A genetic algorithm based recurrent fuzzy neural network for linear induction motor servo drive

Abstract

A genetic algorithm (GA) based recurrent fuzzy neural network (RFNN) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. The GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an on-line training RFNN with backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a real-time GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN on-line. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides good control performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.

A Concise Review of Control Techniques for Reliable and Efficient Control of Induction Motor

“…The factor selection for the input of membership functions was achieved through an optimized GA based Fuzzy Logic Controller [130]. In a similar work, the best parameters for the Proportional Integral controller were calculated using GA based Proportional Integral controller [131,132]. GA was used in hybrid FLC-PI controller for the prototype implementation using dSPACE, and to improve the performance of an induction motor [133].…”

Speed Control of Induction Motor using LQG

“…Induction motor control classification [16] The FOC and DTC methods are basically torque control, so to perform speed control an additional speed control method is required. There are also many speed control algorithms for induction motor speed control, such as PID [17], the fuzzy logic controller (FLC) [18][19], sliding mode controller (SMC) [10], Artificial Neural Network (ANN) [20][21], and the combination between conventional and artificial intelligent method as can be found in [22][23][24].…”