Min Huifang, Xu Shengyuan, Fei Shumin, Yu Xin
IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4322-4331. doi: 10.1109/TNNLS.2021.3056524. Epub 2022 Aug 31.
For full-state constrained nonlinear systems with input saturation, this article studies the output-feedback tracking control under the condition that the states and external disturbances are both unmeasurable. A novel composite observer consisting of state observer and disturbance observer is designed to deal with the unmeasurable states and disturbances simultaneously. Distinct from the related literature, an auxiliary system with approximate coordinate transformation is used to attenuate the effects generated by input saturation. Then, using radial basis function neural networks (RBF NNs) and the barrier Lyapunov function (BLF), an opportune backstepping design procedure is given with employing the dynamic surface control (DSC) to avoid the problem of "explosion of complexity." Based on the given design procedure, an output-feedback controller is constructed and guarantees all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. It is shown that the tracking error is regulated by the saturated input error and design parameters without the violation of the state constraints. Finally, a simulation example of a robot arm is given to demonstrate the effectiveness of the proposed controller.
针对具有输入饱和的全状态约束非线性系统，本文研究了在状态和外部干扰均不可测条件下的输出反馈跟踪控制问题。设计了一种由状态观测器和干扰观测器组成的新型复合观测器，以同时处理不可测的状态和干扰。与相关文献不同的是，采用具有近似坐标变换的辅助系统来减弱输入饱和产生的影响。然后，利用径向基函数神经网络（RBF NNs）和障碍Lyapunov函数（BLF），给出了一种合适的反步设计方法，并采用动态表面控制（DSC）来避免“复杂性爆炸”问题。基于所给出的设计方法，构造了一个输出反馈控制器，保证闭环系统中的所有信号都是半全局一致最终有界的。结果表明，跟踪误差由饱和输入误差和设计参数调节，且不违反状态约束。最后，给出了一个机器人手臂的仿真例子，以证明所提出控制器的有效性。
