CN114089637B - Multi-mode robust active disturbance rejection motion control method and system - Google Patents

Multi-mode robust active disturbance rejection motion control method and system Download PDF

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CN114089637B
CN114089637B CN202210063377.6A CN202210063377A CN114089637B CN 114089637 B CN114089637 B CN 114089637B CN 202210063377 A CN202210063377 A CN 202210063377A CN 114089637 B CN114089637 B CN 114089637B
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CN114089637A (en
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王常虹
王冠
夏红伟
马广程
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Harbin Institute of Technology
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Shenrui Technology Beijing Co ltd
Harbin Institute of Technology
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Abstract

The invention relates to a multi-mode robust active disturbance rejection motion control method and a system. The method comprises the following steps which are executed in sequence: judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method and the system can realize that the control method can correct an ideal control instruction to obtain an actual control instruction, inhibit transient response of a controlled system when different modal control rules are converted, realize control of a multi-modal system, improve control precision and stability of the controlled system, and enable the multi-modal controlled system to have stronger robustness.

Description

Multi-mode robust active disturbance rejection motion control method and system
Technical Field
The invention relates to the technical field of motion control, in particular to a multi-mode robust active disturbance rejection motion control method and system.
Background
Motion control techniques are widely used in today's industry, such as motor motion control, numerically controlled machine tools, robotic control, etc. The motion control means controlling the amount of motion such as position/displacement, velocity, acceleration, and the like. Compared with other types of motion actuators, the motor adopted as the motion actuator has the advantages of simple structure, quick response, high precision and efficiency and the like, is beneficial to realizing high-performance motion control such as high speed or low speed, high precision and the like, and has wide application prospect in the fields of modern industry, civilian use, medical treatment, transportation, military use and the like.
Because the motion control system has the influence of factors such as friction, system parameter change, load disturbance force and the like, in particular to system nonlinear factors (such as signal measurement noise) and uncertain interference, the motion precision of the system is influenced to a great extent. Thus placing high demands on the performance of the motion controller.
At present, more and more advanced control algorithms are applied to the motion control research, and an iterative learning control algorithm, an adaptive robust control algorithm, a neural network control algorithm, an active disturbance rejection control algorithm and the like are common, wherein the active disturbance rejection control is regarded as a more effective technology.
In the prior art, the chinese invention patent "a fractional order active disturbance rejection motion control method based on an adjustable order filter" (publication No. CN108459507B, 25/05/2021) proposes a motion control method with flexible parameter adjustment and easy engineering implementation, and effectively improves the suppression capability of a motion control system on measurement noise and interference, but the model for the motion control method is relatively simple and does not consider the multi-modal characteristics.
The invention patent of china in the prior art "a design method for a multi-mode control system of an aircraft" (publication number CN104573182B, 12/08/2017) proposes a design method for a multi-mode control system of an aircraft, which can define and analyze the system in terms of functions, physics and software architecture, but does not give details about the design of a specific controller.
The paper "Adaptive tracking control for a class of systems of unknown switched nonlinear systems" (ZHao X, ZHEN X, Niu B, et al. Adaptive tracking control for a class of systems of unknown nonlinear systems [ J ]. Automatica,2015,2:185-191.) uses Adaptive backstepping technique to construct a state feedback controller and uses Lyapunov function to demonstrate its stability. The designed state feedback controller can ensure that all signals are bounded and the tracking error converges to a small neighborhood of the origin, but the controller design does not process system disturbance, and transient response of a controlled object can generate sudden change during mode switching, consume more energy and damage system execution mechanisms.
However, the active disturbance rejection motion control method in the related art does not consider the multi-modal characteristics of the system which are relatively complex and have practical engineering significance, and is difficult to suppress transient response sudden change of the execution mechanism, that is, the problems that the stability of the closed loop system is reduced and the control accuracy of the system is reduced due to sudden change of the control command during mode switching exist.
Disclosure of Invention
In view of this, the invention provides a multi-modal robust active disturbance rejection motion control method, which realizes control of a multi-modal system, improves control accuracy and stability of a controlled system, and enables the multi-modal controlled system to have stronger robustness.
The invention firstly provides a multi-modal robust active disturbance rejection motion control method, which comprises the following steps that are sequentially executed: judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In one possible embodiment, the method further comprises: establishing a multi-modal controlled system model according to the control target of the controlled system; and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
In one possible embodiment, the method further comprises: establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command includes: and correcting the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction.
