The Method of Synthesis of a Stable Closed-Loop Object Control System with Limiters

Abstract

The modern theory of automatic control is faced with the problem of complexity of synthesis of regulators for nonlinear control objects in conditions of incomplete information. The existing methods and approaches can no longer satisfy the needs of developers of automatic control systems for complex dynamic objects. In many cases, control objects are essentially nonlinear, nonstationary and require the use of digital control with specified quality indicators. In this case, obtaining an accurate mathematical model is not always possible. We propose an approach to solving this problem using regulators based on artificial neural networks. They can be effectively applied in the case when there is no adequate verified and sufficiently accurate mathematical model of the control object, but experimental data can be obtained. The advantage of such regulators is their ability to learn and adapt to the object based on the obtained data. In addition, there are no theoretical stability guarantees for closed-loop neural network control systems, which significantly reduces the possibility of their application in critical or hazardous facilities. To solve this problem, the paper proposes a method for synthesizing a neural controller that guarantees the stability of a closed loop. Systems with the most frequently encountered in practice nonlinearities (saturation type limiters, rigid mechanical stop type limiters, etc.) are considered as control objects. This paper proposes theoretical approaches to the solution of these problems, and also carries out a comparative analysis with experimental studies to assess the effectiveness of the proposed methods.