Nonparametric Model Learning Adaptive Control Method of DC Motor
Nonparametric model learning adaptive control method (NMLAC) presented in this paper is based on new concepts called pseudo-partial-derivatives (PPD) for a class of nonlinear systems. No structural information, no mathematical model, no training process and no external testing signals are needed. The unmodelled dynamics do not exist. In this paper, nonparametric model learning adaptive control (NMLAC) approach of a class of SISO nonlinear discrete-time systems based on linearization of tight format is applied to DC motor rotate speed control. The design of controller is model-free, based directly on pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion model using novel parameter estimation algorithms. Simulation experiment examples are provided for real nonlinear systems, which are known to be difficult to model, and control to demonstrate the correctness, effectiveness and advantages of the approaches proposed.
computer simulation DC motor NMLAC nonlinear systems and stability
Cao Rongmin Hou Zhongsheng Bai Lianping
School of Automation, Beijing Information Science & Technology University, Beijing 100192, China School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, Chin
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
1779-1782
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）