CN117572810A - Mechanical arm safety cooperative control system based on control obstacle function - Google Patents
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Abstract
The invention discloses a mechanical arm safety cooperative control system based on a control obstacle function, which realizes a consistency formation cooperative control task under the condition that a mechanical arm dynamics system is subjected to external interference caused by a complex environment, and improves the robustness and anti-interference performance of a control strategy. When the multi-mechanical arm safety tracking consistency control protocol is executed, the formation consistency control task can be continuously completed even if the multi-mechanical arm system encounters a dynamic and static obstacle. Through the design of the safety optimization module based on the control obstacle function, the safety formation control can be effectively realized when the system completes the bottom layer consistency control task, and the loss caused by collision in the use process of the mechanical arm is avoided. Meanwhile, the control structure of the modularized design of the invention obviously reduces the complexity of the design of the controller, reduces the calculation load, improves the flexibility of the design of the controller and is convenient for engineering realization.
Description
Technical Field
The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm safety cooperative control system based on a control obstacle function.
Background
The mechanical arm is used as a product of continuous development of scientific technology in modern production and life, and has the characteristics of high precision, wide application range, high cost benefit, strong programmability and the like, thereby being one of important equipment and technology for industrial automation and intelligent manufacturing at present and playing a wide and important application in national defense, national economic construction and scientific technology development. However, in more cases, the task types that can be completed by the single mechanical arm are relatively single, and the working efficiency is low. Compared with a single mechanical arm, the multi-mechanical arm cluster cooperation can optimize task allocation, improve task execution efficiency and reduce task execution time, and meanwhile, when part of individuals fail, the multi-mechanical arm cooperation system still has certain integrity and can continue to execute tasks. In general, the cooperation of multiple mechanical arms can not only exert the advantages of a single mechanical arm, but also avoid the problems of limited task execution and the like caused by the limitation of the single mechanical arm, and become an important development direction of the mechanical arm in the future.
The problem of consistency of mechanical arm formation is a key problem in cooperative control of multiple mechanical arms, and a foundation can be provided for realization of a plurality of other cooperative work tasks. The main goal of the robot arm formation consistency is to design a distributed controller by utilizing the information of the neighbor robot arms, so that the whole robot arm system reaches a desired state protocol. At present, in the aspect of consistency of mechanical arm formation, a plurality of feasible control methods have been proposed by students, but the following problems still exist in the existing control methods:
first, existing multi-mechanical arm consistency control researches require relying on accurate mathematical models of mechanical arms, and have poor anti-interference capability. Under the condition that the mechanical arm dynamics system is interfered by the outside, the control effect can be greatly reduced, so that a certain difficulty exists in the process of achieving the formation consistency task for the multiple mechanical arms.
Secondly, in some existing multi-mechanical-arm consistency control schemes, the mechanical arm is often considered to act in an obstacle-free environment, but in an actual working scene, a large number of dynamic or static obstacles may exist in a field environment, the mechanical arm is used as a high-precision production tool, and the control effect of the mechanical arm is seriously affected by the existence of the obstacles.
Disclosure of Invention
The invention provides a mechanical arm safety cooperative control system based on a control obstacle function, which aims to overcome the technical problems.
In order to achieve the above object, the technical scheme of the present invention is as follows:
a robot arm safety cooperative control system based on a control obstacle function, comprising: the system comprises a plurality of mechanical arms, a distributed dynamic compensation module, a cooperative module, a self-adaptive tracking consistency control module and a safety optimization module;
the mechanical arm is used for acquiring mechanical arm measurement information based on a mechanical arm dynamics model, and the mechanical arm measurement information comprises an uncertain item caused by the position state of the mechanical arm, an uncertain item caused by a neighbor mechanical arm of the mechanical arm and external environment disturbance caused by an obstacle;
the cooperative module is used for acquiring cooperative measurement signals according to the mechanical arm measurement information;
the distributed dynamic compensation module is connected with the cooperative module and is used for acquiring a compensation signal according to the cooperative measurement signal;
the self-adaptive tracking consistency control module is used for acquiring control input information of the mechanical arm based on a consistency control protocol according to the compensation signal and mechanical arm measurement information, so as to realize tracking consistency formation control tasks of the multi-mechanical arm system;
the safety optimization module is respectively used for acquiring an optimized control input vector under the influence of the obstacle according to control input information of the mechanical arm based on the control obstacle function so as to realize safety cooperative control of the multi-mechanical-arm system.
