Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Generalized predictive control—Part II. Extensions and interpretations
Automatica (Journal of IFAC)
Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
Stochastic and neural models of an induction motor
Mathematics and Computers in Simulation - Special issue on modelling and simulation of electrical machines
Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Application of Artificial Intelligence in Process Control: Lecture Notes Erasmus Intensive Course
Application of Artificial Intelligence in Process Control: Lecture Notes Erasmus Intensive Course
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In this paper the authors present a new advanced control algorithm for speed and flux tracking of an induction motor. This algorithm, called neural networks generalized predictive control (NGPC), uses a combination of artificial neural networks (ANN) and generalized predictive control (GPC) technique. The later is traditionally used for systems characterized by a slow dynamics, as in industrial process control. The NGPC algorithm is based on the use of ANN as a nonlinear prediction model of the motor. This modelling technique is done by using I/O data with no need of additional information regarding the machine parameters. The outputs of the neural predictor are the future values of the controlled variables needed by the optimization procedure, which is achieved by minimizing a cost function with a reference control model using the Newton-Raphson optimization algorithm. Simulation results show the effectiveness of the proposed control method.