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.
Highlights
A control system is designed based on many assumptions, which are rarely satisfied in real-life systems
This paper presents the design and implementation of a discrete-time neural controller for quantized nonlinear systems with delays and measurement noise, while presenting disturbances, uncertainties and unmodeled dynamics, without the need for prior knowledge of the system model or the need to estimate its bounds, nor nominal models
We propose to solve the problem of designing a discrete-time neural controller for quantized uncertain nonlinear systems under the presence of delays, without the need to have prior knowledge of the system model
Summary
A control system is designed based on many assumptions, which are rarely satisfied in real-life systems. One of these assumptions is a mathematical model that perfectly represents the behavior of the system. Electronics 2020, 9, 1274 are phenomena that are present in the day to day systems These factors are sources that need to be compensated adequately by the controller to have proper performance and avoid stability problems. The existence of delays can severely degrade the performance of the controllers, as well as causing a loss in system stability, among other undesirable effects [1,2,3]. The design of controllers that consider and compensate quantization effects of measured signals is a problem that has attracted the attention of control system researchers for many years [6], which is highly relevant due to the importance that the investigation of networked control systems has gained recently [7]
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