CN110609473A - Control method for uncertain model robot - Google Patents

Control method for uncertain model robot Download PDF

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CN110609473A
CN110609473A CN201910836293.XA CN201910836293A CN110609473A CN 110609473 A CN110609473 A CN 110609473A CN 201910836293 A CN201910836293 A CN 201910836293A CN 110609473 A CN110609473 A CN 110609473A
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CN110609473B (en
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李芃
张鑫
张兰勇
刘胜
刘洪丹
曹聪
李华健
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Harbin Engineering University
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

The invention discloses a control method for an uncertain model robot. The invention aims at establishing a dynamic model for an uncertain model robot system based on an omnidirectional wheel, converting the dynamic model into a pose error equation in a motion process, deducing the error equation, taking the translational linear velocity and the rotational angular velocity in the equation as virtual control quantities of the system, combining sliding mode variable structure control and self-adaptive control, adopting a Backstepping design method, aiming at an airport runway detection robot system, designing a novel motion control method, and solving the problem of motion control of the nonlinear robot system with uncertain parameters. The method can effectively control the movement of the nonlinear airport runway detection robot, and has the characteristics of good control effect, model uncertainty elimination, strong robustness and shake reduction.

Description

Control method for uncertain model robot
Technical Field
The invention relates to a robot motion control method, in particular to a self-adaptive inversion sliding mode control method of an uncertain model robot, and belongs to the technical field of motion control of uncertain model robots.
Background
With the development of the modern aviation industry, the safety problem of the airport runway is more and more emphasized as an important platform for the takeoff and landing of the airplane. Defects and foreign bodies on the runway of the airport pose a great threat to the taking-off and landing process of the airplane. Therefore, the safety detection of the airport runway has important practical significance for ensuring the smooth takeoff and landing of the airplane. Automatic inspection by using the omnidirectional wheel airport runway inspection robot is a practical solution, and accurate motion control is very important for the robot.
The airport runway detection robot developed based on the omnidirectional wheel type mobile robot is a multivariable and strong-coupling nonlinear system, the parameters of the airport runway detection robot have the characteristics of inaccurate measurement and time-varying, disturbance is extremely easy to occur in the working process, an accurate robot model is difficult to establish, the established robot model often has unknown parameters, based on a system model containing the unknown parameters, the traditional PID control method cannot accurately control the system, and the motion control deviation is large.
Disclosure of Invention
Aiming at the prior art, the invention aims to provide a control method of an uncertain model robot, which can enhance the robustness of adaptive control, effectively eliminate the uncertain influence of a model in sliding mode variable structure control and weaken shake.
In order to solve the above problems, the present invention provides a method for controlling an uncertain model robot, wherein the uncertain model robot is provided with a mecanum wheel motion chassis, comprising the steps of:
s1: the robot coordinate system is established on a robot chassis, a global coordinate system is established on the robot motion environment, the coordinate systems are rectangular coordinate systems, the included angle theta between the robot and an x axis under the global coordinate system, and the conversion relation between the robot coordinate system and the global coordinate system is as follows:
performing dynamic modeling according to a Mecanum wheel chassis of the uncertain model robot to obtain a dynamic model;
s2: decomposing a dynamic model of the uncertain model robot into accelerations in the transverse direction and the longitudinal direction under a global coordinate system and an angular acceleration in the advancing direction of the robot;
s3: setting a precondition for designing the motion controller of the uncertain model robot;
s4: describing the pose error of the robot system in the uncertain model robot coordinate system, and differentiating the pose error;
s5: according to an inversion design method, the translation linear velocity v and the rotation angular velocity omega of the uncertain model robot system are used as virtual control quantities, and an adaptive inversion sliding mode controller is designed to obtain a motion control law and a parameter adaptive law of the system.
The invention also includes:
the dynamic model under the global coordinate system in S1 satisfies the following conditions:
wherein the content of the first and second substances,is the pose of the robot system, x, y are position coordinates, theta is the heading angle,is the position velocity and angular velocity of the robot system,is the position acceleration and angular acceleration of the robot systemThe speed of the motor is controlled by the speed of the motor,is an inertia matrix, wherein m is the weight of the robot, l is half of the axle distance of the robot,in order to be a damping matrix, the damping matrix,is a torque conversion matrix, r is the wheel radius,in order to be a matrix of the restraining forces,is the input torque, τ, of the robot system1,τ2,τ3,τ4The input torque of the four wheels is respectively, the lambda is the constraint force of the robot system, and the rotating speed of the four wheels is controlled by respectively controlling the torque input by the four wheels.