Further, the acquisition of the optimal control input vector is as follows:
，
wherein:the obtained optimized control input vector;u i represents the firstiControl input information of the mechanical arm; />Is a nominal control input vector, i.e. a control input vector when only the underlying control task is implemented; />Representation mechanical armiMechanical armjIs the difference between the position vectors of (a); />Determining an inertia matrix for positive;z ij representing a robotic arm calculated by ISsf-CBFiMechanical armjIs a collision avoidance parameter of (2); />Representation mechanical armiAnd the position vector of the obstacle;z io representing a robotic arm calculated by ISsf-CBFiAnd collision avoidance parameters of the obstacle; />Representing the euclidean norm.
Further, the kinetic model of the mechanical arm is established as follows:，
in the method, in the process of the invention, Nthe total number of the mechanical arms in the cluster system;Erepresenting a collection of robotic arms; />Determining an inertia matrix for positive; />Represents the firstiThe position state of the mechanical arm; />Represents the firstiControl input information of the mechanical arm; />Represents the firstiThe external environment disturbance to the mechanical arm dynamic system of the frame, namely the uncertain parameters of the mechanical arm system, including the external disturbance and the uncertain items brought by the neighbor mechanical arms of the mechanical arm; />Is the firstiA matrix of coriolis force and centripetal force combinations of the gantry robotic arms; />Is the firstiGravitational potential energy vector of the mechanical arm of the frame; />Representing an n-dimensional euclidean space; />Representation->Is a second derivative of (2);
，
wherein:lis an intermediate variable;is a reference input signal, namely a tracking target of each mechanical arm;FandSare all system matrices;D i is a disturbance matrix;d i represents the firstiExternal environmental disturbance to the mechanical arm dynamics system;
linearizing the uncertain parameters of the mechanical arm system is as follows:，
wherein:is a vector composed of uncertain parameters of the mechanical arm system; />Is a known matrix determined by the parametric linearization properties of the robotic arm system;x 1 andx 2 is an arbitrary vector; />Represent the firstiMatrix synthesized by Coriolis force and centripetal force of the mechanical arm of the rack;
further, the cooperative measurement signal is obtained as follows:
，
wherein:is a cooperative measurement signal;inumbering the mechanical arm;jis a mechanical armiIs the number of the neighbor mechanical arm;Nthe total number of the mechanical arms in the cluster system;a ij representation mechanical armiAnd mechanical armjIs a coefficient of connectivity; />Is the firstiCompensating signal generated by dynamic compensator corresponding to mechanical arm of frame, < >>Is a mechanical armjThe compensation signal generated by the corresponding dynamic compensator.
Further, the compensation signal is obtained as follows:
，
in the method, in the process of the invention,is the firstiA compensation signal generated by a dynamic compensator corresponding to the mechanical arm;Fa system matrix that is a leader system; />Is the normal number coupling gain of the compensation module; />Is a cooperative measurement signal; />Is a mechanical armjA compensation signal generated by the corresponding dynamic compensator;Nthe total number of the mechanical arms in the cluster system;a ij representation mechanical armiAnd mechanical armjIs the coefficient of connectivity of the armiCan receive the mechanical armjInformation of (1)a ij 1, otherwise 0; />Is->Is a first derivative of (a).
Further, the consistency control protocol is as follows:
，
wherein,K i is a feedback gain matrix;H i is an adaptive gain matrix;s i is a tracking error signal;representation->Is a transpose of (2); />Representation->Is determined by the adaptive estimation of (1); />Representation ofH i An inverse matrix of (a); />Represents the firstiAnd the control of the mechanical arm of the rack inputs information.
