure SMS_230
Can be expressed as:
Figure SMS_232
Wherein (1)>
Figure SMS_221
Is a unit matrix; when the system enters (k+1), P (k|k) is +.>
Figure SMS_225
Covariance P (k-1|k-1) so that the filtering algorithm continues autoregressively.
And 3) carrying out mode division on the driving working conditions according to the road parameters obtained in the step 2) and the real-time dynamic state parameters of the unmanned mining cards. Obtaining the current wheel slip rate and road surface unevenness power spectrum density by the step 2), and defining a slip rate coefficient after dimensionless processing
Figure SMS_257
Road surface unevenness coefficient->
Figure SMS_259
Comparing with the built-in road working condition parameter identification module, thereby activating the vehicle withoutThe same control mode. The driving situations of the unmanned mining card road recognition module are divided into four major categories: 1. ordinary road surfaces; 2. dry-wet mixed road surface; 3. a bump road surface; 4. sedimentation road surface->
Figure SMS_263
Figure SMS_256
Wherein,,
Figure SMS_261
for maximum slip, +.>
Figure SMS_264
For minimum slip, +.>
Figure SMS_265
For the slip rate at the present moment>
Figure SMS_258
For maximum road power spectral density, +.>
Figure SMS_260
For minimum road power spectral density, < >>
Figure SMS_262
The road power spectral density at the current moment.
Step 4), designing an unmanned mining card transverse and vertical coupling intelligent hierarchical controller aiming at a generalized control target of transverse movement and vertical movement, wherein the intelligent hierarchical controller comprises the following specific steps: the three basic control stages of intelligent hierarchical control are respectively an organization stage, a coordination stage and an execution stage. Wherein the tissue level is based on the relative position information of the vehicle and the expected track, and the lateral acceleration of the vehicle
Figure SMS_266
And yaw rate
Figure SMS_267
Road curvature->
Figure SMS_268
And preset the driving speed of the unmanned mine card +.>
Figure SMS_269
Obtaining a front wheel corner expected by a generalized control target of the unmanned mining card transverse and vertical coupling intelligent hierarchical control system>
Figure SMS_270
And control force of active suspension->
Figure SMS_271
The method comprises the steps of carrying out a first treatment on the surface of the The coordination level controller is used for distributing tasks to all subsystems mainly according to a control target of the path tracking control system, the running state of the unmanned mining card and the chassis cooperative control logic; the execution stage receives task targets distributed by the coordination stage, and achieves the requirements of the coordination stage according to the control strategy and the system characteristics of each subsystem, and finally achieves efficient and stable path tracking of the intelligent automobile.
Receiving the transverse speed output by the unmanned mining card dynamics model in the step 1) according to the pre-aiming error model of the unmanned mining card established according to the relative position of the unmanned mining card and the expected tracking path
Figure SMS_281
And yaw rate>
Figure SMS_272
External input road reference curvature +.>
Figure SMS_278
And unmanned mining truck driving speed->
Figure SMS_286
As input to the pre-aiming error model, the lateral displacement deviation +.>
Figure SMS_289
And lateral orientation deviation->
Figure SMS_288
To guide the control of the lateral movement. The expression is as follows:
Figure SMS_290
Wherein:
Figure SMS_283
Is the lateral displacement deviation;
Figure SMS_287
Is the transverse speed;
Figure SMS_275
Is the longitudinal vehicle speed;
Figure SMS_277
Is the transverse azimuth deviation;
Figure SMS_273
Is yaw rate;
Figure SMS_279
Is the curvature of the road;
Figure SMS_282
For pretightening distance, & gt>
Figure SMS_285
For the rate of change of the lateral displacement deviation +.>
Figure SMS_274
Is the transverse azimuth deviation change rate; and carrying out dimensionless treatment on the transverse displacement deviation and the azimuth deviation, wherein the dimensionless treatment comprises the following formula:
Figure SMS_276
Definition of the Integrated deviation->
Figure SMS_280
：
Figure SMS_284
In the method, in the process of the invention,λis a weight coefficient.
To reduce the integrated offset, the traversing motion controller in the tissue stage outputs a desired front wheel steering angle
Figure SMS_291
The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller.
