Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems With State Constraints
针对一类具有未知动态和状态约束的多输入多输出非线性系统，提出一种自适应模糊神经网络控制方案，通过积分李雅普诺夫函数处理状态约束，并设计基于神经网络的观测器估计不可测状态，仿真验证了有效性。
In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multipleinput and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neuralnetwork-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov's stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.