The beneficial effects are that: the mechanical arm safety cooperative control system based on the control obstacle function can be applied to multiple mechanical arms and can realize cooperative formation control, and under the condition that a mechanical arm dynamic system is subjected to external interference caused by a complex environment, a consistent formation cooperative control task is realized, and the robustness and the anti-interference performance of a control strategy are improved. Meanwhile, when the proposed multi-mechanical-arm safety tracking consistency control protocol is executed, even if a multi-mechanical-arm system encounters a dynamic and static obstacle, formation consistency control tasks can be continuously completed outside safety collision prevention among mechanical arms and collision prevention on the obstacle. Through the design of the safety optimization module based on the control obstacle function, the safety formation control can be effectively realized when the system completes the bottom layer consistency control task, and the loss caused by collision in the use process of the mechanical arm is avoided. Meanwhile, the control structure of the modularized design of the invention obviously reduces the complexity of the design of the controller, reduces the calculation load, improves the flexibility of the design of the controller and is convenient for engineering realization.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic diagram of a mechanical arm safety cooperative control system based on a control obstacle function;
FIG. 2 is a schematic diagram of a communication topology of three robotic arms in an embodiment of the invention;
FIG. 3 is a schematic diagram of error in motion trajectory consistency of three robotic arms in an embodiment of the invention;
FIG. 4 is a schematic diagram of a distributed dynamic compensation error of three mechanical arms according to an embodiment of the present invention;
fig. 5 is a schematic view of three mechanical arms in the embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment provides a mechanical arm safety cooperative control system based on a control obstacle function, as shown in fig. 1, including: the system comprises a plurality of mechanical arms, a distributed dynamic compensation module, a cooperative module, a self-adaptive tracking consistency control module and a safety optimization module;
the mechanical arm is used for acquiring mechanical arm measurement information based on a mechanical arm dynamics model, and the mechanical arm measurement information comprises an uncertain item caused by the position state of the mechanical arm, an uncertain item caused by a neighbor mechanical arm of the mechanical arm and external environment disturbance caused by an obstacle;
preferably, in this embodiment, a robot arm cluster system composed of N robot arms is considered. The kinetic model of each mechanical arm is established as follows:
(1)
in the method, in the process of the invention,，Nthe total number of the mechanical arms in the cluster system;Erepresenting a collection of robotic arms;determining an inertia matrix for positive; />Represents the firstiThe position state of the mechanical arm; />Represents the firstiControl input information of the mechanical arm; />Represents the firstiThe external environment disturbance to the mechanical arm dynamic system of the frame, namely the uncertain parameters of the mechanical arm system, including the external disturbance and the uncertain items brought by the neighbor mechanical arms of the mechanical arm; />Is the firstiA matrix of coriolis force and centripetal force combinations of the gantry robotic arms; />Is the firstiGravitational potential energy vector of the mechanical arm of the frame; />Representing an n-dimensional euclidean space; />Representation->Is a second derivative of (2);
specifically, the invention considers that the multi-mechanical arm system has uncertain parameters which can be used for parameter linearization, and estimates the uncertain parameters through an adaptive law.
The reference input signal considered in this embodiment is generated by an external system, and is as follows:
（2）
wherein:lis an intermediate variable;is a reference input signal, namely a tracking target of each mechanical arm;FandSare all system matrices;D i is a disturbance matrix;d i represents the firstiExternal environmental disturbance to the mechanical arm dynamics system;
linearizing the uncertain parameters of the mechanical arm system is as follows:
（3）
wherein:is a vector composed of uncertain parameters of the mechanical arm system; />Is a known matrix determined by the parametric linearization properties of the robotic arm system;x 1 andx 2 is an arbitrary vector; />Represent the firstiMatrix synthesized by Coriolis force and centripetal force of the mechanical arm of the rack;
the input end of the cooperative module is connected with the mechanical arm through a communication network, and the output end of the cooperative module is connected with the cooperative module and is used for acquiring a cooperative measurement signal according to the mechanical arm measurement information;
preferably, the cooperative module is configured to receive the mechanical arm measurement information, so as to output a cooperative measurement signal. The collaboration module is connected with the communication network and the distributed dynamic compensation module, and information interaction between the mechanical arms in a communication state in the communication topology module structure is carried out through the communication network. The cooperative measurement signal is obtained as follows:
（4）
wherein:is a cooperative measurement signal;inumbering the mechanical arm;jis a mechanical armiIs the number of the neighbor mechanical arm;Nthe total number of the mechanical arms in the cluster system;a ij representation mechanical armiAnd mechanical armjIs a coefficient of connectivity; />Is the firstiCompensating signal generated by dynamic compensator corresponding to mechanical arm of frame, < >>Is a mechanical armjThe compensation signal generated by the corresponding dynamic compensator.