In a possible implementation manner, establishing a multi-modal controlled system model according to the control target of the controlled system includes: establishing a controlled system model as follows:
Figure GDA0003542053880000031
wherein, i is 1,2, n, n represents that the controlled system model comprises n dynamic equations; 1, 2.. m, m denotes that the controlled system model includes m different modelsThe mode of (a);
Figure GDA0003542053880000037
represents state information xiThe first derivative of (a);
Figure GDA0003542053880000032
a state information vector composed of state information representing a controlled system;
Figure GDA0003542053880000033
Figure GDA0003542053880000034
representing the system state vector obtained by using a radial basis function neural network algorithm RBFNN according to the state information vector
Figure GDA0003542053880000035
A continuous function of (a); diRepresents state information xiCorresponding disturbance parameters; u represents a control input in the k-th modality, an actual instruction of the control method; x is the number of1And y represents the state output of the controlled system.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes: aiming at the controlled system model, designing a robust auto-disturbance-rejection motion controller corresponding to the kth mode; for the case of i being 1, the following disturbance observer model is established:
Figure GDA0003542053880000036
the following controller models were established:
Figure GDA0003542053880000041
wherein alpha is1Representing the control command, y, corresponding to the first dynamic equation in the model of the system to be controlledrIs a reference signal representing the control target of the controlled system, tracking errorThe difference is represented as e1＝x1-yr，
Figure GDA0003542053880000042
Is that the disturbance observer is directed at the state information x1The estimated parameters of the disturbance are used,
Figure GDA0003542053880000043
is an observation error, suppose
Figure GDA0003542053880000044
Figure GDA0003542053880000045
Is an estimate of the upper bound of the observation error,
Figure GDA0003542053880000046
is that
Figure GDA0003542053880000047
Is a hyperbolic tangent function; parameter k1＞0，k′1＞0，λ1＞0，τ1＞0，l1More than 0 are all adjustable parameters in the design process of the disturbance observer model, p1There is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes: when i is more than 1 and less than or equal to n-1, establishing the following disturbance observer model:
Figure GDA0003542053880000048
the following controller models were established:
Figure GDA0003542053880000049
wherein alpha isiRepresenting the corresponding of the ith dynamic equation in the controlled system modelWith a tracking error denoted by ei＝xi-αi-1，
Figure GDA00035420538800000410
Is that the disturbance observer is directed at the state information xiThe estimated parameters of the disturbance are used,
Figure GDA00035420538800000411
is an observation error, suppose
Figure GDA00035420538800000412
Figure GDA00035420538800000413
Is an estimate of the upper bound of the observation error,
Figure GDA00035420538800000414
is that
Figure GDA00035420538800000415
Is a hyperbolic tangent function; li＞0，ki＞0，k′i＞0，λi＞0，τiMore than 0 are parameters adjustable in the design process of the disturbance observer and controller model, piThere is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes: for the case of i ═ n, the following disturbance observer model is established:
Figure GDA0003542053880000051
the following controller models were established:
Figure GDA0003542053880000052
wherein alpha isnRepresenting a controlled systemThe tracking error of the ideal control instruction corresponding to the nth dynamic equation in the system model is expressed as en＝xn-αn，
Figure GDA0003542053880000053
Is that the disturbance observer is directed at the state information xnThe estimated parameters of the disturbance are used,
Figure GDA0003542053880000054
is an observation error, suppose
Figure GDA0003542053880000055
zu＝u-αnFor switching errors, representing the difference between the ideal control command and the actual control command, u is the actual control command,
Figure GDA0003542053880000056
is thetanIs estimated, parameter kn＞0，k′n＞0，λn＞0，τnMore than 0 are parameters adjustable in the design process of the disturbance observer and controller model, pnThere is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command includes: when the absolute value of the switching error is less than or equal to a first threshold value, the ideal control command alpha is setnAs an actual control instruction; when the absolute value of the switching error is larger than a first threshold value, the ideal control command alpha is controllednAnd correcting to obtain an actual control command.
In a possible embodiment, the ideal control command α is set when the absolute value of the switching error is equal to or less than a first threshold valuenAs an actual control command, when the absolute value of the switching error is larger than a first threshold, the ideal control command alpha is controllednAnd correcting to obtain an actual control command, wherein the method comprises the following steps:
Figure GDA0003542053880000057
wherein omega is more than 0 and less than 1, beta is more than 0 and less than 1, and c and beta are adjustable positive parameters.
