Constrained Sampled-Data ARC for a Class of Cascaded Nonlinear Systems With Applications to Motor-Servo Systems

作者：Weichao Sun, Yanbin Liu, Huijun Gao · 发表于：IEEE Transactions on Industrial Informatics · 年份：2018 · DOI：10.1109/tii.2018.2821677 · 被引用次数：121 · 研究领域：Adaptive Control of Nonlinear Systems、Advanced Control Systems Optimization、Control Systems and Identification

In this paper, sampled-data adaptive robust control is proposed for a class of uncertain cascaded nonlinear system with states and inputs constraints. The systematic design procedure can be divided into two steps: i) design a sampled-data adaptive robust controller for the plant to not only stabilize the closed-loop system but also track the desired command although there are a variety of uncertainties and disturbances in the system; ii) design a reference governor for the control system to avoid the states and inputs violating their limits. Finally, the proposed method is employed in Motor-servo system to demonstrate the effectiveness.