The distributed dynamic compensation module is connected with the cooperative module and is used for acquiring a compensation signal according to the cooperative measurement signal;
specifically, the distributed dynamic compensation module generates a compensation signal as an internal state variable of the adaptive tracking consistency controller through the obtained cooperative measurement signal, so that the control performance of the consistency controller is improved.
Preferably, the compensation signal is obtained as follows:
（5）
in the method, in the process of the invention,is the firstiA compensation signal generated by a dynamic compensator corresponding to the mechanical arm;Fa system matrix that is a leader system; />Is the normal number coupling gain of the compensation module; />Is a cooperative measurement signal; />Is a mechanical armjA compensation signal generated by the corresponding dynamic compensator;Nthe total number of the mechanical arms in the cluster system;a ij representation mechanical armiAnd mechanical armjIs the coefficient of connectivity of the armiCan receive the mechanical armjInformation of (1)a ij 1, otherwise 0; />Is->Is a first derivative of (a).
The input end of the self-adaptive tracking consistency control module is connected with the mechanical arm and the distributed dynamic compensation module respectively, and the output end of the self-adaptive tracking consistency control module is connected with the safety optimization module; and the control input information of the mechanical arm is acquired based on a consistency control protocol according to the compensation signal and the mechanical arm measurement information, so that a multi-mechanical arm system tracking consistency formation control task is realized.
The self-adaptive tracking consistency control module realizes a multi-mechanical arm system bottom layer control task, namely a multi-mechanical arm system tracking consistency formation control task, based on the dynamic compensation signal and mechanical arm measurement information.
Preferably, the consistency control protocol is:
（6）
wherein,K i is a feedback gain matrix;H i is an adaptive gain matrix;s i is a tracking error signal;representation->Is a transpose of (2); />Representation->Is determined by the adaptive estimation of (1); />Representation ofH i An inverse matrix of (a); />Represents the firstiAnd the control of the mechanical arm of the rack inputs information.
The control obstacle function safety optimization module is respectively connected with the self-adaptive tracking consistent control module and the mechanical arm, and is connected with the cooperative module through a communication network, and is used for acquiring an optimized control input vector under the influence of an obstacle based on the control obstacle function so as to realize the safety cooperative control of the multi-mechanical arm system.
Specifically, the control barrier function safety optimization module is connected with the communication network and the self-adaptive tracking consistent control module, the state information of the multi-mechanical arm system is obtained, the safety constraint of the system is obtained by constructing and inputting the state safety-control barrier function (ISSf-CBF) through a collision prevention mechanism, and the safety cooperative control of the multi-mechanical arm system is realized by setting a secondary planning problem.
Preferably, the control obstacle function safety optimization module is designed to obtain the optimized control input vector as follows:
（7）
wherein:the obtained optimized control input vector;u i represents the firstiControl input information of the mechanical arm; />Is a nominal control input vector, i.e. a control input vector when only the underlying control task is implemented; />Representation mechanical armiMechanical armjIs the difference between the position vectors of (a); />Determining an inertia matrix for positive;z ij representing a robotic arm calculated by ISsf-CBFiMechanical armjIs a collision avoidance parameter of (2); />Representation mechanical armiAnd the position vector of the obstacle;z io representing a robotic arm calculated by ISsf-CBFiAnd collision avoidance parameters of the obstacle; />Representing the euclidean norm.