The invention also provides a multi-modal robust active disturbance rejection motion control system, which is used for executing the control method and comprises the following steps: the control mode judging module is used for judging the control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; the ideal control instruction determining module is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and the instruction correction module is used for determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controlled system model establishing module is used for establishing a multi-modal controlled system model according to a control target of the controlled system; and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controller model and disturbance observer model building module is used for building a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
In a possible implementation manner, the instruction modification module is further configured to modify the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to a previous control modality, so as to obtain a modified actual control instruction.
The multi-mode robust active disturbance rejection motion control method can judge the current control mode of a controlled system according to the information of the controlled system, and determine an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method can correct an ideal control instruction to obtain an actual control instruction, inhibit transient response of the controlled system when control rules of different modes are converted, realize control of the multi-mode system, improve control precision and stability of the controlled system, and enable the multi-mode controlled system to have strong robustness.
Other features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the present invention.
Fig. 2 shows a flow chart of a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the invention.
Fig. 3 shows a flow chart of a multi-modal robust auto-disturbance-rejection motion control method according to another embodiment of the present invention.
Fig. 4 shows a schematic diagram of a robust active disturbance rejection controller controlling a controlled system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without any creative effort belong to the protection scope of the present invention.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
Fig. 1 is a schematic diagram illustrating a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the present invention.
As shown in fig. 1, the actuator of the controlled object may feed back the motion state of the actuator to the controlled object, and the sensor may acquire the state information of the controlled object. The robust active-disturbance-rejection controller can construct a nominal controller according to a control target and a related control theory, estimate unknown disturbance of a system by using a nonlinear disturbance observer, and compensate the nominal controller to obtain the robust active-disturbance-rejection controller capable of realizing active disturbance rejection. The multi-modal robust active disturbance rejection motion control method can comprise a multi-modal switching mechanism, and the mechanism can judge the current mode of a controlled object according to the state information of the controlled object acquired by a sensor. The multi-mode robust active disturbance rejection motion control method can also be used for an event trigger mechanism under a multi-mode condition, the mechanism can determine an ideal control instruction corresponding to the current state according to state information of a controlled object acquired by a sensor, and the robust active disturbance rejection controller can correct the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction, and transmits the corrected actual control instruction to an execution mechanism through a control network, so that the motion control of the controlled object is realized, the execution mechanism is ensured not to generate sudden change within a certain threshold value, and the transient response of a controlled system is restrained when different control modes are converted. Fig. 2 shows a flow chart of a multi-modal robust auto-disturbance-rejection motion control method according to an embodiment of the invention.
The method comprises the following steps which are executed in sequence:
step S1: judging the current control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system;
step S2: determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system;
step S3: and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
Determining an ideal control instruction corresponding to the control modality of the controlled system according to the control modality of the controlled system by judging the control modality of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method can correct an ideal control instruction to obtain an actual control instruction, inhibit transient response of the controlled system when different modal control rules are converted, realize control of the multi-modal system, improve control precision and stability of the controlled system, and enable the multi-modal controlled system to have strong robustness.
In another possible embodiment, as shown in fig. 3, before step S1, step S0 may be further included.
Step S0 includes: establishing a multi-modal controlled system model according to the control target of the controlled system; determining a control mode of the controlled system according to the controlled system model and the control target of the controlled system;
establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters.
In a possible implementation manner, establishing a multi-modal controlled system model according to the control target of the controlled system includes: establishing a controlled system model as follows:
Figure GDA0003542053880000091
wherein, i is 1,2, n, n represents that the controlled system model comprises n dynamic equations; k 1, 2.. m, m denotes that the controlled system model includes m different modalities;
Figure GDA0003542053880000096
represents state information xiThe first derivative of (a);
Figure GDA0003542053880000092
a state information vector composed of state information representing a controlled system;
Figure GDA0003542053880000093
Figure GDA0003542053880000094
representing the system state vector obtained by using a radial basis function neural network algorithm RBFNN according to the state information vector
Figure GDA0003542053880000095
A continuous function of (a); diRepresents state information xiCorresponding disturbance parameter(ii) a u represents a control input in the k-th modality, an actual instruction of the control method; x is the number of1And y represents the state output of the controlled system.
In one possible embodiment, the controlled system may be, for example, a drone system, a robotic arm system, or the like, with motion control. For example, aiming at an unmanned aerial vehicle controlled system, a plurality of control modes of the unmanned aerial vehicle system can be determined according to control targets such as vertical rising, horizontal uniform flight, horizontal acceleration flight, horizontal deceleration flight, vertical falling and the like of the unmanned aerial vehicle system: a vertical rising mode, a horizontal constant-speed flight mode, a horizontal acceleration flight mode, a horizontal deceleration flight mode and a vertical falling mode. A multi-modal controlled system model of the drone may be established from the plurality of control targets of the drone.
In a possible implementation manner, when the controlled system is an unmanned aerial vehicle system, sufficient flight state information is acquired through actual flight experiments
Figure GDA0003542053880000101
Obtaining a state information vector formed by state information of a controlled system, and obtaining enough airplane control input data (or called control commands) u. For example, the flight status information of the drone system includes velocity, acceleration, angular velocity, and the like. Obtaining a fitting Function of the state vector of the controlled system by using a Radial Basis Function Neural Network (RBFNN) algorithm according to the state information vector
Figure GDA0003542053880000102
And
Figure GDA0003542053880000103
and the expression is used for establishing a fitting function database under different modes and establishing a controlled system model according to the function. In a possible embodiment, the function of the state vector of the system to be controlled
Figure GDA0003542053880000104
And
Figure GDA0003542053880000105
and storing the model into a database to prepare for subsequent controller model design.
And acquiring a large amount of motion control data of the controlled system through experiments to obtain a state information vector and control input data which are formed by state information of the controlled system. And obtaining a function of the state vector of the controlled system by using a radial basis function neural network RBFNN algorithm according to the state information vector, thereby establishing a multi-mode including system model. Therefore, the technical problems that a controlled system model designed in the related technology is relatively simple, the complex system multi-mode characteristics with practical engineering significance are not considered, the stability of the controlled system is poor and the control precision is low when the modes are switched are solved by establishing the multi-mode controlled system.
The RBFNN algorithm used in the process of establishing the multi-modal control system model is a commonly used algorithm with better approximation performance in the field, and is widely applied to neural network models in the fields of mode identification, nonlinear function approximation and the like. The invention obtains the function of the state vector of the controlled system by utilizing the radial basis function neural network according to the state information vector
Figure GDA0003542053880000106
And
Figure GDA0003542053880000107
the specific implementation of (a) is not limited.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes:
aiming at the controlled system model, designing a robust auto-disturbance-rejection motion controller corresponding to the kth mode;
for the case of i being 1, the following disturbance observer model is established:
Figure GDA0003542053880000108
the following controller models were established:
Figure GDA0003542053880000111
wherein alpha is1Representing the control command, y, corresponding to the first dynamic equation in the model of the system to be controlledrIs a reference signal representing the control target of the controlled system, and the tracking error is represented by e1＝x1-yr，
Figure GDA0003542053880000112
Is that the disturbance observer is directed at the state information x1The estimated parameters of the disturbance are used,
Figure GDA0003542053880000113
is an observation error, suppose
Figure GDA0003542053880000114
Figure GDA0003542053880000115
Is an estimate of the upper bound of the observation error,
Figure GDA0003542053880000116
is that
Figure GDA0003542053880000117
Is a hyperbolic tangent function; parameter k1＞0，k′1＞0，λ1＞0，τ1＞0，l1More than 0 are parameters adjustable in the design process of the disturbance observer and controller model, p1There is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
Wherein, the function of the controlled system state vector in the disturbance observer model and the controller model
Figure GDA0003542053880000118
And
Figure GDA0003542053880000119
it can be directly obtained from the fitting function database, and the invention is not particularly limited in this regard.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes: when i is more than 1 and less than or equal to n-1, establishing the following disturbance observer model:
Figure GDA00035420538800001110
the following controller models were established:
Figure GDA00035420538800001111
wherein alpha isiRepresenting the control instruction corresponding to the ith dynamic equation in the controlled system model, and the tracking error is represented as ei＝xi-αi-1，
Figure GDA00035420538800001112
Is that the disturbance observer is directed at the state information xiThe estimated parameters of the disturbance are used,
Figure GDA00035420538800001113
is an observation error, suppose
Figure GDA00035420538800001114
Figure GDA00035420538800001115
Is an estimate of the upper bound of the observation error,
Figure GDA00035420538800001116
is that
Figure GDA00035420538800001117
Is a hyperbolic tangent function; li＞0，ki＞0，k′i＞0，λi＞0，τiMore than 0 are parameters adjustable in the design process of the disturbance observer and controller model, piThere is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
In one possible embodiment, the establishing a controller model and a disturbance observer model includes: for the case of i ═ n, the following disturbance observer model is established:
Figure GDA0003542053880000121
the following controller models were established:
Figure GDA0003542053880000122
wherein alpha isnExpressing an ideal control instruction corresponding to the nth dynamic equation in the controlled system model, and expressing a tracking error as en＝xn-αn，
Figure GDA0003542053880000123
Is that the disturbance observer is directed at the state information xnThe estimated parameters of the disturbance are used,
Figure GDA0003542053880000124
is an observation error, suppose
Figure GDA0003542053880000125
zu＝u-αnFor switching errors, representing the difference between the ideal control command and the actual control command, u is the actual control command,
Figure GDA0003542053880000126
is thetanIs estimated, parameter kn＞0，k′n＞0，λn＞0，τnMore than 0 are parameters adjustable in the design process of the disturbance observer and controller model, pnTo disturbIntermediate quantities in the design process of the dynamic observer model have no practical physical significance.
