Abstract
A method for finding the optimal PID controller settings is proposed, which takes into account all the shortcomings of the most common engineering methods for finding controller settings. The method is characterized by: simplicity and versatility, which allows determining the optimal controller settings for one iteration; highly accurate identification of the control object, taking into account its nonlinearity, does not require an active experiment, provides improved dynamic properties of systems. Tuning parameters of controllers are found by the proposed method of finding the optimal settings of the controller and the most common engineering methods for finding the settings of controllers for ACS control objects in the production of nitric acid. In addition to the examples given, a number of control objects with varying degrees of oscillation and inertia are investigated. A comparative analysis of the proposed method with the most common engineering methods for finding controller settings for ACS control objects in the production of nitric acid is performed. The analysis shows that the controller parameters found by the proposed method significantly improve the dynamic properties of the system (the overshoot decreased by 10 times, the regulation time decreased by about 30 %, the static and dynamic errors decreased by 2–3 times).
Highlights
Market globalization is becoming comprehensive and this means that in order to stay in business, manufacturing industries need to pay more attention to quality and efficiency issues
Investigation of the method for finding the optimal controller settings The distributed control system was implemented in the TRACE MODE SCADA system, the executive modules of which ensured the real-time operation of the system
Without taking into account these features of the methods, it is impossible to further improve the methods for determining the optimal controller settings for systems and objects of various types, and to improve their dynamic properties. All these drawbacks are taken into account by the proposed method for finding the optimal controller settings [4], which combines two algorithms: an algorithm for identifying model parameters using global extrema of dynamic characteristics [4] and an algorithm for finding optimal controller settings using a quadratic optimization function [5, 6]: – to identify the control objects and further determine the optimal settings of the controller, a passive experiment is conducted in an open loop, since in this case there is no possibility of taking the system out of stability; – non-linearity of the control object is taken into account, depending on the type of the transition process, by the corresponding equation of the transition function of the second-order link [7]
Summary
Market globalization is becoming comprehensive and this means that in order to stay in business, manufacturing industries need to pay more attention to quality and efficiency issues. This focuses on the development of advanced control systems so that the processes flow better. The overwhelming majority of ACSs operating in enterprises have enormous untapped potential This potential can be “raised” at the expense of not technological measures, but at the expense of increasing the operation efficiency of these systems, optimal tuning, the main ACS devices, and controllers. Today the task is very relevant, the development of reliable methods for finding the optimal controller settings, which would significantly improve the ACS quality
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