CN106444359A - Human-simulated intelligent control method for autonomous region keeping of water-jet propulsion unmanned ship - Google Patents
Human-simulated intelligent control method for autonomous region keeping of water-jet propulsion unmanned ship Download PDFInfo
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Abstract
The invention provides a human-simulated intelligent control method for autonomous region keeping of a water-jet propulsion unmanned ship, which is characterized in that analysis and calculation for a key parameter are performed on parameters measured by a position and attitude sensor used for controlling the boundary, a region keeping judgment system judges whether region keeping is required to be performed or not according to a calculated value and an actual value of the key parameter, a deviation equation of an under-actuated water-jet propulsion unmanned ship motion equation and a region keeping stop position trajectory equation is solved if region keeping is required to be performed, data information is transferred to a human-simulated intelligent controller, and the human-simulated intelligent controller determines a region keeping control strategy according to a regulated variable, the deviation and the variation trend of the deviation and implementing the region keeping control strategy through an execution mechanism. The human-simulated intelligent control method is applicable to the fields of region marine search and rescue and exploitation technologies and the like, and can ensure the unmanned ship not to be away from the specified region no matter how the unmanned ship moves.
Description
Technical Field
The invention relates to an unmanned ship control method, in particular to a humanoid intelligent control method for autonomous region maintenance of an unmanned ship.
Background
The water jet propulsion surface unmanned vehicle (USV) is an offshore intelligent motion platform which can safely and independently sail in actual marine environment and complete various tasks, can carry sensors and communication devices, has the characteristics of good invisibility, flexible operation, automatic driving and the like by combining the advantages of the water jet propulsion, is suitable for executing tasks such as detection research, shallow water surveying and mapping, marine search and rescue, oceanographic research and the like in various severe environments and has no danger of casualties. The unmanned ship system is an under-actuated, large-disturbance, fast-changing, multi-input and multi-output nonlinear coupling system. For an under-actuated system, due to the lack of transverse driving force, the fixed-point positioning function cannot be realized, and the dynamic positioning function is difficult to realize, but the actual task requirements of the unmanned boat need to relate to an area maintaining function, such as area marine search and rescue and survey, area sampling and monitoring, port patrol and the like of the unmanned boat. Therefore, the research on the unmanned boat autonomous area maintaining control technology is significant. The autonomous region maintaining method is realized on the basis of dynamic positioning, the main application object is a large ship with a plurality of propelling devices, and the autonomous region maintaining method is not used for a small unmanned ship with a single propelling device. Therefore, no report about the maintenance control method of the autonomous region of the unmanned ship is found in published documents at home and abroad.
Disclosure of Invention
The invention aims to provide a human-simulated intelligent control method for maintaining an autonomous region of a water jet propulsion unmanned ship, which can ensure that the unmanned ship cannot drive away from a specified region no matter how the unmanned ship moves.
The purpose of the invention is realized as follows: the method comprises the steps of analyzing and calculating key parameters of parameters measured by a pose sensor of a control boundary, transmitting data information to a region holding judgment system, comparing and judging whether region holding is performed according to calculated values and actual values of the key parameters, solving a deviation equation of an under-actuated water jet propulsion unmanned ship motion equation and a region holding stop position trajectory equation if the region holding is required, transmitting the data information to a humanoid intelligent controller, determining a region holding control strategy according to the regulated amount, the deviation and the variation trend of the deviation by the humanoid intelligent controller, and implementing the region holding control strategy through an execution mechanism.
The present invention may further comprise:
1. the analysis and calculation of the key parameters specifically comprises:
(1) whether the track is in the area maintained by the area, namely the maximum value r of the distance from the center O of the area to the track of the unmanned boatmaxThe calculation formula is as follows:
wherein (X)O,YO) Is the coordinate of the area center O, (X, Y) is the unmanned boat motion track coordinate,
(2) the limit movement distance k is the limit range r 'of the unmanned ship for control measures'maxThe calculation formula is as follows:
wherein R is the area radius and the direction angle of the interference force action,
(3) propulsion device reset angle thetaFThat is, when the heading angle ψ is equal to the return angle, the propulsion device is reset, and the calculation formula is:
r' is the radius of the inner circle,
(4) a propulsion device stop position, wherein O is set as a stop position when the sea state is unknown, O' is set as a stop position when the sea state is known and is at or below the sea state of four levels,
wherein O' is the intersection point of the interference force passing through the center of the circle and the inner circle.
