Abstract
This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.
Highlights
Vector-controlled induction motors (IMs) have been implemented in various industrial applications [1,2,3,4]
This paper proposes an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) to improve the performance of the FCMAC
In order to demonstrate the feasibility of the proposed adaptive supervisory sliding FCMAC control system in induction motor system, simulation results are presented
Summary
Vector-controlled induction motors (IMs) have been implemented in various industrial applications [1,2,3,4]. Developing an effective method for designing a speed controller for high-performance vector-controlled IM drives is crucial. Several studies have developed intelligent methods for various applications Such methods include neural networks (NNs) [5,6,7,8] and the cerebellar model articulation controller (CMAC) [9,10,11,12]. Previous studies have addressed various aspects associated with the conventional CMAC, including the selection of a basis function, input-space partitioning, weight-space size, and the incorporation of appropriate learning algorithms. The outputs of the traditional CMAC are not continuous for consecutive quantized states, which can cause control actions to fluctuate
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