IEEE Trans Cybern. 2016 Feb;46(2):524-34. doi: 10.1109/TCYB.2015.2405616. Epub 2015 Mar 3.
In this paper, a fuzzy adaptive approach for stochastic strict-feedback nonlinear systems with quantized input signal is developed. Compared with the existing research on quantized input problem, the existing works focus on quantized stabilization, while this paper considers the quantized tracking problem, which recovers stabilization as a special case. In addition, uncertain nonlinearity and the unknown stochastic disturbances are simultaneously considered in the quantized feedback control systems. By putting forward a new nonlinear decomposition of the quantized input, the relationship between the control signal and the quantized signal is established, as a result, the major technique difficulty arising from the piece-wise quantized input is overcome. Based on fuzzy logic systems' universal approximation capability, a novel fuzzy adaptive tracking controller is constructed via backstepping technique. The proposed controller guarantees that the tracking error converges to a neighborhood of the origin in the sense of probability and all the signals in the closed-loop system remain bounded in probability. Finally, an example illustrates the effectiveness of the proposed control approach.
本文针对具有量化输入信号的随机严格反馈非线性系统，提出了一种模糊自适应方法。与现有的量化输入问题研究相比，现有工作侧重于量化稳定化，而本文则考虑了量化跟踪问题，将稳定化作为一个特例来恢复。此外，在量化反馈控制系统中同时考虑了不确定的非线性和未知的随机干扰。通过提出一种新的量化输入的非线性分解，建立了控制信号与量化信号之间的关系，从而克服了由分段量化输入引起的主要技术难点。基于模糊逻辑系统的通用逼近能力，通过反推技术构建了一种新的模糊自适应跟踪控制器。所提出的控制器保证了跟踪误差在概率意义上收敛到原点的一个邻域内，并且闭环系统中的所有信号都在概率意义上保持有界。最后，通过一个实例说明了所提出的控制方法的有效性。
