An Application of MVMO Based Adaptive PID Controller for Process with Variable Delay
In this work, a Mean–Variance Mapping Optimization (MVMO) based adaptive PID controller is developed for a chemical process with variable time delay. For this, an adaptive tuning equation is obtained based on delay time variation. In order to verify the p
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Abstract. In this work, a Mean–Variance Mapping Optimization (MVMO) based adaptive PID controller is developed for a chemical process with variable time delay. For this, an adaptive tuning equation is obtained based on delay time variation. In order to verify the performance of the proposed controller, a comparison against an adaptive Smith Predictor, an adaptive control tuned by Dahlin equations and a Gain Scheduling controller through the ISE, TVu indexes is made. The simulations results show that the response of the system with the proposal approach improves the performance of the process with variable time delay. Keywords: Adaptive control Optimization problems
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· MVMO · Smith Predictor ·
Introduction
Most industrial processes are inherently nonlinear, and this feature makes the control problem challenging because nonlinearities could make the system not always kept at operating conditions. Another problem in industrial processes is the time-delays, which could vary due to the dynamic behavior of the system, strong disturbances or the delay time that comes from a material that moves from one point to another [1,2]. When time-delay varies, the controller could not be effective for new scenarios and this implies possible plant stoppages and economic losses [3], in these situations, the process needs a new tuning or adjustment of the control parameters. The PID controller is widely used in the industry due to its simple structure and ease of implementation. Studies have shown that 90% of all controllers in the process industry have a PID structure [4]. However, the performance of this controller is limited for complex systems, such as those with time-varying parameters, and more than 60% of them require re-tuning after only 6 months [5]. Thus, PID control schemes that can adapt their parameters to the new scenarios have been developed, the main applications of adaptive PID control c The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Botto-Tobar et al. (Eds.): ICCIS 2020, AISC 1273, pp. 353–365, 2021. https://doi.org/10.1007/978-3-030-59194-6_29
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(APID) has been made on non-linear systems, where the analysis and control implementation are difficult [6]. However, most of the APID parameters are tuned based on classical methods such as Ziegler-Nichols, consequently, their performance could not be the best [7]. In order to tune PID controllers, several authors had studied different optimization algorithms. For instance, Verma S. and Mudi R. developed Particle Swarm Optimization (PSO) based adaptive PID controller for a chemical process [8]. Another optimization algorithm used in order to tune a PID controller for a tank coupled system is based on Genetic Algorithm (GA) [9]. In the present work, an adaptive control scheme based on Mean-Variance Mapping Optimization (MVMO) for tuning the PI controller parameters for a process with variable time-delay is developed. MVMO is the optimization algorithms proposed in [10], has been widel
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