Equivalent controller
Defining a switching function:
Figure SMS_300
wherein S is a switching function, +.>
Figure SMS_294
Is a sliding mode surface coefficient->
Figure SMS_296
Is the error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate:
Figure SMS_306
Wherein s is the above-mentioned switching function, sgn(s) is a sign function,/->
Figure SMS_308
,
Figure SMS_309
Is an approach rate parameter
Figure SMS_311
For->
Figure SMS_301
Derivation to obtain equivalent control->
Figure SMS_304
The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller:
Figure SMS_292
Figure SMS_299
for switching the control coefficient function, +.>
Figure SMS_293
For approach rate parameter, ++>
Figure SMS_298
Is a sign function; substituting the vehicle transverse dynamics model to obtain ∈>
Figure SMS_302
The final sliding mode controller is as follows:
Figure SMS_307
In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>
Figure SMS_297
In the control process, the fuzzy control can adjust the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through the fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is maintained, so that the control rule is obtained as follows:
Figure SMS_303
After defuzzification, the fuzzy controller is:
Figure SMS_305
Figure SMS_310
To achieve control of the body position, the vertical motion controller outputs the desired control force of the force generator of the active suspension in the tissue level +.>
Figure SMS_295
The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller;
the equivalent controller defines a switching function:
Figure SMS_322
in->
Figure SMS_312
Is a sliding mode surface coefficient->
Figure SMS_316
Is the roll angle error of the vehicle body,
Figure SMS_326
is the roll angle error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate:
Figure SMS_331
In (1) the->
Figure SMS_330
,
Figure SMS_333
Is approach rate parameter->
Figure SMS_323
For->
Figure SMS_327
Deriving, taking equivalent control in dynamics model>
Figure SMS_315
The method comprises the steps of carrying out a first treatment on the surface of the The handover controller defines a handover controller:
Figure SMS_318
Substituting the vehicle transverse dynamics model to obtain ∈>
Figure SMS_324
The final sliding mode controller is as follows:
Figure SMS_328
In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>
Figure SMS_329
During control, the dieThe paste control can adjust the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is kept, so that the control rule is obtained as follows:
Figure SMS_332
After defuzzification, the fuzzy controller is:
Figure SMS_313
Figure SMS_319
The coordination level cooperatively controls the suspension system and the steering system according to the transverse and vertical coordination control logic of the unmanned mine truck chassis. Wherein the control logic is as follows: />
Figure SMS_321
In the table of the present invention,
Figure SMS_325
、
Figure SMS_314
desired front wheel steering angle and desired suspension effort for tissue level output; steering-by-wire system AFS in the execution stage for controlling steering receives the steering angle output of the coordination stage>
Figure SMS_317
The active suspension system CDC controlling the attitude of the vehicle body receives the control force of the force generator of the coordination level +.>
Figure SMS_320
。
And 5) updating the vehicle state, evaluating the control effect, and further performing corresponding intervention operation on the unmanned mine card until the path tracking is finished, wherein the method comprises the following steps of: the curvature optimization system is realized by analyzing different paths based on the completed path tracking control systemVehicle body stability under working conditions, determining stability constraint boundary conditions in the optimization process by means of vehicle yaw rate, lateral acceleration and roll angle, and then optimizing algorithm according to known expected path curvature
Figure SMS_334
And +.>
Figure SMS_335
Finding the actual driving path nearest to the original expected path within the constraint range +.>
Figure SMS_336
The corresponding optimized desired path +.>
Figure SMS_337
The tracking error is input to a path tracking controller, which performs tracking control again by taking the path as a tracking path>
Figure SMS_338
Reduced to->
Figure SMS_339
The tracking precision is improved, and meanwhile stability constraint ensures that the stability of the vehicle body is changed within a reasonable range, so that the comprehensive improvement of the tracking effect is realized.
