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An Adaptive Hybrid Technique for Frequency Domain Identification of Servo System with Friction Force
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Abstract
A servo system, sometimes also called as servomechanism, is a mechanical device that uses error-sensing negative feedback to correct the performance of a mechanism. A servo system is a well known control system that performs its controlling action through a plant, controller, and an actuator. Any machine or piece of equipment that has rotating parts contains one or more servo control systems for controlling the nonlinearities in the system. These mechanical devices usually come with undesirable nonlinearities. Hence, due to these nonlinearities the frequency domain system identification process in servo system seems to be a tough task. To overcome the problem, in the paper, an adaptive hybrid technique will be proposed. The proposed adaptive hybrid technique is combined with adaptive neural network and adaptive genetic algorithm. In the proposed adaptive hybrid technique, the input training dataset and the number of hidden layer of network is optimized by classical genetic algorithm. Then, the system parameters are optimized by the proposed adaptive genetic algorithm. The proposed adaptive hybrid technique is implemented in MATLAB working platform and the system identification performance is tested by using two testing system. Then, the adaptive hybrid technique is compared with classical system and conventional hybrid technique
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