2. The solving of the deviation equation of the motion equation of the under-actuated water jet propulsion unmanned ship and the area keeping stop position trajectory equation specifically comprises the following steps:
the motion equation of the 3 degrees of freedom of the unmanned ship horizontal plane is as follows:
wherein η ═ x, y, ψ]T、υT＝[u,v,r]、τ＝[τ1,0,τ3]TRespectively position, velocity and propulsion, tau1And τ3Respectively the longitudinal propulsion and the bow-turning moment m of the unmanned surface vehicle11,m22,m33For system inertia matrix parameters including additional mass, du,dv,drThe interference of the environment and the hydrodynamic coefficient of the rest;
reference track (x) of area holding stop positiond,yd,ψd,ud,vd,rd) And a reference control input (tau)1d,τ3d) Satisfy the requirement of
Carrying out differential homoembryo transformation on the motion equation of 3 degrees of freedom of the unmanned surface vehicle horizontal plane so as to ensure that
z1＝xcosψ-ysinψ
z3＝ψ
z5＝v
z6＝r
And control input transformation
The new state equation after the arrangement is
Differential homomorphic transformation equation for solving reference locus of area keeping control boundary in same wayDefining a deviation
e＝[e1,e2,e3,e4,e5,e6]T＝[z1-z1d,z2-z2d,z3-z3d,z4-z4d,z5-z5d,z6-z6d]T
Then
Elimination of z from the formula1,z2,z3,z4,z5,z6，
z4z6-z4dz6d
＝z4z6-z4z6d+z4z6d-z4dz6d
＝z4e6-z4de6+z4de6+z6de4
＝e4e6+z4de6+z6de4
Equation of deviation
Wherein,
3. the judging whether the area keeping is needed specifically comprises: when r is less than or equal to rmaxIn time, no start-up zone maintenance is required; when r ismax＜r≤r′maxWhen zero, starting the area to keep; when r is > r'maxWhen this happens, the region remains failed.
4. The determining the region maintaining control strategy specifically includes:
the human-simulated intelligent integration algorithm is as follows:
wherein u is a control amount, e is a deviation,is a derivative of the deviation, KpTo proportional gain, KiTo integrate the gain, KdIs the differential gain;
(1) operation control stage
The ideal error target trajectory isThe characteristic basic element set of the unmanned ship operation control level is as follows: q1＝{q1,q2,q3,q4,q5,q6,q7}，
Wherein:
the model of the unmanned ship operation control level is phi1＝{φ11,φ12,φ13,φ14,φ15,φ16,φ17,φ18,φ19,φ110}
Wherein:
control mode level psi of unmanned ship operation control level1＝{ψ11，ψ12，ψ13，ψ14，ψ15，ψ16}
Wherein:
in the formula, symbol un,un-1The nth, n-1 time output of the controller; u shapemaxA maximum value of the controller output; e.g. of the typen,Controlling the deviation and the deviation change rate of the system for the nth time; e.g. of the typemiControlling the ith extreme value of the system deviation; kp,Kd,Ki,SpProportional coefficient, differential coefficient, integral coefficient, sign of proportional coefficient, gain suppression factor of k controller;
inference rule set omega of unmanned ship operation control level1＝{ω11,ω12,ω13,ω14,ω15,ω16}
Wherein:
(2) parameter correction stage
Feature element set Q of unmanned ship parameter correction stage2＝{q1,q2,q3,q4,q5,q6,q7,q8,q9}
Wherein:
characteristic model phi of parameter correction stage of unmanned ship2＝{Φ21，Φ22}，
Wherein phi21＝{φ212，φ2141，φ215，φ2171，φ2172，φ218The feature model of 2 and 4 quadrants,
Φ22＝{φ22101，φ22102，φ22103，φ22104the method is a characteristic model of quadrants 1 and 3, wherein:
decision modality level Ψ of unmanned surface vehicle parameter correction level2＝{Ψ21，Ψ22}, wherein: Ψ21＝{ψ21i},i＝2,41,5,71,72,8Ψ22＝{ψ22i101,102,103,104, wherein:
ψ212＝{Kd＝Kd+kd1} ψ2141＝{Kp＝Kp+kp1}
ψ215＝{Kp＝Kp-kp1} ψ2171＝{Kd＝Kd+kd1,Sp＝-1}
ψ2172＝{Kd＝Kd+kd1,Kp＝Kp-kp1} ψ218＝{Kd＝Kd+kd1}
ψ22101＝{Kd＝Kd+kd2,Kp＝Kp-kp2} ψ22102＝{Kp＝Kp+kp2}
ψ22103＝{Kd＝Kd+kd2,Kp＝Kp+kp2} ψ22104＝{Kd＝Kd+kd2,Kp＝Kp-kp2}
the symbols in the formula have the following meanings: k is a radical ofp1,kd1,kp2,kd2: 2. a proportional or differential increase or decrease coefficient of 4 quadrants, an l, 3 quadrant or differential increase or decrease coefficient,
inference rule set omega of unmanned ship parameter correction level2＝{Ω21,Ω22}, wherein: omega21＝{ω21i},i＝2,41,5,71,72,8，Ω22＝{ω22i},i＝101,102,103,104，
In the formula:
(3) task adaptation level
Characteristic model phi of task adaptation level of unmanned ship3＝{φ31,φ32}, wherein: phi is a31For the input of a normalized control feature model, phi32Outputting an inverse normalized control feature model;
unmanned ship task adaptation level control mode set psi3＝{ψ31,ψ32}, wherein:
ψ32＝{un＝Umax·un,0,if|un,0|＞1thenun,0＝sgn(un,0) The symbols in the formula have the following meanings:
en,0,inputs for deviation and deviation rate of change normalization; e.g. of the typen,The deviation and the rate of change of deviation of the control system; dmax,Vmax: maximum traction distance of controlled quantity, maximum traction speed of controlled quantity; u. ofn,un,0,Umax: the output of the actual controller, the normalized controller output, the maximum input of the control actuator;
inference rule set omega of task adaptation level of unmanned ship3＝{ω31,ω32}, wherein:
the invention provides a humanoid intelligent control method for autonomous region maintenance of a USV (Universal Serial bus), which mainly judges whether region maintenance is carried out or not through calculation data of key parameters of the region maintenance, solves a deviation equation of an unmanned ship motion equation and a region maintenance stop position trajectory equation if the region maintenance is required, transmits data information to a humanoid intelligent controller, and further controls a propelling device of the unmanned ship by adopting a humanoid intelligent control algorithm, so that the unmanned ship cannot drive away from a specified region no matter how the unmanned ship moves.