The invention adopts a method for optimizing the curvature based on a genetic algorithm. The optimization process is to find the target path closest to the expected path for tracking, which is the minimum problem, so that the target path needs to be subjected to scale conversion:
Figure SMS_358
in (1) the->
Figure SMS_361
Evaluation of the index as a fitness functionFThe design process of (2) is as follows:
Figure SMS_363
Wherein ρ2 and ρ1 are respectively the optimized path curvesRate and desired tracking path curvature, d e2 In order to correspond to the lateral deviation of the path, the optimized curvature in the fitness function should also meet the boundary condition requirements of the vehicle body stability:
Figure SMS_340
Vehicle body roll angle +.>
Figure SMS_345
Lateral acceleration->
Figure SMS_349
Dynamic vertical load of wheel->
Figure SMS_352
Calculating the roll moment and further reversely outputting the control force through the active suspension actuator to enable the roll angle of the vehicle body to be +.>
Figure SMS_350
Decay to approximately 0; wherein the roll moment mainly comprises: roll moment due to centrifugal force of suspended mass>
Figure SMS_354
Roll moment due to gravity of suspended mass>
Figure SMS_356
The vertical load is transferred at the left and right wheel loads at the time of rolling, and a load transfer moment is generated +.>
Figure SMS_359
：
Figure SMS_357
In->
Figure SMS_360
Is sprung mass, < >>
Figure SMS_362
Is centroid lateral acceleration>
Figure SMS_364
Is centroid height->
Figure SMS_341
For roll angle of car body->
Figure SMS_347
For the track, ->
Figure SMS_351
Gravitational acceleration; wherein (1)>
Figure SMS_355
The method comprises the following steps:
Figure SMS_343
Figure SMS_344
Figure SMS_348
Figure SMS_353
The control objective of the vertical control of the invention is to enable the body to lean
Figure SMS_342
As infinitely approaching zero as possible, the control forces of the force generators of the individual active suspensions are inversely determined by the relationship of the load transfer and the roll angle of the vehicle body>
Figure SMS_346
。
The above is only a preferred embodiment of the present invention; the scope of the invention is not limited in this respect. Any person skilled in the art, within the technical scope of the present disclosure, may apply to the present invention, and the technical solution and the improvement thereof are all covered by the protection scope of the present invention.

Claims (9)

1. The unmanned mining card transverse and vertical coupling hierarchical control method for the complex mining area working conditions is characterized by comprising the following steps of:
step 1), establishing an unmanned mining card fourteen-degree-of-freedom whole vehicle dynamics reference model;
step 2), dynamically identifying dynamic parameters of the unmanned mine truck under the complex road working condition of the mine area by using a vehicle-mounted sensor;
step 3), carrying out mode division on the driving working conditions according to the road parameters in the complex road working conditions and the real-time dynamic state parameters in the dynamic parameters of the unmanned mining cards obtained in the step 2;
step 4), designing an unmanned mining card transverse coupling intelligent hierarchical controller and a vertical coupling intelligent hierarchical controller aiming at generalized control targets of transverse movement and vertical movement;
and 5) updating the vehicle state, evaluating the control effect, and performing corresponding intervention operation on the unmanned mine card until the path tracking is finished.
2. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working conditions according to claim 1, wherein in the step 2), a Kalman filter observer is used for observing the tire slip rate in the unmanned mining truck dynamics parameters and the road surface unevenness power spectral density in the complex road working conditions.