The accelerations in the lateral and longitudinal directions in the global coordinate system and the angular acceleration in the robot heading direction in S2 satisfy:
whereinu1＝τ1+τ2+τ3+τ4For control input of position, u2＝τ1-τ2+τ3-τ4Is a control input for the gesture and,andis that of the systemA linear error function.
The preconditions in S3 are:
(1) the robot system is assumed to be in a motion state relative to the ground at any time in the process of traveling;
(2) the non-linear error function is bounded, i.e.Andis bounded.
And 4, the pose errors of the robot system under the global coordinate system in the S4 satisfy the following conditions:
wherein x ise,ye,θePosition and attitude errors, x, for robotic systemsr,yr,θrSetting a motion track of the robot system;
under the constraint conditionThe differential of the pose error is:
where v is the linear velocity of the robot, ω is the angular velocity of the robot, and v and ω are:
the virtual control amount of the robot system in S5 is the virtual control amount of the translational linear velocity v and the rotational angular velocity ω of the robot system, and the difference between the virtual control amount and the virtual feedback is the velocity error variable:
wherein the content of the first and second substances,anderror variables, v, of translational linear velocity and rotational angular velocity, respectivelydAnd ωdVirtual feedback values of the translation linear velocity and the rotation angular velocity;
the obtained motion control law and parameter self-adaptive law are respectively as follows:
wherein: u. of1And u2Respectively are the control laws of the system position and posture,andare respectively beta1And beta2Is estimated by1＝v-vdAnd s2＝ω-ωdA switching function for the sliding mode control method, c1,c2And k is a design parameter;andfor adaptive law of unknown parameters, gamma1And gamma2Is a parametric adaptive gain constant.
The invention has the beneficial effects that: according to the method, sliding mode variable structure control and self-adaptive control are combined, a Backstepping design method is adopted, a novel motion control method is designed for the airfield runway detection robot system, and the problem of motion control of the nonlinear robot system with uncertain parameters is solved. The motion control method has the advantages of two control methods: the robustness of the adaptive control can be enhanced by the sliding mode variable structure control, the uncertainty influence of the model in the sliding mode variable structure control can be effectively eliminated by the adaptive control, and the shake can be weakened. The novel control strategy has the characteristics of eliminating model uncertainty, having strong robustness and effectively weakening trembling and shaking when aiming at the motion control system of the robot for detecting the airport runway.
Drawings
Fig. 1 is a flowchart of a sliding mode control method for adaptive inversion of an airport runway detection robot according to the present invention.
Fig. 2 is a relation diagram of a body coordinate system and a global coordinate system of the airport runway detection robot.
Fig. 3 is a force analysis diagram of a body part of the airport runway detection robot.
Fig. 4 is a stress analysis diagram of a wheel part of the airport runway detection robot.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
The invention designs an airport runway detection robot system, establishes a dynamic model of the airport runway detection robot for the robot system, converts the dynamic model into a pose error equation, designs an adaptive sliding mode controller by adopting a Backstepping method on the basis of the pose error equation, and controls the movement of the airport runway detection robot through the controller, wherein a control flow chart is shown in figure 1.
An airport runway detection robot self-adaptive inversion sliding mode control method comprises the following steps:
step S1: performing dynamic modeling according to an omnidirectional wheel motion chassis of the airport runway detection robot to obtain a dynamic model;
the step S1 specifically includes:
step S11: as shown in fig. 2, a robot body coordinate system is established on a robot chassis, a global coordinate system is established on a robot motion environment, the coordinate systems are rectangular coordinate systems, an included angle θ between the robot and an x axis is formed in the global coordinate system, and a conversion relationship between the robot body coordinate system and the global coordinate system is as follows:
step S12: the robot chassis part can be decomposed into a vehicle body part and a wheel part, and the force of the vehicle body and the wheel part is analyzed respectively, as shown in fig. 3 and fig. 4. A Lagrange equation method is adopted according to the stress analysis to establish a dynamic model of the airport runway detection robot:
step S2: decomposing a dynamic model of the airport runway detection robot into transverse and longitudinal accelerations and an angular acceleration of the robot in the advancing direction;
the step S2 specifically includes:
step S21: the accelerations in the transverse and longitudinal directions and the angular acceleration in the advancing direction of the robot can be obtained by the dynamic formula of the system:
step S3: setting a precondition for designing a motion controller of the airport runway detection robot;
the step S3 specifically includes:
step S31: under the constraint condition:in the following, it is assumed that:
1) the robot system is assumed to be in a motion state relative to the ground at any time in the process of traveling;
2) the non-linear error function is bounded, i.e.Andis bounded.
