Adaptive Fixed‐Time Trajectory Tracking Control for Underactuated Hovercraft with Prescribed Performance in the Presence of Model Uncertainties

Abstract

This paper develops an adaptive fixed-time trajectory tracking controller of an underactuated hovercraft with a prescribed performance in the presence of model uncertainties and unknown time-varying environment disturbances. It is the first time that the proposed method is applied to the motion control of the hovercraft. To begin with, based on the hovercraft's four degrees of freedom (DOF) model, the virtual control laws are designed using an error transforming function and the fixed-time stability theory to guarantee that the position tracking errors are constrained within the prescribed convergence rates and minimum overshoot. In addition, by combining the Lyapunov direct method and the adaptive radial basis function neural network (ARBFNN), the actual control laws are designed to ensure that the velocity tracking errors converge to a small region containing zero while handling model uncertainties and external disturbances effectively. Finally, all tracking errors of the closed-loop system are uniformly ultimately bounded and fixed-time convergent. Results from a comparative simulation study verify the effectiveness and advantage of the proposed method.

Global Prescribed Performance-Based Fault-Tolerant Control for Trajectory Tracking of the ACV With Uncertainty

“…The control system combines PPC method and a disturbance observer (DO) to improve the control performance of the system. Based on a prescribed performance function, an adaptive fixed-time Trajectory tracking controller of the ACV with model uncertainties and environment disturbances is proposed by [13]. In [14], an adaptive fault-tolerant controller with a monitoring function is proposed for nonlinear systems with uncertain parameters and actuator failures, applying the PPC to guarantee the transient steadystate performance of the system.…”

“…The difference is that the solution in this paper is more general. [3] [21] used a special error transformation function such that the initial value of the performance boundary is infinity; But this transformation function is too special and lacks generality; 2) Compared with the traditional PPFs designed in [10][11][12][13][14][15], a new PPC method with time-varying bounds that is not constrained by the initial conditions is proposed in this paper. Transformation errors do not require normalization as [3], let alone special transformation functions.…”