الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Adaptive control for high performance drive systems has recently become an important subject of research. In applications such as robotics, aircraft, the rotor of the electric motor should follow a pre-selected track at all time. Parameter uncertainties, unknown load variation, or sudden external disturbances should not affect the tracking accuracy. A neural control technique is proposed for identification and control of electric motors. The main feature of the proposed controller is a neural network, which performs two functions. The first is to identify the nonlinear system dynamics at all times. Hence, detailed and elaborated models for the electric motors are not needed. Furthermore, unknown nonlinear dynamics which are difficult to model such as load disturbances, system noise and parameter variation can be recognized by the trained neural network. The second function of the neural network is to control the motor voltage, so that the speed and position are made to follow pre-selected tracks (trajectories) at all times. The control action emulated by the neural network is based on the indirect model reference adaptive control. Performances of the identification and control algorithms are evaluated by simulating them on a typical DC motor model and permanent magnet synchronous (PMSM) model. KEYWORDS: Trajectory Control, System Identification, Artificial Neural Networks, DC Motors, and Permanent Magnet Synchronous Motors.