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Nonlinear Adaptive Control Of Time-varying Uncertain Electro-mechanical Motion System

Posted on:2009-06-03Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y Y LiangFull Text:PDF
GTID:1118360242495824Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Electro-mechanical motion system is one of the industrial systems most frequently used. As the development of social production, electro-mechanical motion system is becoming more and more complex and appers complicated nonlinear phenomenon such as dead-zone, stick-slip and velocity dependecne, it is also time varying because of the mechanical wear, inertia variation and temperature and humid changing in the environment during the operating. Therefore, it is difficult to describe the precise model and unmodeling uncertainty by common mathematical modeling methods. However, super precise machine tools, industrial robots and semiconductor equipments demand for higher velocity and precision in practical industrial production. It is demonstrated that motion system with high velocity and high precision needs not only ingenious mechanical design but also high performance motion controller.Adaptive technology is a unique control method arising from the practical engineering application, which can learn the system uncertainty automatically, and new application demands promote a new round of development of this technology. But, there are still a lot of unsolved problems in adaptive control theory for time varying electro-mechanical motion system, and continue hard work is needed. Therefore, this dissertation studied the design of adaptive controller for time varying uncertain electro-mechanical motion system, and then applied the proposed controller to the practical electro-mechanical motion control systems. The main contents include the following four parts.1) Based on advantages of simple structure and good property in PID and self-regulation and adaptive of neural networks, a multivariable adaptive PID-like neural network controller (PIDNNC) was proposed for single input multi output nonlinear uncertain system. By means of defining error function as the design objective, using resilient back-propagation algorithm with sign instead of the gradient to deal with some differential relations, the correcting formation of on-line updating network was obtained. Lyapunov stability theorem was used to derive the values of learning rate to insure close-loop control system stability. Finally, the proposed PIDNNC was applied to the stabilizing of actual inverted pendulum, and then the experimental result was compared with that of LQR. Besides, problem of how to obtain the optimal initial weights of PIDNNC with LQR was also discussed in detail.2) The PIDNNC was extended to a neural network nonlinear adaptive controller with PID property (NLPIDC). Firstly, structure of the controller and on-line update law of weights was introduced, and then stability of the close-loop control system was analyzed with discrete Lyapunov direct method, and the range of learning rate was obtained which guaranteed the stability of the close-loop control system. Finally, the proposed NLPIDC was used to control the triple inverted pendulum in the nonlinear dynamic simulation software system constructed by virtual prototype software ADAMS and MATLAB.3) Based on function approximation technique and sliding mode control principle, a novel adaptive controller for nonlinear time varying uncertain upper triangular system (FASMAC) was proposed. The bound unknown nonlinear uncertainty was transformed into the product of an unknown time invariant coefficient vector and a known time varying series vector, and then on-line approximation of the uncertainty and adaptive compensation of the approximation error were derived by Lyapunov direct method. Finally, actual experiment on the direct current motor position tracking was conduced with the proposed FASMAC.4) Introducing an additional performance about square sum of error into the controller design, the FASMAC for the time varying uncertain upper triangular system was extended to an adaptive controller SIMOAC for general single input multi output system. Finally, the proposed SIMOAC was applied to the position tracking control on actual direct current motor system and the stabilizing of triple inverted pendulum, the experimental result and simulation result were also compared with other methods.
Keywords/Search Tags:Time varying uncertainty, Electro-mechanical motion control system, Nonlinear adaptive control, PID neural network, Sliding mode control, Function approximation technique
PDF Full Text Request
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