In this way, a controller model and a disturbance observer model are designed for the established multi-modal controlled system, and the technical problem that only the software system structure of the controller is defined in the related art but the specific controller design is not given in detail is solved.
In one possible implementation, the system state vector under different modes is established according to the controlled system model
Figure GDA0003542053880000127
Fitting function of
Figure GDA0003542053880000128
And
Figure GDA0003542053880000129
a database. A multi-mode switching mechanism is designed, when the system state information changes, in step S1, the current control mode of the controlled system is determined according to the controlled system information, that is, the controller of the current corresponding mode is started, and then the controller determines the ideal control instruction corresponding to the current control mode of the controlled system.
Therefore, different control modes correspond to different controllers, accuracy of control instructions output by the controllers is improved, and control precision is improved. In addition, when mode switching is considered in the design of the controller, transient response of a controlled system can generate sudden disturbance, and observation and estimation are carried out on the system disturbance, so that the technical problems that the system consumes more energy and can damage a system execution mechanism due to the disturbance are solved.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command may include: and correcting the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction.
In a possible embodiment, the determining an actual control command of the controlled system according to the controlled system information and the ideal control command may include: when the absolute value of the switching error is less than or equal to a first threshold value, the ideal control command alpha is setnAs an actual control instruction; when the absolute value of the switching error is larger than a first threshold value, the ideal control command alpha is controllednAnd correcting to obtain an actual control command.
In a possible embodiment, the ideal control command α is set when the absolute value of the switching error is equal to or less than a first threshold valuenAs an actual control command, when the absolute value of the switching error is larger than a first threshold, the ideal control command alpha is controllednAnd correcting to obtain an actual control command, wherein the method comprises the following steps:
Figure GDA0003542053880000131
wherein omega is more than 0 and less than 1, beta is more than 0 and less than 1, and c and beta are adjustable positive parameters.
Thus, when the absolute value of the switching error is equal to or less than the first threshold, the ideal control command α is setnAs an actual control instruction; when the absolute value of the switching error is larger than a first threshold value, the ideal control command alpha is controllednAnd correcting to obtain an actual control command. And suppressing the transient response of the controlled system when different control modes are switched. When the mode of the controlled system is switched, the ideal control instruction which is to be input into the system by the controller is corrected, specifically, an event trigger mechanism is set according to a given first threshold condition, and the actuator is ensured not to generate sudden change within a certain threshold.
In one possible embodiment, the first threshold is set to | zuAnd | is less than or equal to β | u | + c, wherein u is a control instruction corresponding to the previous control mode, and c and β are positive parameters which can be adjusted in an experiment and adapt to a corresponding control system. The present invention is not particularly limited to c and beta。(1-ω)u+ωαnU in the equation is a control instruction corresponding to a previous control modality, and the equation represents that an actual control instruction corresponding to the previous control modality and an ideal control instruction corresponding to a current control modality are weighted to obtain an actual control instruction corresponding to the current control modality, and the actual control instruction is a modified control instruction.
In a possible implementation manner, an event trigger mechanism is arranged in the controller, and when the event trigger mechanism in the controller monitors that an ideal control instruction corresponding to a current control mode and an actual control instruction before switching of the control mode are too large in difference, an instruction correction mechanism is started, that is, the ideal control instruction is corrected to obtain an actual control instruction which is actually input to the controlled system, so that the fact that an actuator does not mutate within a certain threshold value is ensured, that is, transient response of the controlled system when switching of different control modes is suppressed.