The size of the area is determined by the task requirements, and a range (called a control boundary) is defined in the area, in the range, the propeller does not output power, the heading is not maintained, and the propeller only works when the control boundary is reached or approached. Therefore, the main factors influencing the maintenance effect of the autonomous region of the unmanned ship are as follows: the unmanned boat track position information x, y and psi are respectively a longitudinal coordinate, a transverse coordinate and a heading angle; maximum value r of key parameters such as distance from area center O to unmanned boat trackmaxDistance of ultimate movement k, ultimate range r'maxAngle of repose thetaFThe pusher stop position, etc.
The invention has the following beneficial effects:
1. the invention can give consideration to stability, rapidity and accuracy, on-line feature identification and feature memory can correspondingly adopt different control modes according to the current feature interval of the system at any time, and the multimode control not only considers the stability, but also gives consideration to the requirements of rapidity and accuracy;
2. the invention has stronger robustness, the design of the humanoid intelligent controller is insensitive to the change of object characteristics to a certain extent, the change of the model parameters of a controlled system can be better adapted, and the external interference such as wind, wave and flow is reduced to the maximum extent by selectively integrating the error;
3. the invention can deal with the difficult controlled objects, such as strong nonlinearity and uncertain systems which are difficult to be described by fixed and approximate mathematical models, and the more difficult the controlled objects are, the better the superiority of the human-simulated intelligent control can be embodied.
Drawings
FIG. 1 is a block diagram of a USV zone maintenance architecture;
FIG. 2 is a schematic diagram of USV region holding key parameters;
FIG. 3 is a flow chart of the USV region holding algorithm;
FIG. 4 is a structure of a humanoid intelligent control second-order production system;
FIG. 5 is a human-simulated intelligent control system architecture;
FIG. 6 is a characteristic model of the operational control stage;
fig. 7 is a feature model of a parametric correction stage.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
The invention discloses a humanoid intelligent control method for maintaining an autonomous region of a USV (universal serial bus) mainly comprising the following steps:
step one, analyzing and calculating key parameters, comprising the following steps: the system comprises a propelling device starting position, a limit movement distance, a propelling device resetting angle and a propelling device stopping position, and transmits data information to an area maintenance judgment system;
under the condition that sea conditions are known, solving the motion state of the unmanned ship according to the acting force, so as to solve the region holding parameters; under the condition that sea conditions are unknown, the force and the moment are reversely solved by using a parameter estimation method through the existing position information in a period of time, and finally the motion state of the unmanned ship is solved according to the acting force, so that the region maintenance parameters are solved;
step two, the area keeping judging system judges whether area keeping is needed or not according to comparison between the calculated value and the actual value of the key parameter;
step three, if the region is required to be maintained, solving a deviation equation of an unmanned ship motion equation and a region maintaining stop position trajectory equation, and transmitting data information to the humanoid intelligent controller;
and step four, determining an area maintenance control strategy according to the adjusted quantity, the deviation and the variation trend of the deviation by the controller based on the humanoid intelligent algorithm and effectively implementing the strategy by an actuating mechanism.
The area of step one keeps the analysis and calculation of key parameters, as shown in fig. 2:
whether the track is in the area maintained by the area, namely the maximum value r of the distance from the center O of the area to the track of the unmanned boatmax. The calculation formula is as follows:
wherein (X)O,YO) The coordinates of the area center O are shown, and the (X, Y) coordinates are the unmanned boat motion track coordinates.
The limit movement distance k is the limit range r 'of the unmanned ship for control measures'max. The calculation formula is as follows:
wherein, R is the area radius and is the direction angle of the action of the interference force.