On the basis of the analysis, the motion control problem of the airport runway detection robot is regarded as searching for the input torque, so that the tracking error of the robot system approaches to 0 infinitely.
Step S4: describing the pose error of the robot system in a robot coordinate system, and differentiating the pose error;
the step S4 specifically includes:
step S41, the pose error of the airport runway detection robot is as follows:
step S42: differentiating the above equation under constraint conditions can obtain:
where v is the linear velocity of the robot, ω is the angular velocity of the robot, vr＝xrcosθr+yrsinθr，
Step S43: v and ω can be expressed as:
step S5: according to an inversion design method, taking the translational linear velocity v and the rotation angular velocity omega of the uncertain model robot system as virtual control quantities, and carrying out adaptive inversion sliding mode controller design to finally obtain a motion control law and a parameter adaptive law of the system;
the step S5 specifically includes:
step S51: because the error differential formula does not have actual control input, a Backstepping method is adopted, v and omega are used as virtual control quantities, an actual control law is deduced, and the motion control of the robot is realized.
Step S52: the difference between the virtual control quantity and the virtual feedback value is a speed error variable:
step S53: taking a switching function s controlled by a sliding mode variable structure as follows:
since Backstepping design method actually makes the error variable 0 at each step, under this condition, the sliding condition of sliding mode variable structure control is naturally achieved:
step S54: defining a Lyapunov function for a pose error equation of the airport runway detection robot:
where k is a normal number.
To V1The derivation can be:
step S55: taking the virtual feedback value as:
step S56: tidying Lyapunov function V1Derivative of (2)The following can be obtained:
step S57: the derivative with respect to time is taken for s:
step S58: and obtaining a motion control law and a parameter self-adaptive law of the system at the last step of the Backstepping design method derivation process. Introduction ofAnd their estimated valuesAndand a parameter adaptive gain constant gamma1And gamma2And defining a Lyapunov function:
step S59: to V2Taking the derivative, we can get:
step S510: nonlinear error function of systemAndis bounded. Then there is:
step S511:satisfies the following conditions:
step S512: the motion control law and the parameter self-adaption law of uncertain parameters of the airport runway detection robot can be obtained by the following formula:
the specific implementation mode of the invention also comprises:
a Backstepping sliding mode control method for an airport runway detection robot comprises the following steps:
s1: the robot coordinate system is established on a robot chassis, a global coordinate system is established on the robot motion environment, the coordinate systems are rectangular coordinate systems, the included angle theta between the robot and an x axis under the global coordinate system, and the conversion relation between the robot coordinate system and the global coordinate system is as follows:
performing dynamic modeling according to a Mecanum wheel chassis of the uncertain model robot to obtain a dynamic model;
s2: decomposing a dynamic model of the uncertain model robot into accelerations in the transverse direction and the longitudinal direction under a global coordinate system and an angular acceleration in the advancing direction of the robot;
s3: setting a precondition for designing the motion controller of the uncertain model robot;
s4: describing the pose error of the robot system in the uncertain model robot coordinate system, and differentiating the pose error;
s5: according to an inversion design method, the translation linear velocity v and the rotation angular velocity omega of the uncertain model robot system are used as virtual control quantities, an adaptive inversion sliding mode controller is designed, and finally a motion control law and a parameter adaptive law of the system are obtained.
Further, the dynamics model of the airport runway inspection robot in S1 satisfies:
wherein the content of the first and second substances,is the pose of the robot system (x, y are position coordinates, theta is the heading angle),is the position and angular velocity of the robotic system,is the position and angular acceleration of the robot system,is an inertia matrix (m is the weight of the robot, l is half of the axle distance of the robot),in order to be a damping matrix, the damping matrix,is the moment transformation matrix (r is the wheel radius),in order to be a matrix of the restraining forces,is the input torque of the robot system, and λ is the restraining force of the robot system.