Fig. 4 shows a schematic diagram of a robust active disturbance rejection controller controlling a controlled system according to an embodiment of the present invention. In one possible implementation, a multi-modal controlled system model is established according to the control targets of the unmanned aerial vehicle system. For example, the unmanned aerial vehicle system comprises control targets such as vertical ground ascending, horizontal uniform flight, horizontal acceleration flight, horizontal deceleration flight, vertical ground descending and the like, and a multi-mode control model of the unmanned aerial vehicle system is established according to the control targets. And determining a plurality of control modes of the controlled system according to the unmanned aerial vehicle system model and the control target of the unmanned aerial vehicle system, such as a horizontal uniform speed control mode, an accelerating ascending control mode, a decelerating descending control mode and other control modes of the unmanned aerial vehicle system.
As shown in fig. 4, the controlled system includes a sensor and an actuator, and the sensor is used for sensing state information of the controlled system. For example, when the controlled system is an unmanned aerial vehicle system, the sensor may sense information such as height, speed, acceleration, angular velocity, and the like of the unmanned aerial vehicle system; the actuator may be an electric motor for driving the movement of the drone. The unmanned aerial vehicle system comprises a robust active disturbance rejection controller and a controlled system. The robust active-disturbance-rejection controller in fig. 4 may obtain state information of the controlled system sensed by a sensor in the controlled system, and determine a control mode in which the controlled system is currently located according to the state information, for example, the robust active-disturbance-rejection controller determines that the unmanned aerial vehicle system is in a control mode that is vertical to the ground and accelerates and rises, and determines an ideal control instruction corresponding to the control mode that is currently located and accelerates and rises; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In a possible implementation manner, the robust active disturbance rejection controller in fig. 4 may modify the ideal control command according to the ideal control command and an actual control command corresponding to a previous control modality, so as to obtain a modified actual control command. For example, when an event trigger mechanism in the robust active disturbance rejection controller detects that an ideal control instruction corresponding to a current control mode (an accelerated ascent control mode) and an actual control instruction corresponding to a control mode before switching of the control mode (for example, uniform horizontal flight) are too large in difference (namely, greater than a certain threshold), an instruction correction mechanism is started, that is, the ideal control instruction is subjected to finger-type correction to obtain an actual control instruction which is actually input to a controlled system, so that it is ensured that an execution mechanism does not generate sudden change within a certain threshold, that is, transient response of the controlled system when switching of different control modes is suppressed.
The invention also provides a multi-modal robust active disturbance rejection motion control system, which is used for executing the control method and comprises the following steps: the control mode judging module is used for judging the control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; the ideal control instruction determining module is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; and the instruction correction module is used for determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controlled system model establishing module is used for establishing a multi-modal controlled system model according to a control target of the controlled system; and determining the control mode of the controlled system according to the controlled system model and the control target of the controlled system.
In one possible embodiment, the multi-modal robust auto-disturbance-rejection motion control system further includes: the controller model establishing module is used for establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; and the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters. In a possible implementation manner, the instruction modification module is further configured to modify the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to a previous control modality, so as to obtain a modified actual control instruction.
According to the multi-mode robust active disturbance rejection motion control method, the control mode of the controlled system can be judged according to the information of the controlled system, and an ideal control instruction corresponding to the control mode of the controlled system is determined according to the control mode of the controlled system; and determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction. The control method can realize the control of the multi-mode system, inhibit the transient response of the controlled system when the control rules of different modes are converted, improve the control precision and the stability of the controlled system, and enable the multi-mode controlled system to have stronger robustness.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (7)

1. A multi-modal robust active disturbance rejection motion control method is characterized by comprising the following steps which are sequentially executed:
establishing a multi-modal controlled system model according to a control target of a controlled system;
determining the current control mode of the controlled system according to the controlled system model and the control target of the controlled system;
establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models, and the disturbance observer models are used for estimating disturbance parameters of the controlled system;
determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameter by using the controller model;
determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction;
wherein, according to the control target of the controlled system, establishing a multi-modal controlled system model, comprising:
establishing a controlled system model as follows:
Figure 910721DEST_PATH_IMAGE001
wherein,
Figure 341702DEST_PATH_IMAGE002
representing the controlled system model includes
Figure 314337DEST_PATH_IMAGE003
A dynamic equation;
Figure 881585DEST_PATH_IMAGE004
represents the aboveThe controlled system model comprises
Figure 184038DEST_PATH_IMAGE005
A number of different modalities;
Figure 102316DEST_PATH_IMAGE006
indicating status information
Figure 878642DEST_PATH_IMAGE007
The first derivative of (a);
Figure 565975DEST_PATH_IMAGE008
a state information vector composed of state information representing a controlled system;
Figure 97451DEST_PATH_IMAGE009
representing the system state vector obtained by using a radial basis function neural network algorithm RBFNN according to the state information vector
Figure 362079DEST_PATH_IMAGE010
A continuous function of (a);
Figure 801150DEST_PATH_IMAGE011
indicating status information
Figure 218356DEST_PATH_IMAGE012
Corresponding disturbance parameters;
Figure 983050DEST_PATH_IMAGE013
is shown as
Figure 734974DEST_PATH_IMAGE014
Control input in an individual modality, the control methodActual instructions of the method;
Figure 180999DEST_PATH_IMAGE015
and
Figure 842925DEST_PATH_IMAGE016
representing a state output of the controlled system;
wherein, the determining the actual control instruction of the controlled system according to the controlled system information and the ideal control instruction comprises:
and correcting the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to the previous control mode to obtain a corrected actual control instruction.