Propulsion device reset angle thetaFI.e. the propulsion device is reset when the heading angle psi equals the reset angle. The calculation formula is as follows:
the propulsion device stop position is O if the sea state is unknown, and O' if the sea state is known and is at or below the fourth-order sea state. Monitoring the position of the unmanned surface vehicle and calculating the angle psi between the unmanned surface vehicle and the center of the defined area, when psi is greater than the angle psi between the predetermined stop position and the center of the areatWhen the unmanned boat stops moving, the unmanned boat can stop moving.
Wherein R 'is the radius of the inner circle, and O' is the intersection point of the interference force which passes through the center of the circle and the inner circle.
In the third step, solving a deviation equation of an unmanned ship motion equation and an area keeping stop position track equation, wherein firstly, the motion equation of 3 degrees of freedom of the unmanned ship horizontal plane is as follows:
wherein η ═ x, y, ψ]T，υT＝[u,v,r]，τ＝[τ1,0,τ3]TPosition, velocity and propulsion, τ, respectively1And τ3Respectively the longitudinal propulsion and the bow-turning moment m of the unmanned surface vehicle11,m22,m33For system inertia matrix parameters including additional mass, du,dv,drThe rest is hydrodynamic coefficient for environmental interference.
Starting a reference track (x) of a region keeping control boundary on the premise of keeping key parameters in a region in the step oned,yd,ψd,ud,vd,rd) And a reference control input (tau)1d,τ3d) Satisfy the requirement of
Carrying out differential homoembryo transformation on the motion equation of 3 degrees of freedom of the unmanned surface vehicle horizontal plane so as to ensure that
z1＝xcosψ-ysinψ
z3＝ψ
z5＝v
z6＝r
And control input transformation
The new state equation after the arrangement is
In the same way, the differential homomorphic transformation equation of the reference track of the area keeping control boundary can be obtainedDefining a deviation
e＝[e1,e2,e3,e4,e5,e6]T＝[z1-z1d,z2-z2d,z3-z3d,z4-z4d,z5-z5d,z6-z6d]T
Then
By the formula1,z2,z3,z4,z5,z6，
z4z6-z4dz6d
＝z4z6-z4z6d+z4z6d-z4dz6d
＝z4e6-z4de6+z4de6+z6de4
＝e4e6+z4de6+z6de4
Equation of deviation
Wherein,
in the second step, the region keeping judging system judges whether the unmanned ship carries out region keeping control or not according to comparison between the calculated value and the actual value of the key parameter, and when r is not more than rmaxIn time, no start-up zone maintenance is required; when r ismax＜r≤r′maxWhen zero, starting the area to keep; when r is > r'maxWhen this happens, the region remains failed. (ii) a
In the fourth step, the process of making the region holding strategy by the humanoid intelligent controller according to the deviation of the unmanned ship motion equation and the region holding control boundary trajectory equation is as follows: as shown in fig. 4 and 5, wherein fig. 4 is a structure of a humanoid intelligent control second-order production type system, fig. 5 is a structure of a humanoid intelligent control system,
the human-simulated intelligent integration algorithm is as follows:
wherein u is a control amount, e is a deviation,is a derivative of the deviation, KpTo proportional gain, KiTo integrate the gain, KdIs the differential gain.
1. Operation control stage
The human-simulated intelligent control is to monitor the deviation of the controlled quantity and the change rate of the deviation and judge the dynamic process of the system, and different control algorithms are adopted in different dynamic processes. The error phase plane can be divided according to the deviation of the controlled quantity and the size of the change rate of the deviationSo as to facilitate the visual analysis of the dynamic control process.
In conjunction with fig. 6, the locus indicated by the dotted line with an arrow is the ideal error target locusThe characteristic primitive set of the unmanned ship operation control level is the threshold value of deviation and deviation change rate: q1＝{q1,q2,q3,q4,q5,q6,q7}
Wherein:human-simulated intelligent control strategy analysis:
firstly, in the initial stage of control, when the deviation is large, corresponding to the area 1, the control function is adopted as large as possible, for example, pound-pound mode control is adopted, so that the deviation change speed is excited, and the convergence speed of a control system is improved;
secondly, in the process of reducing the deviation, if the deviation change speed is less than the preset speed, corresponding to the areas 4 and 5, adopting proportional mode control to improve the deviation change speed so as to reduce the deviation quickly;
in the process of reducing the deviation, if the deviation change speed is greater than the preset speed, corresponding to the areas 2, 7 and 8, on the basis of the proportional mode, introducing a differential mode to form a proportional plus differential control mode for reducing the deviation change speed and avoiding overshoot;
when both the deviation and the deviation change rate meet the requirements (the error change speed is in a preset speed range), corresponding to the areas 3 and 6, adopting hold mode control, and observing and holding in an open loop to make a user feel static;
in the process of increasing the deviation (overshoot occurs), corresponding to the area 10, in order to inhibit the increase of the deviation, a control mode of proportional plus derivative and integral is adopted, so that the deviation returns as soon as possible;
when the deviation and the deviation change rate are both small (within the given steady-state error requirement range), corresponding to the area 9, the mode control is kept, so that the self-attenuation of the area can reach balance; for a system with external environment interference, extreme value sampling holding mode control can be adopted to reduce steady-state errors or improve the anti-interference capability.