The two safety constraints are obtained by using ISsf-CBF, and are respectively safety constraints for avoiding collision among mechanical arms and obstacle avoidance;is an abbreviation for subject to.
First, compared with the existing information interaction method of the mechanical arm by using the fixed topology control protocol, the communication network working in this embodiment only requires joint connection, which is a relatively mild requirement for network connectivity. Even in the case that there is no communication link between the mechanical arms for a while, the safety formation control can be realized.
Secondly, the embodiment can realize the consistency formation cooperative control task under the condition that the mechanical arm dynamic system is subjected to external interference caused by a complex environment, and the robustness and the anti-interference performance of a control strategy are improved.
Third, compared with the existing cooperative control strategy of a plurality of mechanical arms, when the multi-mechanical arm safety tracking consistency control protocol provided by the embodiment is executed, even if a multi-mechanical arm system encounters a dynamic and static obstacle, the formation consistency control task can be continuously completed outside the safety collision prevention among mechanical arms and the collision prevention to the obstacle.
Fourth, in summary, the present embodiment is applied to multiple mechanical arms and can realize cooperative formation control. Through the design of the safety optimization mechanism based on the control barrier function, the safety formation control can be effectively realized when the system completes the bottom layer consistency control task, and the loss caused by collision of the mechanical arm in the use process is avoided. More importantly, the complexity of the controller design is remarkably reduced, the calculation load is reduced, the flexibility of the controller design is improved, and the engineering realization is facilitated through the control structure of the modularized design.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Fig. 1 is a schematic structural diagram of a mechanical arm safety cooperative control system based on a control obstacle function according to the present invention, as shown in fig. 1, the embodiment may include:
in the embodiment, a multi-mechanical arm formation cluster system formed by three mechanical arms is taken as an example, and the scheme in the embodiment is further described. Further, the motion model of the mechanical arm according to the embodiment is derived from the work in Robot Manipulator Control: theory and Practice, as follows:
，
wherein,；p i1 representation mechanical armiIs a first dimension element of a position vector of (a);p i2 representation mechanical armiIs a second dimensional element of the position vector of (a); />Representing the presentation to bep i1 Andp i2 merging into a new column vector;
wherein,representing a vector of unknown parameters of the system;are all unknown parameters of the system.
The communication topology between the multiple robotic arms is shown in fig. 2. The switching signal is as follows:
in the method, in the process of the invention,a signal representing a system communication topology switch;ais a positive constant; />Representing a switching period;trepresenting time; wherein (1)>=0.1s. The position error vectors of the three mechanical arms in the system formation are respectively as follows:
further, specific parameters designed in this embodiment are as follows:
wherein,p 0 (0) Is the initial position vector of the obstacle,q 0 (0) Is the initial velocity variable of the obstacle, the obstacle in the simulation embodiment of the invention is set as a circular obstacle,is the radius of the obstruction.
