Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

Abstract

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. Firstly, a FNN learning algorithm is developed to identify the unknown plant model. Secondly, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Thirdly, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, Integral Barrier Lyapunov function (IBLF) is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, Some simulation examples are carried out to verify the effectiveness of the designed control.

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“…[4][5][6][7][8][9] Since the ELSs usually work in complex environment, the unknown parameters and disturbances can not be ignored, so how to overcome the influence brought by uncertain dynamics and disturbances such that the desired consensus can be guaranteed is a great challenge, and many control schemes have been proposed to achieve this objective. [10][11][12] He et al 11 considered the iterative learning control of ELS with distributed disturbances, and 12 studied the adaptive fuzzy control of multiple ELSs with constraint using impedance learning. Owing to the advantages of backstepping and adaptive control for nonlinear systems and uncertain systems, 13 their reasonable combination can effectively solve the coordination control problem for multiple ELSs with uncertainties and disturbances.…”

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