Abstract
Neural Adaptive based on feedback linearization is used in this study to control the velocity and recognition of an electro hydraulic servo system (EHSS) in the presence of flow nonlinearities as well as internal friction and noise. This controller consists of four parts: PID controller, feedback linearization controller, neural network controller and the neural network identifier. The feedback linearization controller is used to prevent the system state in a region where the neural network can be accurately trained to achieve optimal control. The combination of controllers produces a stable system which adapts to optimize performance. This technique, as shown, can be prosperously used to stabilize any selected operating point of the system with noise and without interference. All consequences achieved are validated by computer simulation of a nonlinear mathematical model of the system. The fore mentioned controllers have a vast range to control the system. We compare Neural Adaptive based on feedback linearization controller results with feedback linearization, back stepping and PID controller.
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