Model phi of unmanned ship operation control level1＝{φ11,φ12,φ13,φ14,φ15,φ16,φ17,φ18,φ19,φ110}
Wherein:
control mode level psi of unmanned ship motion control level1＝{ψ11，ψ12，ψ13，ψ14，ψ15，ψ16}
Wherein:
in the formula, symbol un,un-1The nth, n-1 time output of the controller; u shapemaxA maximum value of the controller output; e.g. of the typen,Controlling the deviation and the deviation change rate of the system for the nth time; e.g. of the typemiControlling the ith extreme value of the system deviation; kp,Kd,Ki,SpAnd the proportional coefficient, the differential coefficient, the integral coefficient, the sign of the proportional coefficient and the gain suppression factor of the k controller.
Inference rule set omega of unmanned ship motion control level1＝{ω11,ω12,ω13,ω14,ω15,ω16}
Wherein:
2. parameter correction stage
After the initial model of the humanoid intelligent controller is determined, the threshold value and the parameters are adjusted, and the ideal phase track of the specific object is found and realized. With reference to fig. 7, feature cell set Q of the unmanned ship parameter correction stage2＝{q1,q2,q3,q4,q5,q6,q7,q8,q9}
Wherein:
according to the thought of humanoid intelligent control: when the deviation change rate is larger, the differential action is increased to weaken the proportional action, when the deviation change speed is small, the differential action is weakened to enlarge the proportional action. The following measures are taken for parameter correction of each region:
region 1: pound-pound control does not require parameter calibration;
region 2: exceeding the limit of the variation rate of the deviation, the differential action should be increased through parameter correction;
regions 3, 6, 9: the system works in an ideal state, does not need parameter correction and adopts a parameter keeping mode;
region 41: the deviation change rate is small, the deviation is large, and the proportional action is increased through parameter correction;
region 42: the deviation change rate is ideal, no parameter correction is needed, and a parameter holding mode is adopted;
region 5: the deviation rate enters the steady state requirement, and the proportional action is weakened through parameter correction;
region 71: the deviation enters the steady-state requirement, the deviation change rate is high, the differential action is increased and the positive feedback is introduced through parameter correction, and a strong differential plus positive feedback control mode is formed;
region 72: the deviation change rate is large, and the differential action is increased and the proportional action is weakened through parameter correction;
region 8: the deviation enters the steady-state requirement, but has a smaller deviation change rate, and the differentiation effect is increased through parameter correction;
region 101: overshoot occurs, the deviation is large, the deviation change speed is small, and the proportional action is slightly increased and the differential action is reduced through parameter correction;
region 102: the overshoot is small, the deviation change speed is small, the steady state requirement is not met, and the proportional action is slightly increased;
region 103: the overshoot is larger, the deviation change speed is also larger, and the differential action and the proportional action are slightly increased through parameter correction;
region 104: the overshoot is small, but the deviation change speed is still large, and the differential action is enhanced and the proportional action is slightly weakened through parameter correction.
Characteristic model phi of parameter correction stage of unmanned ship2＝{Φ21，Φ22}，
Wherein phi21＝{φ212，φ2141，φ215，φ2171，φ2172，φ218The feature model of 2 and 4 quadrants,
Φ22＝{φ22101，φ22102，φ22103，φ22104the feature model of the 1 and 3 quadrants is adopted. In the formula:
decision modality level Ψ of unmanned surface vehicle parameter correction level2＝{Ψ21，Ψ22}, wherein: Ψ21＝{ψ21i},i＝2,41,5,71,72,8Ψ22＝{ψ22i101,102,103,104, wherein:
ψ212＝{Kd＝Kd+kd1} ψ2141＝{Kp＝Kp+kp1}
ψ215＝{Kp＝Kp-kp1} ψ2171＝{Kd＝Kd+kd1,Sp＝-1}
ψ2172＝{Kd＝Kd+kd1,Kp＝Kp-kp1} ψ218＝{Kd＝Kd+kd1}
ψ22101＝{Kd＝Kd+kd2,Kp＝Kp-kp2} ψ22102＝{Kp＝Kp+kp2}
ψ22103＝{Kd＝Kd+kd2,Kp＝Kp+kp2} ψ22104＝{Kd＝Kd+kd2,Kp＝Kp-kp2}
the symbols in the formula have the following meanings: k is a radical ofp1,kd1,kp2,kd2: 2. a proportional or differential increase or decrease coefficient of 4 quadrants, an l, 3 quadrant or differential increase or decrease coefficient.