2. The method of claim 1, wherein the establishing a controller model and a disturbance observer model comprises:
aiming at the controlled system model, designing
Figure 653886DEST_PATH_IMAGE017
A robust auto-disturbance-rejection motion controller corresponding to each mode;
for the
Figure 502893DEST_PATH_IMAGE018
When the temperature of the water is higher than the set temperature,
the following disturbance observer model is established:
Figure 752609DEST_PATH_IMAGE019
the following controller models were established:
Figure 737882DEST_PATH_IMAGE020
wherein,
Figure 500171DEST_PATH_IMAGE021
representing the control command corresponding to the first dynamic equation in the controlled system model,
Figure 305316DEST_PATH_IMAGE022
is a reference signal and represents a control target of a controlled system, and a tracking error is represented by
Figure 358722DEST_PATH_IMAGE023
Is that the disturbance observer aims at the state information
Figure 136186DEST_PATH_IMAGE024
The estimated parameters of the disturbance are used,
Figure 616845DEST_PATH_IMAGE025
is an observation error, suppose
Figure 99167DEST_PATH_IMAGE026
Is an estimate of the upper bound of the observation error,
Figure 690685DEST_PATH_IMAGE027
is the first derivative of (a) is,
Figure 650551DEST_PATH_IMAGE028
is a hyperbolic tangent function; parameter(s)
Figure 161167DEST_PATH_IMAGE029
Figure 472062DEST_PATH_IMAGE030
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
Figure 601692DEST_PATH_IMAGE031
for disturbance observer modelIntermediate quantities in the counting process have no practical physical significance.
3. The method of claim 2, wherein the establishing a controller model and a disturbance observer model comprises:
for the
Figure 822589DEST_PATH_IMAGE032
Then, the following disturbance observer model is established:
Figure 176210DEST_PATH_IMAGE033
the following controller models were established:
Figure 364615DEST_PATH_IMAGE034
wherein,
Figure 94674DEST_PATH_IMAGE035
representing the model of the controlled system
Figure 497973DEST_PATH_IMAGE036
The tracking error is expressed as
Figure 897862DEST_PATH_IMAGE037
That the disturbance observer is directed to the state information
Figure 183349DEST_PATH_IMAGE038
The estimated parameters of the disturbance are used,
Figure 107312DEST_PATH_IMAGE039
is an observation error, suppose
Figure 161856DEST_PATH_IMAGE026
Is an estimate of the upper bound of the observation error,
Figure 732645DEST_PATH_IMAGE027
the first derivative of (a) is,
Figure 505429DEST_PATH_IMAGE040
is a hyperbolic tangent function
Figure 780553DEST_PATH_IMAGE041
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
Figure 608045DEST_PATH_IMAGE042
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
4. The method of claim 3, wherein the establishing a controller model and a disturbance observer model comprises:
for the
Figure 943211DEST_PATH_IMAGE043
Then, the following disturbance observer model is established:
Figure 813078DEST_PATH_IMAGE044
the following controller models were established:
Figure 688630DEST_PATH_IMAGE045
wherein,
Figure 576821DEST_PATH_IMAGE046
representing the model of the controlled system
Figure 879626DEST_PATH_IMAGE047
The tracking error of the ideal control command corresponding to the dynamic equation is expressed as
Figure 236789DEST_PATH_IMAGE048
Is that the disturbance observer aims at the state information
Figure 650453DEST_PATH_IMAGE049
The estimated parameters of the disturbance are used,
Figure 658729DEST_PATH_IMAGE050
is an observation error, suppose
Figure 132436DEST_PATH_IMAGE051
For switching errors, representing the difference between the ideal control command and the actual control command,
Figure 242474DEST_PATH_IMAGE052
in order to actually control the command, it is,
Figure 459829DEST_PATH_IMAGE053
estimated value of, parameter of
Figure 322612DEST_PATH_IMAGE054
Figure 170482DEST_PATH_IMAGE055
Are all adjustable parameters in the design process of a disturbance observer and a controller model,
Figure 626871DEST_PATH_IMAGE056
there is no practical physical significance for the intermediate quantities in the disturbance observer model design process.
5. The method of claim 4, wherein determining the actual control commands of the controlled system according to the information of the controlled system and the ideal control commands comprises:
when the absolute value of the switching error is less than or equal to a first threshold value, taking the ideal control command as an actual control command;
and when the absolute value of the switching error is larger than a first threshold value, correcting the ideal control command to obtain an actual control command.
6. The method according to claim 5, wherein the ideal control command is used as the actual control command when the absolute value of the switching error is equal to or less than a first threshold, and the ideal control command is used when the absolute value of the switching error is greater than the first threshold
Figure 257704DEST_PATH_IMAGE057
And correcting to obtain an actual control command, wherein the method comprises the following steps:
Figure DEST_PATH_IMAGE059A
wherein,
Figure 305819DEST_PATH_IMAGE060
is an adjustable positive parameter.
7. A multi-modal robust auto-disturbance-rejection motion control system, wherein the system is configured to perform the method of any of claims 1-6, the system comprising: the control mode judging module is used for judging the control mode of the controlled system according to the information of the controlled system; wherein the controlled system information comprises state information of a controlled system; the ideal control instruction determining module is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the current control mode of the controlled system; the instruction correction module is used for determining an actual control instruction of the controlled system according to the controlled system information and the ideal control instruction;
the multi-modal robust auto-disturbance rejection motion control system further comprises: the controlled system model establishing module is used for establishing a multi-modal controlled system model according to a control target of the controlled system; determining a control mode of the controlled system according to the controlled system model and the control target of the controlled system; wherein, according to the control target of the controlled system, establishing a multi-modal controlled system model, comprises:
establishing a controlled system model as follows:
Figure 731115DEST_PATH_IMAGE001
wherein,
Figure 940379DEST_PATH_IMAGE061
representing the controlled system model includes
Figure 624171DEST_PATH_IMAGE003
A dynamic equation;
Figure 336912DEST_PATH_IMAGE062
representing the controlled system model includes
Figure 526585DEST_PATH_IMAGE005
A number of different modalities;
Figure 567353DEST_PATH_IMAGE006
indicating status information
Figure 664622DEST_PATH_IMAGE007
The first derivative of (a);
Figure 622082DEST_PATH_IMAGE063
a state information vector composed of state information representing a controlled system;
Figure 779394DEST_PATH_IMAGE009
representing the system state vector obtained by using a radial basis function neural network algorithm RBFNN according to the state information vector
Figure 573038DEST_PATH_IMAGE010
A continuous function of (a);
Figure 473998DEST_PATH_IMAGE011
indicating status information
Figure 285965DEST_PATH_IMAGE012
Corresponding disturbance parameters;
Figure 614178DEST_PATH_IMAGE013
is shown as
Figure 957435DEST_PATH_IMAGE014
Control input in each mode, and actual instructions of the control method;
Figure 271873DEST_PATH_IMAGE015
and
Figure 79292DEST_PATH_IMAGE016
representing a state output of the controlled system;
the multi-modal robust auto-disturbance rejection motion control system further comprises: the controller model establishing module is used for establishing a controller model and a disturbance observer model; the control modes correspond to different control models and disturbance observer models; wherein, the disturbance observer model is used for estimating the disturbance parameters of the controlled system; the controller model is used for determining an ideal control instruction corresponding to the current control mode of the controlled system according to the controlled system model and the disturbance parameters;
the instruction correction module is further configured to correct the ideal control instruction according to the ideal control instruction and an actual control instruction corresponding to a previous control modality, so as to obtain a corrected actual control instruction.
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