1999/03/01 by M.A. Brdys, Mietek A. Brdyś, G.J. Kulawski
Computer Science · Engineering · #Adaptive Control of Nonlinear Systems #Neural Networks and Applications #Sensorless Control of Electric Motors
paper · doi:10.1109/72.750564
openalex publication_date 1999/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
The paper reports application of recently developed adaptive control techniques based on neural networks to the induction motor control. This case study represents one of the more difficult control problems due to the complex, nonlinear, and time-varying dynamics of the motor and unavailability of full-state measurements. A partial solution is first presented based on a single input-single output (SISO) algorithm employing static multilayer perceptron (MLP) networks. A novel technique is subsequently described which is based on a recurrent neural network employed as a dynamical model of the plant. Recent stability results for this algorithm are reported. The technique is applied to multiinput-multioutput (MIMO) control of the motor. A simulation study of both methods is presented. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control.