Inference rule set omega of unmanned ship parameter correction level2＝{Ω21,Ω22}, wherein: omega21＝{ω21i},i＝2,41,5,71,72,8，Ω22＝{ω22i},i＝101,102,103,104。
In the formula:
3. task adaptation level
According to different control systems and different tasks, all parameters and closed values of an operation control stage and a parameter correction stage are put in and modified, input values (deviation and change rate of the deviation) of a controller are normalized, output values of the controller are subjected to inverse normalization, stability of the control system is monitored, and the like.
Firstly, normalizing the input value (deviation and change rate of the deviation) of the controller according to the control system parameters; then, obtaining normalized control output through calculation of a parameter correction stage and an operation control stage; and finally, performing inverse normalization on the normalized control output value control actuating mechanism to obtain an actual control output value.
Characteristic model phi of task adaptation level of unmanned ship3＝{φ31,φ32}, wherein: phi is a31For the input of a normalized control feature model, phi32To output an inverse normalized control feature model.
Unmanned ship task adaptation level control mode set psi3＝{ψ31,ψ32}, wherein:
ψ32＝{un＝Umax·un,0,if|un,0|＞1thenun,0＝sgn(un,0)}. The symbols in the formula have the following meanings:
en,0,inputs for deviation and deviation rate of change normalization; e.g. of the typen,The deviation and the rate of change of deviation of the control system; dmax,Vmax: maximum traction distance of controlled quantity, maximum traction speed of controlled quantity; u. ofn,un,0,Umax: the output of the actual controller, the normalized controller output, controls the maximum input of the actuator.
At the mission adaptation level of unmanned boatsInference rule set omega3＝{ω31,ω32}, wherein:
the control method achieves the effect of the invention, and the unmanned ship can independently maintain the region in the process of sailing and smoothly complete the preset task through the mutual cooperation of the modules in the process of searching, rescuing and surveying the regional ocean.
Claims (5)
1. A water jet propulsion unmanned ship autonomous region keeping humanoid intelligent control method is characterized in that: the method comprises the steps of analyzing and calculating key parameters of parameters measured by a pose sensor of a control boundary, comparing the calculated values and actual values of the key parameters by a region maintenance judging system, judging whether region maintenance is needed or not, solving a deviation equation of an under-actuated water jet propulsion unmanned ship motion equation and a region maintenance stopping position trajectory equation if the region maintenance is needed, transmitting data information to a humanoid intelligent algorithm controller, determining a region maintenance control strategy by the humanoid intelligent controller according to the regulated amount, the deviation and the variation trend of the deviation, and implementing the region maintenance control strategy through an executing mechanism.
2. The humanoid intelligent control method for autonomous region maintenance of a water jet propelled unmanned ship according to claim 1, wherein the analysis and calculation of the key parameters specifically comprises:
(1) whether the track is in the area maintained by the area, namely the maximum value r of the distance from the center O of the area to the track of the unmanned boatmaxThe calculation formula is as follows:
wherein (X)O,YO) Is the coordinate of the area center O, (X, Y) is the unmanned boat motion track coordinate,
(2) the limit movement distance k is the limit range r 'of the unmanned ship for control measures'maxThe calculation formula is as follows:
wherein R is the area radius and the direction angle of the interference force action,
(3) propulsion device reset angle thetaFThat is, when the heading angle ψ is equal to the return angle, the propulsion device is reset, and the calculation formula is:
r' is the radius of the inner circle,
(4) a propulsion device stop position, wherein O is set as a stop position when the sea state is unknown, O' is set as a stop position when the sea state is known and is at or below the sea state of four levels,
wherein O' is the intersection point of the interference force passing through the center of the circle and the inner circle.
3. The humanoid intelligent control method for autonomous region conservation of a water jet propelled unmanned ship according to claim 2, wherein the solving of the deviation equation of the motion equation of the under-actuated water jet propelled unmanned ship and the trajectory equation of the region conservation stopping position specifically comprises:
the motion equation of the 3 degrees of freedom of the unmanned ship horizontal plane is as follows:
wherein η ═ x, y, ψ]T、vT＝[u,v,r]、τ＝[τ1,0,τ3]TRespectively position, velocity and propulsion, tau1And τ3Respectively the longitudinal propulsion and the bow-turning moment m of the unmanned surface vehicle11,m22,m33The system inertia matrix parameters containing the additional mass, and the rest hydrodynamic coefficients;
the start-up area holds the reference trajectory (x) of the control boundaryd,yd,ψd,ud,vd,rd) And a reference control input (tau)1d,τ3d) Satisfy the requirement of
Carrying out differential homoembryo transformation on the motion equation of 3 degrees of freedom of the unmanned surface vehicle horizontal plane so as to ensure that
z1＝x cosψ-y sinψ
z3＝ψ
z5＝v
z6＝r
And control input transformation
The new state equation after the arrangement is
Differential homomorphic transformation equation for solving reference locus of area keeping control boundary in same wayDefining a deviation
e＝[e1,e2,e3,e4,e5,e6]T＝[z1-z1d,z2-z2d,z3-z3d,z4-z4d,z5-z5d,z6-z6d]T
Then
Elimination of z from the formula1,z2,z3,z4,z5,z6，
z4z6-z4dz6d
＝z4z6-z4z6d+z4z6d-z4dz6d
＝z4e6-z4de6+z4de6+z6de4
＝e4e6+z4de6+z6de4
Equation of deviation
Wherein,
4. the humanoid intelligent control method for autonomous region maintenance of the water jet propelled unmanned boat as claimed in claim 3, wherein the judging whether region maintenance is required specifically comprises: when r is less than or equal to rmaxIn time, no start-up zone maintenance is required; when r ismax＜r≤r′maxWhen zero, starting the area to keep; when r is > r'maxWhen this happens, the region remains failed.
