Prescribed-Time Cooperative Control of Multilateral Teleoperation Systems: A Novel Composite Fuzzy Learning-Based Approach

Abstract

In this article, a novel prescribed-time composite learning-enhanced fuzzy (PrTCLF) cooperative control approach is proposed for the multiple-leadermultiple-follower (MLMF) teleoperation systems in the presence of system model uncertainties and external interferences. First, compared with traditional MLMF systems, a unifying virtual leader–follower teleoperation control framework, notably applicative for more general situations where the number of leaderand follower robots is not the same, is constructed by introducing scheduled control authority for different operators, especially in cooperative control mode. A significant feature of this article is that the first result of a novel time-dependent function integrated PrTCLF learning law rendering all the synchronization errors of the uncertain MLMF teleoperation system to zero is creatively derived, by which the effect of the traditional fuzzy learning error on the precision of system convergence is essentially solved. Meanwhile, in order to ensure the high efficiency and robustness of cooperative work, a new class of nonsingular prescribed-time terminal sliding mode surface is designed without any switching behavior. Besides, the sufficient conditions for maintaining the prescribed-time stability of the MLMF teleoperation system are provided through systematic Lyapunov stability analysis. Finally, the effectiveness of the control structure and algorithm is verified by a large number of simulation and experimental results.