A simulated annealing-based optimal controller for a three phase induction motor

This paper presents a new approach for optimal controller design of a three-phase induction motor (IM), based on using the simulated annealing (SA) method to find the optimal controller gains that satisfy a specific performance criterion. Optimal control requires well-known information about the system dynamics, which will preclude its applicability with systems having partially known or unknown dynamics. Accordingly; the proposed approach is implemented to emulate the structure and hence the characteristics of the optimal controller in spite of the partially known system dynamics, inaccuracy or uncertainties of system parameter. The problem is a hard nonlinear optimization problem in continuous variables. An adaptive cooling schedule and a new method for variables discretization are implemented to enhance the speed and convergence of the original simulated annealing algorithm (SAA). The proposed algorithm comprises structure of the optimal controller, a new error system and vector control of a three phase IM. The IM is described as a three input, three output controlled object. The state equations of IM suitable for voltage control are implemented based on the vector, method. Simulation results show better system performance compared to previously obtained results

"A Global ANN Algorithm for Induction Motor Based On Optimal Preview Control Theory"

In this paper a global Artificial Neural Network (ANN), algorithm for on-line speed control of a threephase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the 1M. The IM model includes thee input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and wnstant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed Ah” using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have ken carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm

"A Global ANN Algorithm for Induction Motor Based On Optimal Preview Control Theory"

In this paper a global Artificial Neural Network (ANN), algorithm for on-line speed control of a threephase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the 1M. The IM model includes thee input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and wnstant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed Ah” using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have ken carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm