A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints. The fuzzy logic system is used to design the approximator, which deals with uncertain and continuous functions in the process of backstepping design. The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint, but also mixes the states and errors to directly constrain the state, reducing the conservativeness of the constraint satisfaction condition. Considering that the states in most nonlinear systems are immeasurable, a fuzzy adaptive states observer is constructed to estimate the unknown states. Combined with adaptive backstepping technique, an adaptive fuzzy output feedback control method is proposed. The proposed control method ensures that all signals in the closed-loop system are bounded, and that the tracking error converges to a bounded tight set without violating the full state constraint. The simulation results prove the effectiveness of the proposed control scheme.

中文翻译：

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具有整体状态约束的非线性系统中基于积分神经网络Lyapunov函数的基于观测器的自适应模糊跟踪控制

针对一类不可估量，完全约束的严格反馈非线性系统，提出了一种新的模糊自适应控制方法。模糊逻辑系统用于设计逼近器，它在反推设计过程中处理不确定和连续的函数。积分势垒Lyapunov函数的使用不仅确保所有状态都在约束范围内，而且将状态和错误混合在一起以直接约束状态，从而降低了约束满足条件的保守性。考虑到大多数非线性系统中的状态是无法测量的，构造了模糊自适应状态观察器以估计未知状态。结合自适应反推技术，提出了一种自适应模糊输出反馈控制方法。所提出的控制方法确保了闭环系统中的所有信号均是有界的，并且跟踪误差收敛到有界的紧集而不会违反完整状态约束。仿真结果证明了所提控制方案的有效性。