5. The humanoid intelligent control method for autonomous region conservation of a water jet propelled unmanned ship according to claim 4, wherein the determining a region conservation control strategy specifically comprises:
the human-simulated intelligent integration algorithm is as follows:
wherein u is a control amount, e is a deviation,is a derivative of the deviation, KpTo proportional gain, KiTo integrate the gain, KdIs the differential gain;
(1) operation control stage
The ideal error target trajectory isThe characteristic basic element set of the man-boat operation control level is as follows: q1＝{q1,q2,q3,q4,q5,q6,q7}，
Wherein:q2:|en|≥e1,q3:|en|≥e2,q4:|en|≥e3,
the model of the unmanned ship operation control level is phi1＝{φ11,φ12,φ13,φ14,φ15,φ16,φ17,φ18,φ19,φ110}
Wherein:
control mode level psi of unmanned ship operation control level1＝{ψ11，ψ12，ψ13，ψ14，ψ15，ψ16}
Wherein:
in the formula, symbol un,un-1The nth, n-1 time output of the controller; u shapemaxA maximum value of the controller output;controlling the deviation and the deviation change rate of the system for the nth time; e.g. of the typemiControlling the ith extreme value of the system deviation; kp,Kd,Ki,SpProportional coefficient, differential coefficient, integral coefficient, sign of proportional coefficient, gain suppression factor of k controller;
inference rule set omega of unmanned ship operation control level1＝{ω11,ω12,ω13,ω14,ω15,ω16}
Wherein:
(2) parameter correction stage
Feature element set Q of unmanned ship parameter correction stage2＝{q1,q2,q3,q4,q5,q6,q7,q8,q9}
Wherein:
characteristic model phi of parameter correction stage of unmanned ship2＝{Φ21，Φ22}，
Wherein phi21＝{φ212，φ2141，φ215，φ2171，φ2172，φ218The feature model of 2 and 4 quadrants,
Φ22＝{φ22101，φ22102，φ22103，φ22104the method is a characteristic model of quadrants 1 and 3, wherein:
decision modality level Ψ of unmanned surface vehicle parameter correction level2＝{Ψ21，Ψ22}, wherein: Ψ21＝{ψ21i},i＝2,41,5,71,72,8Ψ22＝{ψ22i101,102,103,104, wherein:
ψ212＝{Kd＝Kd+kd1}ψ2141＝{Kp＝Kp+kp1}
ψ215＝{Kp＝Kp-kp1}ψ2171＝{Kd＝Kd+kd1,Sp＝-1}
ψ2172＝{Kd＝Kd+kd1,Kp＝Kp-kp1}ψ218＝{Kd＝Kd+kd1}
ψ22101＝{Kd＝Kd+kd2,Kp＝Kp-kp2}ψ22102＝{Kp＝Kp+kp2}
ψ22103＝{Kd＝Kd+kd2,Kp＝Kp+kp2}ψ22104＝{Kd＝Kd+kd2,Kp＝Kp-kp2}
the symbols in the formula have the following meanings: k is a radical ofp1,kd1,kp2,kd2: 2. a proportional or differential increase or decrease coefficient of 4 quadrants, an l, 3 quadrant or differential increase or decrease coefficient,
inference rule set omega of unmanned ship parameter correction level2＝{Ω21,Ω22}, wherein: omega21＝{ω21i},i＝2,41,5,71,72,8，Ω22＝{ω22i},i＝101,102,103,104，
In the formula:
(3) task adaptation level
Characteristic model phi of task adaptation level of unmanned ship3＝{φ31,φ32}, wherein: phi is a31For the input of a normalized control feature model, phi32Outputting an inverse normalized control feature model;
unmanned ship task adaptation level control mode set psi3＝{ψ31,ψ32}, wherein:
ψ32＝{un＝Umax·un,0,if|un,0|＞1thenun,0＝sgn(un,0) The symbols in the formula have the following meanings:
inputs for deviation and deviation rate of change normalization;the deviation and the rate of change of deviation of the control system; dmax,Vmax: maximum traction distance of controlled quantity, maximum traction speed of controlled quantity; u. ofn,un,0,Umax: the output of the actual controller, the normalized controller output, the maximum input of the control actuator;
inference rule set omega of task adaptation level of unmanned ship3＝{ω31,ω32}, wherein:
