In this paper, an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation. A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external disturbances. To guarantee the prescribed performance, an error transformation is applied to transfer the time-varying constraints into a constant constraint. Then, by employing a barrier Lyapunov function and the backstepping technique, an observer-based tracking control strategy is presented. It is proven that using the proposed algorithm, all the closed-loop signals are bounded, and the tracking errors satisfy the predefined time-varying performance requirements. Finally, simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

在本文中，针对一类具有或不具有输入饱和的不确定多输入多输出非线性系统，开发了一种基于观测器的自适应指定性能跟踪控制方案。构建了一种新的有限时间神经网络扰动观测器来估计系统的不确定性和外部扰动。为了保证规定的性能，应用误差变换将时变约束转换为常数约束。然后，通过使用障碍Lyapunov 函数和backstepping 技术，提出了一种基于观察者的跟踪控制策略。证明使用所提出的算法，所有闭环信号都是有界的，跟踪误差满足预定义的时变性能要求。最后，