In S2, the dynamical model of the airport runway inspection robot may be decomposed into accelerations in the lateral and longitudinal directions and angular acceleration in the robot heading direction:
wherein(r is the radius of the wheel, m is the weight of the robot, l is half of the wheelbase of the robot) u1＝τ1+τ2+τ3+τ4For control input of position, u2＝τ1-τ2+τ3-τ4A control input that is a gesture; andis a nonlinear error function of the system.
In S3, the following two assumptions are set:
1) the robot system is assumed to be in a motion state relative to the ground at any time in the process of traveling;
2) the non-linear error function is bounded, i.e.Andis bounded.
In S4, the pose error of the robot system is:
wherein x ise,ye,θePosition and attitude errors, x, for robotic systemsr,yr,θrAnd setting a value for the motion trail of the robot system.
The derivative of the pose error is:
where v is the linear velocity of the robot and ω is the angular velocity of the robot. v and ω can be expressed as:
in S5, the translational linear velocity v and the rotational angular velocity ω of the robot system are used as virtual control amounts, and the difference between the virtual control amount and the virtual feedback is used as a velocity error variable:
wherein the content of the first and second substances,anderror variables, v, of translational linear velocity and rotational angular velocity, respectivelydAnd ωdVirtual feedback values for translational linear velocity and rotational angular velocity.
The obtained motion control law and parameter self-adaptive law are respectively as follows:
wherein: u. of1And u2Respectively are the control laws of the system position and posture,andare respectively beta1And beta2Is estimated by1And s2A switching function for the sliding mode control method, c1,c2And k is a design parameter;andfor adaptive law of unknown parameters, gamma1And gamma2Is a parametric adaptive gain constant.
The drawings illustrate embodiments of the present invention in detail, but the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (6)

1. A control method of an indeterminate model robot provided with a mecanum wheel motion chassis, characterized by comprising the steps of:
s1: the robot coordinate system is established on a robot chassis, a global coordinate system is established on the robot motion environment, the coordinate systems are rectangular coordinate systems, the included angle theta between the robot and an x axis under the global coordinate system, and the conversion relation between the robot coordinate system and the global coordinate system is as follows:
performing dynamic modeling according to a Mecanum wheel chassis of the uncertain model robot to obtain a dynamic model;
s2: decomposing a dynamic model of the uncertain model robot into accelerations in the transverse direction and the longitudinal direction under a global coordinate system and an angular acceleration in the advancing direction of the robot;
s3: setting a precondition for designing the motion controller of the uncertain model robot;
s4: describing the pose error of the robot system in the uncertain model robot coordinate system, and differentiating the pose error;
s5: according to an inversion design method, the translation linear velocity v and the rotation angular velocity omega of the uncertain model robot system are used as virtual control quantities, and an adaptive inversion sliding mode controller is designed to obtain a motion control law and a parameter adaptive law of the system.
2. The control method for an uncertain model robot as recited in claim 1, wherein: s1, the dynamic model under the global coordinate system meets the following conditions:
wherein the content of the first and second substances,is the pose of the robot system, x, y are position coordinates, theta is the heading angle,is the position velocity and angular velocity of the robot system,is the positional acceleration and angular acceleration of the robot system,is an inertia matrix, wherein m is the weight of the robot, l is half of the axle distance of the robot,in order to be a damping matrix, the damping matrix,is a torque conversion matrix, r is the wheel radius,in order to be a matrix of the restraining forces,is the input torque, τ, of the robot system1,τ2,τ3,τ4The input torque of the four wheels is respectively, the lambda is the constraint force of the robot system, and the rotating speed of the four wheels is controlled by respectively controlling the torque input by the four wheels.
3. The control method for an uncertain model robot as recited in claim 1, wherein: s2 the acceleration in the lateral and longitudinal directions and the angular acceleration in the robot heading direction in the global coordinate system satisfy:
whereinu1＝τ1+τ2+τ3+τ4For control input of position, u2＝τ1-τ2+τ3-τ4Is a control input for the gesture and,andis a nonlinear error function of the system.