3. The unmanned mining truck transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 2, wherein the estimation of the tire slip rate is as follows:
from the slip definition it follows that:
Figure QLYQS_1
Figure QLYQS_7
in (1) the->
Figure QLYQS_10
For each wheel effective radius, approximately obtained from the tire parameters, ->
Figure QLYQS_3
For each tire rotational angular velocity, obtained by a wheel speed sensor, +.>
Figure QLYQS_4
The longitudinal speed of each wheel center is obtained by a speed sensor; selecting the slip rate as a state variable of the system, i.e
Figure QLYQS_6
Selecting longitudinal acceleration, lateral acceleration, yaw acceleration of the body, i.e. +.>
Figure QLYQS_9
Road adhesion coefficient input is ∈>
Figure QLYQS_2
If the system process noise (w) and the measurement noise (v) are considered, the continuous random state equation can be expressed as:
Figure QLYQS_5
In (1) the->
Figure QLYQS_8
Is a system matrix; x (t) is a state variable, U (t) is a known external input variable; discretizing a continuous random system with a sampling period of Ts, the discrete control process of the system can be expressed as follows:
Figure QLYQS_11
where k=t/Ts, a (k) = (i+tsa (t)), B (k) =tsb (t), I is a unitary matrix;
let w (k), v (k) be gaussian white noise independent of each other and subject to normal distribution, i.e.:
w(k)~N(0，Q(k))
v(k)~N(0，R(k))
wherein Q (k) is the covariance of the process noise, and R (k) is the covariance of the measurement noise; the workflow of the Kalman filter mainly comprises time update (prediction) and measurement update (correction)
The specific state observation process is as follows:
first, the system state update, i.e. predicting the system state at the next moment by using the system process model, can be expressed as:
Figure QLYQS_12
in (1) the->
Figure QLYQS_16
Predictive value representing the current time (k),>
Figure QLYQS_22
representing an optimal estimate of the last time instant (k-1); subsequently, the update corresponds to
Figure QLYQS_14
Can be expressed as:
Figure QLYQS_18
Wherein P (k|k-1) is the one corresponding to +.>
Figure QLYQS_23
P (k-1|k-1) is corresponding to +.>
Figure QLYQS_26
Is a covariance of (2); then, combine the predictive value +.>
Figure QLYQS_13
Observation value +.>
Figure QLYQS_17
The obtaining of the optimal estimated value at the current time can be expressed as:
Figure QLYQS_21
wherein K is g For the Kalman gain, this can be expressed as:
Figure QLYQS_24
finally, in order to keep the system state updated, the current time state also needs to be updated +.>
Figure QLYQS_15
Can be expressed as:
Figure QLYQS_19
Wherein (1)>
Figure QLYQS_20
Is a unit matrix; when the system enters (k+1), P (k|k) is +.>
Figure QLYQS_25
Covariance P (k-1|k-1) so that the filtering algorithm continues autoregressively.
4. The unmanned mining truck transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 3, wherein the current wheel slip rate and road surface unevenness power spectrum density are obtained in the step 2), and slip rate coefficients are defined after dimensionless processing
Figure QLYQS_29
Road surface unevenness coefficient->
Figure QLYQS_32
Comparing the control modes with a built-in road working condition parameter identification module to activate different control modes;
Figure QLYQS_33
Figure QLYQS_28
Wherein (1)>
Figure QLYQS_31
For maximum slip, +.>
Figure QLYQS_35
For minimum slip, +.>
Figure QLYQS_36
The slip rate at the current moment;
Figure QLYQS_27
For maximum road power spectral density, +.>
Figure QLYQS_30
For minimum road power spectral density, < >>
Figure QLYQS_34
The road power spectral density at the current moment.
5. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working condition according to claim 4, wherein the driving scene of the unmanned mining truck road identification module is divided into: common road surface, dry-wet mixed road surface, convex road surface and subsidence road surface.
6. The unmanned mining truck transverse and vertical coupling hierarchical control method for the complex mining area working condition according to claim 1, wherein three basic control stages in the hierarchical control method are respectively an organization stage, a coordination stage and an execution stage; wherein the tissue level is mainly based on the relative position information of the vehicle and the expected track, and the lateral acceleration of the vehicle
Figure QLYQS_37
And yaw rate>
Figure QLYQS_38
Road curvature->
Figure QLYQS_39
And preset the driving speed of the unmanned mine card +.>
Figure QLYQS_40
Obtaining a front wheel corner expected by a generalized control target of the unmanned mining card transverse and vertical coupling intelligent hierarchical control system>
Figure QLYQS_41
And control force of active suspension->
Figure QLYQS_42
The coordination level controller is mainly used for distributing tasks to all subsystems according to a control target of the path tracking control system, the running state of the unmanned mining card and the chassis cooperative control logic; the execution stage receives task targets distributed by the coordination stage, and realizes the requirement of the coordination stage according to the control strategy and the system characteristics of each subsystem, and finally realizes the path tracking of the intelligent automobile.
