Abstract
This paper proposes an adaptive formation tracking control algorithm optimized by Q‐learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q‐learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed‐loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady‐state performance.
Highlights
A multi-mobile robot system can present intelligent behaviours through mutual cooperation and achieve work efficiency and fault tolerance that a single individual cannot provide, so that it can complete some more difficult tasks.e coordinated formation control of multiple mobile robots has received extensive attention due to its important applications in the industrial and medical field [1]. e most existing control methods dealing with formation control problems of multiple mobile robots mainly include behaviour-based control [2], virtual structures [3], and leader-follower architecture [4,5,6]
Inspired by the above statements, this paper investigates the formation tracking control of a multiple omnidirectional mobile robot system. e considered mobile robots have internal modelling uncertainties and external disturbances
In order to achieve better formation tracking control performance, the Linear ESO (LESO)-sliding mode control (SMC) scheme will be designed for system (3) in the presence of the unknown disturbances and model uncertainties, such that all follower omnidirectional mobile robot (OMR) can track the virtual leader with the given formation configuration in advance and maintain the same speed with the virtual leader
Summary
A multi-mobile robot system can present intelligent behaviours through mutual cooperation and achieve work efficiency and fault tolerance that a single individual cannot provide, so that it can complete some more difficult tasks. Considering the bounded external disturbance and parameter uncertainty of mobile robots, reference [16] proposed a dual-loop attitude tracking robust controller for mobile robots, using SMC with modified arrival law to ensure that the actual speed converges within a finite time. Reference [24] employed a nonlinear extended state observer (NESO) to estimate unknown states as well as uncertainties and designed a robust finite-time tracking control scheme to handle wheeled mobile robots with parameter uncertainties and disturbances. (1) An LESO is constructed to estimate the ‘total disturbances’ in real time, including both internal parameter uncertainties and external disturbances, and an LESO-SMC based formation protocol is developed for the OMR system. (2) To take full advantages of the LESO-SMC, an adaptive method of LESO-SMC parameters optimized by Q-learning algorithm is proposed in the formation tracking control of the OMR system. In denotes an n × n unit matrix. 0n denotes an n × n zero matrix. ⊗ stands for Kronecker product. sign(·) is the sign function. ‖ · ‖ represents the Euclidean norm. diagx, x2, . . . , xn denotes the diagonal matrix with its diagonal entries being x1, x2, . . . , xn. λmax(A) and λmin(A) represent the maximum and the minimum eigenvalues of matrix A, respectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