4. The control method for an uncertain model robot as recited in claim 1, wherein: s3, the precondition is:
(1) the robot system is assumed to be in a motion state relative to the ground at any time in the process of traveling;
(2) the non-linear error function is bounded, i.e.Andis bounded.
5. The control method for an uncertain model robot as recited in claim 1, wherein: s4, the pose errors of the robot system in the global coordinate system satisfy the following conditions:
wherein x ise,ye,θePosition and attitude errors, x, for robotic systemsr,yr,θrSetting a motion track of the robot system;
under the constraint conditionThe differential of the pose error is:
where v is the linear velocity of the robot, ω is the angular velocity of the robot, and v and ω are:
6. the control method for an uncertain model robot as recited in claim 1, wherein: s5, the virtual control quantity of the robot system is the translational linear velocity v and the rotational angular velocity ω of the robot system are virtual control quantities, and the difference between the virtual control quantity and the virtual feedback is a velocity error variable:
wherein the content of the first and second substances,anderror variables, v, of translational linear velocity and rotational angular velocity, respectivelydAnd ωdVirtual feedback values of the translation linear velocity and the rotation angular velocity;
the obtained motion control law and parameter self-adaptive law are respectively as follows:
wherein: u. of1And u2Respectively are the control laws of the system position and posture, andare respectively beta1And beta2Is estimated by1＝v-vdAnd s2＝ω-ωdA switching function for the sliding mode control method, c1,c2And k is a design parameter;andfor adaptive law of unknown parameters, gamma1And gamma2Is a parametric adaptive gain constant.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112540536A (en) * 2020-11-27 2021-03-23 南京航空航天大学 Design method of sliding mode optimization controller of worm pipeline robot
CN113467475A (en) * 2021-08-02 2021-10-01 上海工程技术大学 Track tracking sliding mode control method for Mecanum wheel omnidirectional mobile robot
CN114407007A (en) * 2022-01-17 2022-04-29 山东新一代信息产业技术研究院有限公司 Self-adaptive nonsingular terminal sliding mode control method and device for mechanical arm and medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170138623A (en) * 2016-06-07 2017-12-18 중앙대학교 산학협력단 Apparatus and method for controlling adaptive synchronized tracking of heterogeneous spherical robots using distributed hierarchical sliding surfaces under a directed graph
CN107544256A (en) * 2017-10-17 2018-01-05 西北工业大学 Underwater robot sliding-mode control based on adaptive Backstepping
CN108776432A (en) * 2018-06-08 2018-11-09 哈尔滨工程大学 Network-based airfield runway detection robot forecast Control Algorithm
CN109960150A (en) * 2019-03-30 2019-07-02 南京理工大学 Crusing robot Trajectory Tracking Control method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170138623A (en) * 2016-06-07 2017-12-18 중앙대학교 산학협력단 Apparatus and method for controlling adaptive synchronized tracking of heterogeneous spherical robots using distributed hierarchical sliding surfaces under a directed graph
CN107544256A (en) * 2017-10-17 2018-01-05 西北工业大学 Underwater robot sliding-mode control based on adaptive Backstepping
CN108776432A (en) * 2018-06-08 2018-11-09 哈尔滨工程大学 Network-based airfield runway detection robot forecast Control Algorithm
CN109960150A (en) * 2019-03-30 2019-07-02 南京理工大学 Crusing robot Trajectory Tracking Control method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
QIAO JIHONG.ETC: "Robust Adaptive Fuzzy Output Tracking Control of Uncertain Robot System Using Backstepping Desig", 《2007 CHINESE CONTROL CONFERENCE》 *
张鑫等: "参数不确定移动机器人全局轨迹跟踪的自适应滑模控制", 《微电子学与计算机》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112540536A (en) * 2020-11-27 2021-03-23 南京航空航天大学 Design method of sliding mode optimization controller of worm pipeline robot
CN113467475A (en) * 2021-08-02 2021-10-01 上海工程技术大学 Track tracking sliding mode control method for Mecanum wheel omnidirectional mobile robot
CN113467475B (en) * 2021-08-02 2023-08-25 上海工程技术大学 Mecanum wheel omnidirectional mobile robot track tracking sliding mode control method
CN114407007A (en) * 2022-01-17 2022-04-29 山东新一代信息产业技术研究院有限公司 Self-adaptive nonsingular terminal sliding mode control method and device for mechanical arm and medium

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