7. The unmanned mining card transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 1, wherein the unmanned mining card pre-aiming error model established by the relative positions of the unmanned mining card and the expected tracking path receives the transverse speed outputted by the unmanned mining card dynamics model in the step 1)
Figure QLYQS_58
And yaw rate>
Figure QLYQS_53
External input road reference curvature +.>
Figure QLYQS_54
And unmanned mining truck driving speed->
Figure QLYQS_56
As input to the pre-aiming error model, the lateral displacement deviation +.>
Figure QLYQS_64
And lateral azimuth deviation
Figure QLYQS_63
To direct the control of lateral movement, expressed as follows:
Figure QLYQS_65
Wherein:
Figure QLYQS_51
Is the lateral displacement deviation;
Figure QLYQS_57
Is the transverse speed;
Figure QLYQS_44
Is the longitudinal vehicle speed;
Figure QLYQS_47
Is the transverse azimuth deviation;
Figure QLYQS_59
Is yaw rate;
Figure QLYQS_61
Is the curvature of the road;
Figure QLYQS_60
for pretightening distance, & gt>
Figure QLYQS_62
For the rate of change of the lateral displacement deviation +.>
Figure QLYQS_45
Is the transverse azimuth deviation change rate; and carrying out dimensionless treatment on the transverse displacement deviation and the azimuth deviation, wherein the dimensionless treatment comprises the following formula:
Figure QLYQS_48
Figure QLYQS_52
For the lateral maximum displacement deviation +.>
Figure QLYQS_55
Is the transverse minimum displacement deviation;
Figure QLYQS_43
For the maximum lateral deviation, +.>
Figure QLYQS_49
Is the lateral minimum azimuth deviation; definition of the Integrated deviation->
Figure QLYQS_46
Figure QLYQS_50
In the method, in the process of the invention,λis a weight coefficient.
8. The unmanned mining truck transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 6, wherein,
the lateral motion controller in the tissue stage outputs a desired front wheel steering angle
Figure QLYQS_66
The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller;
equivalent controller
Defining a switching function:
Figure QLYQS_67
wherein S is a switching function, +.>
Figure QLYQS_70
Is a sliding mode surface coefficient->
Figure QLYQS_73
Is the error change rate; to meet the controlThe corresponding speed of the controller can reduce the influence of buffeting on the fuzzy synovial membrane controller, and the index approach rate is comprehensively considered and selected:
Figure QLYQS_69
Wherein s is the above-mentioned switching function, sgn(s) is a sign function,/->
Figure QLYQS_72
，
Figure QLYQS_74
Is an approach rate parameter
Figure QLYQS_75
For->
Figure QLYQS_68
Derivation to obtain equivalent control->
Figure QLYQS_71
；
Switching controller
Defining a switching controller:
Figure QLYQS_76
；
Figure QLYQS_79
for switching the control coefficient function, +.>
Figure QLYQS_81
For approach rate parameter, ++>
Figure QLYQS_85
Is a sign function; substituting the vehicle transverse dynamics model to obtain ∈>
Figure QLYQS_78
The final fuzzy synovial membrane controller is as follows:
Figure QLYQS_82
In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface +.>
Figure QLYQS_83
In the control process, the fuzzy control adjusts the equivalent control part and the switching control part in the sliding mode controller according to the sliding mode surface state, and accordingly, the control rule is obtained as follows:
Figure QLYQS_86
After defuzzification, the fuzzy controller is:
Figure QLYQS_77
Figure QLYQS_80
Finally, the fuzzy sliding film controller outputs the front wheel angle +.>
Figure QLYQS_84
A vertical motion controller in a tissue stage outputs a desired control force of a force generator of an active suspension
Figure QLYQS_87
The fuzzy sliding film controller is designed, and the design of the fuzzy sliding film controller comprises three parts in total: an equivalent controller, a switching controller and a fuzzy controller;
equivalent controller
Defining a switching function:
Figure QLYQS_90
in->
Figure QLYQS_93
Is a sliding mode surface coefficient->
Figure QLYQS_95
For the roll angle error of the car body,
Figure QLYQS_88
Is the roll angle error change rate; in order to meet the corresponding speed of the controller and reduce the influence of buffeting on the controller, the comprehensive consideration selects the index approach rate:
Figure QLYQS_92
In (1) the->
Figure QLYQS_96
，
Figure QLYQS_97
Is approach rate parameter->
Figure QLYQS_89
For->
Figure QLYQS_91
Deriving, taking equivalent control in dynamics model>
Figure QLYQS_94
；
Switching controller
Defining a switching controller:
Figure QLYQS_98
substituting the vehicle transverse dynamics model to obtain ∈>
Figure QLYQS_99
The final sliding mode controller is as follows:
Figure QLYQS_100
；
Fuzzy controller
In the fuzzy sliding mode controller, the input of the fuzzy controller is a sliding mode surface
Figure QLYQS_101
In the control process, the fuzzy control is based onThe sliding mode surface state is used for adjusting an equivalent control part and a switching control part in the sliding mode controller, namely when the system state is far away from the sliding mode surface, the switching control is required to be added through fuzzy control, and when the system is close to the sliding mode surface, the original equivalent part is kept, so that the control rule is obtained as follows:
Figure QLYQS_102
After defuzzification, the fuzzy controller is:
Figure QLYQS_103
Figure QLYQS_104
the coordination level cooperatively controls the suspension system and the steering system according to the transverse and vertical coordination control logic of the unmanned mine truck chassis, wherein the control logic is as follows: />
Figure QLYQS_105
；
In the table of the present invention,
Figure QLYQS_106
，
Figure QLYQS_107
desired front wheel steering angle and desired suspension effort for tissue level output; the steer-by-wire system AFS in the executive stage controlling the steering receives the corner output of the coordinator stage +.>
Figure QLYQS_108
The active suspension system CDC controlling the attitude of the vehicle body receives the control force of the force generator of the coordination level +.>
Figure QLYQS_109
。
9. The unmanned mining truck transverse and vertical coupling hierarchical control method for complex mining area working conditions according to claim 8, wherein the method comprises the following steps ofThe step 5) is specifically as follows: the curvature is optimized based on a genetic algorithm, the optimization process is to find a target path closest to an expected path for tracking, and the minimum problem is solved, so that the curvature is required to be subjected to scale conversion:
Figure QLYQS_112
in (1) the->
Figure QLYQS_117
Evaluation of the index as a fitness functionFThe design process of (2) is as follows:
Figure QLYQS_120
Wherein ρ is 2 ，ρ 1 D for optimizing path curvature and desired tracking path curvature, respectively e2 In order to correspond to the lateral deviation of the path, the optimized curvature in the fitness function should also meet the boundary condition requirements of the vehicle body stability:
Figure QLYQS_113
Vehicle body roll angle +.>
Figure QLYQS_114
Lateral acceleration->
Figure QLYQS_119
Dynamic vertical load of wheel->
Figure QLYQS_123
Calculating roll moment, reversely outputting control force through active suspension actuator to enable roll angle of vehicle body>
Figure QLYQS_111
Decay to approximately 0; wherein the roll moment mainly comprises: roll moment due to centrifugal force of suspended mass>
Figure QLYQS_115
Roll moment due to gravity of suspended mass>
Figure QLYQS_121
The vertical load is transferred at the left and right wheel loads at the time of rolling, and a load transfer moment is generated +.>
Figure QLYQS_124
：
Figure QLYQS_126
In->
Figure QLYQS_133
Is sprung mass, < >>
Figure QLYQS_136
Is centroid lateral acceleration>
Figure QLYQS_138
Is centroid height->
Figure QLYQS_128
For roll angle of car body->
Figure QLYQS_131
For the track, ->
Figure QLYQS_134
Gravitational acceleration; wherein (1)>
Figure QLYQS_137
The method comprises the following steps:
Figure QLYQS_110
，
Figure QLYQS_116
Figure QLYQS_118
Figure QLYQS_125
In->
Figure QLYQS_122
For the quality of the whole car, the weight of the whole car is increased>
Figure QLYQS_129
For centroid to front axis distance +.>
Figure QLYQS_132
Distance from center of mass to rear axle +.>
Figure QLYQS_135
Is centroid longitudinal acceleration->
Figure QLYQS_127
Is centroid lateral acceleration>
Figure QLYQS_130
Is the centroid height. />
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