An artificial intelligence-based control system for an electric motor drive

Recently, artificial intelligence (AI) that mImICS the adaptivedistributed architecture in the human brain provides potential alternatives totackle different control problems in the industry.When the structure of the plant is unknown or the parameter variationis excessive; the effectiveness of modem control theory diminishes. Even, if it is possible to develop a asonably accurate model, the resulting controlalgorithm is so computationally intensive that it becomes very difficult, and some times infeasible to implement in a real-time control vironment.So, it is believed that a controller should be designed to have abilitiesto learn from experience and to use the knowledge gained.The applications of AI techniques such as artificial neural networks(ANN) promise high computation rate provided by the massive parallelism, thedistributed representation, and the adaptation, learning and generalization toimprove performance.A separately excited de motor drive has been used in the industrial fieldwhen a wide range and high accurate speed control is necessary.The aim of the thesis is to design a controller that has abilities to learnfrom experience and to use the knowledge gained to improve the total