Abstract
In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.
Highlights
This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples
The MATLAB simulation results validate that the proposed Neural Network Predictive Controller (NNPC) performs better than the conventional Proportional Integral (PI) controller
The open loop response of the proposed Single Phase Induction Motor (SPIM) at no load is shown in Figure 2; in which the linear region lies between 450 rpm and 1430 rpm
Summary
Automatic controls are indispensable to the process industries in order. Geetha to minimize complexity of plants control, to maximize the production rate and to meet sharper specification of product quality [1]-[3] This stipulates the continuous monitoring and control of Industrial Drives (ID) for set point tracking as well as for disturbance rejection [4]-[6]. If the process has a strong interference and ambiguity with a high degree of nonlinearity, only relying on normal Proportional Integral (PI) control is not effective; the use of Neural Network (NN) based controller is the viable alternative, and Model Predictive Control (MPC) is a promising substitute, in the modern era to such composite systems [6] [7] [9] [11]-[15]. The MATLAB simulation results validate that the proposed NNPC performs better than the conventional PI controller
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