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107255923A (en) * | 2017-06-14 | 2017-10-17 | 哈尔滨工程大学 | Drive lacking unmanned boat Track In Track control method based on the RBF ICA CMAC neutral nets recognized |
| CN109634099A (en) * | 2018-12-29 | 2019-04-16 | 百度在线网络技术（北京）有限公司 | Method for controlling a vehicle and device |
| CN111781923A (en) * | 2020-06-16 | 2020-10-16 | 北京航天控制仪器研究所 | Water surface unmanned boat area maintaining control system and method |
| CN112486186A (en) * | 2020-12-14 | 2021-03-12 | 浙江嘉蓝海洋电子有限公司 | Unmanned surface vessel autonomous navigation method based on Bayes multi-feature fusion |
| CN112650257A (en) * | 2020-12-30 | 2021-04-13 | 大连海事大学 | Unmanned ship area maintaining control method and device |
| CN112904719A (en) * | 2021-01-15 | 2021-06-04 | 哈尔滨工程大学 | Annular area tracking control method suitable for underwater robot position |
| CN114488777A (en) * | 2022-01-17 | 2022-05-13 | 国能神华九江发电有限责任公司 | Coordination control strategy based on humanoid intelligent control |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102945045A (en) * | 2012-11-19 | 2013-02-27 | 上海大学 | Unmanned ship combined obstacle avoiding device and method based on laser sensor and forward looking sonar |
| JP2015203938A (en) * | 2014-04-14 | 2015-11-16 | 株式会社Ihi | Abnormal-time return control method for autonomous vehicle and autonomous vehicle for use to implement abnormal-time return control method |
-
2016
- 2016-12-21 CN CN201610942248.9A patent/CN106444359A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102945045A (en) * | 2012-11-19 | 2013-02-27 | 上海大学 | Unmanned ship combined obstacle avoiding device and method based on laser sensor and forward looking sonar |
| JP2015203938A (en) * | 2014-04-14 | 2015-11-16 | 株式会社Ihi | Abnormal-time return control method for autonomous vehicle and autonomous vehicle for use to implement abnormal-time return control method |
Non-Patent Citations (6)
| Title |
|---|
| 付明玉 等: "环境最优区域动力定位控制方法的研究", 《中国造船》 * |
| 吴恭兴: "无人艇操纵性与智能控制技术研究", 《万方数据库》 * |
| 曾薄文: "喷水推进水面无人艇的非线性控制方法研究", 《万方数据库》 * |
| 王德胜: "《普通管理学》", 30 September 2001, 北京：北京师范大学出版社 * |
| 瞿少成: "《应用非线性控制技术》", 31 May 2008, 长沙：国防科技大学出版社 * |
| 邹金锟: "基于运动仿真的无人艇区域保持方法研究", 《万方数据库》 * |
Cited By (11)
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|---|---|---|---|---|
| CN107255923A (en) * | 2017-06-14 | 2017-10-17 | 哈尔滨工程大学 | Drive lacking unmanned boat Track In Track control method based on the RBF ICA CMAC neutral nets recognized |
| CN107255923B (en) * | 2017-06-14 | 2020-04-03 | 哈尔滨工程大学 | RBF identification-based under-actuated unmanned ship track tracking control method of ICA-CMAC neural network |
| CN109634099A (en) * | 2018-12-29 | 2019-04-16 | 百度在线网络技术（北京）有限公司 | Method for controlling a vehicle and device |
| CN111781923A (en) * | 2020-06-16 | 2020-10-16 | 北京航天控制仪器研究所 | Water surface unmanned boat area maintaining control system and method |
| CN111781923B (en) * | 2020-06-16 | 2023-07-14 | 航天时代(青岛)海洋装备科技发展有限公司 | Water surface unmanned ship area maintenance control system and method |
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| CN112486186B (en) * | 2020-12-14 | 2022-09-16 | 浙江嘉蓝海洋电子有限公司 | Unmanned surface vessel autonomous navigation method based on Bayes multi-feature fusion |
| CN112650257A (en) * | 2020-12-30 | 2021-04-13 | 大连海事大学 | Unmanned ship area maintaining control method and device |
| CN112650257B (en) * | 2020-12-30 | 2024-02-02 | 大连海事大学 | Unmanned ship area maintenance control method and device |
| CN112904719A (en) * | 2021-01-15 | 2021-06-04 | 哈尔滨工程大学 | Annular area tracking control method suitable for underwater robot position |
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