NN-based adaptive event-triggered predefined time control of flexible joint robot with full-state error constraints

For flexible joint robot (FJR) system with full-state error constraints, this paper proposes an event-triggered adaptive predefined time control method. This study employs a radial basis function neural networks (RBFNN) to handle unknown nonlinear dynamic functions. In addition, it makes use of the universal barrier Lyapunov function to ensure that both the full-state errors and tracking errors converge to a user-specified range of control accuracy. Based on the event-triggered control strategy, a communication limitation control scheme is established to reduce the execution time of the controller and save communication resources. Combined with the DSC technique, the differential explosion problem is avoided by utilizing the constructed predefined time filters. By using the proposed predefined time stability criterion and Lyapunov function, it has been demonstrated that the controlled system achieves practical predefined time stable (PPTS) and all control signals bounded within the predefined time interval. Simulation results are utilized to verify the predefined time control performance.