Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor

Abstract

This work introduces a neural-feedback control scheme for discrete-time quantized nonlinear systems with time delay. Traditionally, a feedback controller is designed under ideal assumptions that are unrealistic for real-work problems. Among these assumptions, they consider a perfect communication channel for controller inputs and outputs; such a perfect channel does not consider delays, or noise introduced by the sensors and actuators even if such undesired phenomena are well-known sources of bad performance in the systems. Moreover, traditional controllers are also designed based on an ideal plant model without considering uncertainties, disturbances, sensors, actuators, and other unmodeled dynamics, which for real-life applications are effects that are constantly present and should be considered. Furthermore, control system design implemented with digital processors implies sampling and holding processes that can affect the performance; considering and compensating quantization effects of measured signals is a problem that has attracted the attention of control system researchers. In this paper, a neural controller is proposed to overcome the problems mentioned above. This controller is designed based on a neural model using an inverse optimal approach. The neural model is obtained from available measurements of the state variables and system outputs; therefore, uncertainties, disturbances, and unmodeled dynamics can be implicitly considered from the available measurements. This paper shows the performance and effectiveness of the proposed controller presenting real-time results obtained on a linear induction motor prototype. Also, this work includes stability proof for the whole scheme using the Lyapunov approach.

Advanced Control for Electric Drives: Current Challenges and Future Perspectives

“…In [19], the influence of the harmonic torque on the performance of PMSM drive, in a pure electric vehicle, was investigated by considering the dead-time, the voltage drop effects and the nonlinear characteristics of the transmission system. In [20], a feedback controller was designed for controlling a three-phase linear induction motor. The system modeling was carried out using a neural network identifier to deal with uncertainties due to disturbances, unmodeled quantities, sensors and actuators.…”

A Recurrent Neural Network for Identifying Multiple Chaotic Systems

“…On the other hand are the algorithms that perform their training and identification online while the system to be studied is evolving [15,16]. Therefore, the weight parameter estimation is adjusted online due to training processes, such as the filtered error algorithm [17] and the Extended Kalman Filter (EKF) [18].…”