Chang Yeong-Chan
Department of Electrical Engineering, Kun-Shan University, Tainan 71003, Taiwan.
IEEE Trans Syst Man Cybern B Cybern. 2009 Feb;39(1):142-55. doi: 10.1109/TSMCB.2008.2002854.
This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.
本文研究了一大类包含对象不确定性和外部干扰的严格反馈非线性系统的鲁棒跟踪控制问题。输入和虚拟输入加权矩阵受到有界时变不确定性的扰动。将开发一种基于自适应模糊（或基于神经网络）的动态反馈跟踪控制器，以使闭环系统的所有状态和信号都有界，并且轨迹跟踪误差应尽可能小。首先，设计具有线性参数化模型的自适应逼近器，并针对所开发的自适应逼近器提出一种划分过程，使得模糊（或神经网络）基函数的实现仅取决于状态变量，而不取决于调整逼近参数。此外，我们扩展设计了非线性参数化自适应逼近器。因此，本文开发的智能鲁棒跟踪控制方案具有计算简单和易于实现的特性。最后，给出了仿真示例以证明所提出控制算法的有效性。
