Distributed Neural Network Fixed-Time Consensus Control for Multiple Manipulators System with Input Deadzone

This is the authors' abstract. We don't add key points for this paper.

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 presented for indefinite MMS with input deadzone under a directed communication graph. To solve the problem of the network congestion exists in MMS, an event-triggered mechanism (ETM) is proposed to decrease the upgrade rates of output control signal. Furthermore, a RBFNNs is presented to estimate uncertain dynamics for controller compensation. Moreover, by applying backstepping method, a fixedtime controller is developed to ensure that the state errors can convergence to zero point in finite time not relevant to system initial state. Ultimately, the designed control strategy is validated by adequate simulation and experimental results.