Further, the simulation results of this case are shown in fig. 3-5: fig. 3 is a schematic diagram of error in motion trajectory consistency of three mechanical arms. As can be seen from fig. 3, under the action of external disturbance, three mechanical arms can realize consistent formation to complete cooperative control tasks. Fig. 4 is a schematic diagram of distributed dynamic compensation errors of three mechanical arms, wherein the distributed dynamic compensation errors of the three mechanical arms all tend to be 0. Fig. 5 is a schematic view of three mechanical arms for avoiding collision and avoiding obstacle, and it can be seen that the three mechanical arms can realize collision and avoiding obstacle and realize safety control.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (5)
1. Mechanical arm safety cooperative control system based on control obstacle function, characterized by comprising: the system comprises a plurality of mechanical arms, a distributed dynamic compensation module, a cooperative module, a self-adaptive tracking consistency control module and a safety optimization module;
the mechanical arm is used for acquiring mechanical arm measurement information based on a mechanical arm dynamics model, and the mechanical arm measurement information comprises an uncertain item caused by the position state of the mechanical arm, an uncertain item caused by a neighbor mechanical arm of the mechanical arm and external environment disturbance caused by an obstacle;
the kinetic model of the mechanical arm is established as follows:，
in the method, in the process of the invention,，
Nthe total number of the mechanical arms in the cluster system;
Erepresenting a collection of robotic arms;
determining an inertia matrix for positive;
represents the firstiThe position state of the mechanical arm;
represents the firstiControl input information of the mechanical arm;
represents the firstiThe external environment disturbance to the mechanical arm dynamic system of the frame, namely the uncertain parameters of the mechanical arm system, including the external disturbance and the uncertain items brought by the neighbor mechanical arms of the mechanical arm;
is the firstiCoriolis force and centripetal force combination of a gantry robotA matrix formed;
is the firstiGravitational potential energy vector of the mechanical arm of the frame;
representation ofnA wieuclidean space;
representation->Is a second derivative of (2);
，
wherein:lis an intermediate variable;is a reference input signal, namely a tracking target of each mechanical arm;FandSare all system matrices;D i is a disturbance matrix;d i represents the firstiExternal environmental disturbance to the mechanical arm dynamics system;
linearizing the uncertain parameters of the mechanical arm system is as follows:，
wherein:is a vector composed of uncertain parameters of the mechanical arm system; />Is due to the parameter linearization property of the mechanical arm systemA determined known matrix;x 1 andx 2 is an arbitrary vector; />Represent the firstiMatrix synthesized by Coriolis force and centripetal force of the mechanical arm of the rack;
the cooperative module is used for acquiring cooperative measurement signals according to the mechanical arm measurement information;
the distributed dynamic compensation module is connected with the cooperative module and is used for acquiring a compensation signal according to the cooperative measurement signal;
the self-adaptive tracking consistency control module is used for acquiring control input information of the mechanical arm based on a consistency control protocol according to the compensation signal and mechanical arm measurement information, so as to realize tracking consistency formation control tasks of the multi-mechanical arm system;
the safety optimization module is respectively used for acquiring an optimized control input vector under the influence of the obstacle according to control input information of the mechanical arm based on the control obstacle function so as to realize safety cooperative control of the multi-mechanical-arm system.
2. The robot safety cooperative control system based on the control obstacle function according to claim 1, wherein the acquisition of the optimized control input vector is as follows:
，
wherein:the obtained optimized control input vector;u i represents the firstiControl input information of the mechanical arm; />Is a nominal control input vector, i.e. a control input vector when only the underlying control task is implemented; />Representation mechanical armiMechanical armjIs the difference between the position vectors of (a); />Determining an inertia matrix for positive;z ij representing a robotic arm calculated by ISsf-CBFiMechanical armjIs a collision avoidance parameter of (2); />Representation mechanical armiAnd the position vector of the obstacle;z io representing a robotic arm calculated by ISsf-CBFiAnd collision avoidance parameters of the obstacle; />Representing the euclidean norm.
3. The robot safety cooperative control system based on a control obstacle function according to claim 1, wherein the cooperative measurement signal is obtained as follows:，
wherein:is a cooperative measurement signal;inumbering the mechanical arm;jis a mechanical armiIs the number of the neighbor mechanical arm;Nthe total number of the mechanical arms in the cluster system;a ij representation mechanical armiAnd mechanical armjIs a coefficient of connectivity; />Is the firstiCompensating signal generated by dynamic compensator corresponding to mechanical arm of frame, < >>Is a mechanical armjThe compensation signal generated by the corresponding dynamic compensator.
4. The robot safety cooperative control system based on the control obstacle function according to claim 1, wherein the compensation signal is obtained as follows:
，
in the method, in the process of the invention,is the firstiA compensation signal generated by a dynamic compensator corresponding to the mechanical arm;Fa system matrix that is a leader system; />Is the normal number coupling gain of the compensation module; />Is a cooperative measurement signal; />Is a mechanical armjA compensation signal generated by the corresponding dynamic compensator;Nthe total number of the mechanical arms in the cluster system;a ij representation mechanical armiAnd mechanical armjIs the coefficient of connectivity of the armiCan receive the mechanical armjInformation of (1)a ij 1, otherwise 0; />Is->Is a first derivative of (a).
