Distributed Finite-Time Fuzzy Adaptive Consensus Control for Robot Manipulators With Input Deadzone and Model Uncertainties

The multiple manipulator system (MMS) has strong coupling properties and nonlinearities, which is used to accomplish complex cooperation tasks. In this study, a distributed consensus control algorithm is proposed for uncertain MMS with input deadzone under a directed communication graph. Meanwhile, a fuzzy logic system (FLS) is designed to approximate uncertain dynamics for controller compensation. A fast finite-time convergence backstepping controller is designed to ensure that the state error of the MMS system converges to the zero neighborhood within a finite time. In addition, an adaptive method is used to estimate and compensate for the unknown input dead zone parameters. Finally, the effectiveness of the control method is verified through the simulation model and experimental platform, and its advantages are verified through comparative experiments.