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The dynamic response of a hybrid controlled quick-return mechanism, which is driven by a servo induction motor (IM), is described in this thesis. By means of a microprocessor, indirect field-oriented control method and some position control strategies, the proposed robust control schemes, based on a neural network, focus on improving the dynamic behavior to achieve a high performance of motor-quick-return mechanism system.
Moreover, in order to deal with the design of high performance tracking control of a motor-quick-return mechanism, an artificial recurrent neural network (RNN) control scheme that uses continual online training to simultaneously identify and control the quick-return mechanism is proposed. The control scheme utilizes two three-layer RNNs: 1) a recurrent neural network identifier (RNNI) which captures the nonlinear dynamics of the plant system over any arbitrary time interval in its of operation and 2) a recurrent neural network controller (RNNC) combining with a conventional PI controller to provide the necessary control actions to achieve tracking desired reference trajectory. Due to dynamic back propagation (DBP) algorithm is utilized for online training purposes, online training has been carried out to update RNN under continuous mode of operation. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero.
Finally, the simulation and experimental results due to sinusoidal reference command reveal that the control architecture adapts and generalizes its learning to wide range of operating conditions and provides promising results under parameter variations and load changes.

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