Adaptive finite-time event-triggered control of marine surface vehicles with prescribed performance and output constraints

Abstract

This paper investigates the safety guaranteed formation control problem for marine surface vehicles (MSVs) with unknown environment disturbances, model uncertainties and output constraints. Initially, by employing the prescribed performance method to ensure that the position tracking errors are converged to the specified range. Then, a new barrier Lyapunov function (BLF) is incorporated into the formation control algorithm to guarantee that the states satisfy the constraints. Furthermore, by using an adaptive radial basis function neural network (RBFNN) to approximate the unknown nonlinear function, an adaptive RBFNN finite-time output constrained formation control law is proposed based on the backstepping design method. On this basis, in order to greatly reduce the update rate of the controller and mechanical loss of the actuator, the relative threshold event-triggered mechanism is introduced into the design of formation controller, the formation controller will be updated if and only if event-triggered conditions are satisfied. Subsequently, the stability analysis demonstrates that all error signals of the closed-loop system can converge into a small neighborhood around zero in finite time under the proposed method, meanwhile the prescribed bounds on the tracking errors will never be violated. And, it is proved by theory that the Zeno behavior can be avoided. Finally, numerical simulation results illustrate the effectiveness of the proposed formation controller.