| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系 === 90 === A approach for on-line adaptive neural control of MIMO nonlinear systems is explored in this thesis with sliding gain using diagonal recurrent neural network (DRNN) learning model. We use the nonlinear state feedback to design the control input of a nonlinear system containing several unknown parameters of nonlinear systems. These parameters are estimated by diagonal recurrent neural networks with on-line modeling. The resulting adaptive learning law, which is designed by sliding mode update, can adjust the weights of DRNN to the optimal value for modeling in short time. System modeling errors are asympotatically converged to the small region , and a sliding-mode control which compensates for the neural approximation errors is proposed. The adaptation of the feedback sliding gain will stop as soon as all sliding surface have reached a small range. As a result, the undesirable parameter drift phenomenon can be avoided. It is proved that the resulting close-loop system is stable and the trajectory tracking of MIMO nonlinear systems is achieved. Some simulation results are also provided to evaluate the design. Finally, simulation results show that on-line adaptive DRNN controller provide better performance than the other adaptive modify back-propagation neural controller.
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