PDE-based boundary event-triggered control of uncertain flexible manipulator with input nonlinearities

This study focuses on a novel event-triggered boundary control design of an uncertain flexible manipulator subject to input constraints and external disturbances. To this end, a novel boundary control law is developed based on the Lyapunov stability theory to suppress elastic deflection and regulate the manipulator to track the desired angular position. Moreover, to approximate the unknown functions and compensate for the effect of input constraints, the neural network technique is adopted with an arbitrarily adjustable approximating error. Simultaneously, the event-triggering mechanism is incorporated with a controller design to alleviate the communication load and the execution rate of the actuator. Lastly, numerical simulations are performed to demonstrate the effectiveness of the derived scheme.