5. The robot safety cooperative control system based on a control obstacle function according to claim 1, wherein the consistency control protocol is:
，
wherein,K i is a feedback gain matrix;H i is an adaptive gain matrix;s i is a tracking error signal;representation ofIs a transpose of (2); />Representation->Is determined by the adaptive estimation of (1); />Representation ofH i An inverse matrix of (a); />Represents the firstiAnd the control of the mechanical arm of the rack inputs information.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114815823A (en) * | 2022-04-15 | 2022-07-29 | 大连海事大学 | Unmanned ship cluster safety target tracking system based on control barrier function |
| CN115032986A (en) * | 2022-05-13 | 2022-09-09 | 大连海事大学 | Multi-unmanned fleet shape reconstruction system based on vector field guidance and fixed time CBF |
| CN115256395A (en) * | 2022-08-17 | 2022-11-01 | 北京理工大学 | Model uncertain robot safety control method based on control barrier function |
| CN115685755A (en) * | 2022-10-27 | 2023-02-03 | 浙江工业大学 | Man-machine cooperation control method based on discrete control barrier function |
| US20230064332A1 (en) * | 2021-08-31 | 2023-03-02 | Siemens Aktiengesellschaft | Controller for autonomous agents using reinforcement learning with control barrier functions to overcome inaccurate safety region |
| CN115857483A (en) * | 2022-06-13 | 2023-03-28 | 江苏优思微智能科技有限公司 | Unmanned ship obstacle avoidance control method based on control obstacle function model predictive control algorithm |
| CN116276966A (en) * | 2023-01-09 | 2023-06-23 | 戴盟(深圳)机器人科技有限公司 | Mobile operation robot whole body reaction planning control method based on quadratic programming |
| CN116360420A (en) * | 2023-01-09 | 2023-06-30 | 中国矿业大学 | CLF-CBF optimized unmanned vehicle motion planning control method |
| US20230321831A1 (en) * | 2022-04-07 | 2023-10-12 | California Institute Of Technology | Systems and Methods for Model-free Safety Control in Robotics Platforms |
-
2024
- 2024-01-16 CN CN202410059757.1A patent/CN117572810B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230064332A1 (en) * | 2021-08-31 | 2023-03-02 | Siemens Aktiengesellschaft | Controller for autonomous agents using reinforcement learning with control barrier functions to overcome inaccurate safety region |
| US20230321831A1 (en) * | 2022-04-07 | 2023-10-12 | California Institute Of Technology | Systems and Methods for Model-free Safety Control in Robotics Platforms |
| CN114815823A (en) * | 2022-04-15 | 2022-07-29 | 大连海事大学 | Unmanned ship cluster safety target tracking system based on control barrier function |
| CN115032986A (en) * | 2022-05-13 | 2022-09-09 | 大连海事大学 | Multi-unmanned fleet shape reconstruction system based on vector field guidance and fixed time CBF |
| CN115857483A (en) * | 2022-06-13 | 2023-03-28 | 江苏优思微智能科技有限公司 | Unmanned ship obstacle avoidance control method based on control obstacle function model predictive control algorithm |
| CN115256395A (en) * | 2022-08-17 | 2022-11-01 | 北京理工大学 | Model uncertain robot safety control method based on control barrier function |
| CN115685755A (en) * | 2022-10-27 | 2023-02-03 | 浙江工业大学 | Man-machine cooperation control method based on discrete control barrier function |
| CN116276966A (en) * | 2023-01-09 | 2023-06-23 | 戴盟(深圳)机器人科技有限公司 | Mobile operation robot whole body reaction planning control method based on quadratic programming |
| CN116360420A (en) * | 2023-01-09 | 2023-06-30 | 中国矿业大学 | CLF-CBF optimized unmanned vehicle motion planning control method |
Non-Patent Citations (1)
| Title |
|---|
| 姚绍杰, 闫帅明, 张浩等: ""无人机系统安全控制研究综述:控制障碍函数"", 控制与决策, 9 October 2023 (2023-10-09) * |
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