Multi-Loop Model Reference Proportional Integral Derivative Controls: Design and Performance Evaluations

Baris Baykant Alagoz,Celaleddin Yeroglu,Aleksei Tepljakov,Eduard Petlenkov

doi:10.3390/a13020038

Baris Baykant Alagoz, Celaleddin Yeroglu + Show 2 more

Open Access

https://doi.org/10.3390/a13020038

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Journal: Algorithms	Publication Date: Feb 13, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: Inonu University, Tallinn University of Technology

Abstract

Due to unpredictable and fluctuating conditions in real-world control system applications, disturbance rejection is a substantial factor in robust control performance. The inherent disturbance rejection capacity of classical closed loop control systems is limited, and an increase in disturbance rejection performance of single-loop control systems affects the set-point control performance. Multi-loop control structures, which involve model reference control loops, can enhance the inherent disturbance rejection capacity of classical control loops without degrading set-point control performance; while the classical closed Proportional Integral Derivative (PID) control loop deals with stability and set-point control, the additional model reference control loop performs disturbance rejection control. This adaptive disturbance rejection, which does not influence set-point control performance, is achieved by selecting reference models as transfer functions of real control systems. This study investigates six types of multi-loop model reference (ML-MR) control structures for PID control loops and presents straightforward design schemes to enhance the disturbance rejection control performance of existing PID control loops. For this purpose, linear and non-linear ML-MR control structures are introduced, and their control performance improvements and certain inherent drawbacks of these structures are discussed. Design examples demonstrate the benefits of the ML-MR control structures for disturbance rejection performance improvement of PID control loops without severely deteriorating their set-point performance.

Highlights

Control systems encounter unpredictable disturbances in real-world control applications
The findings of the current study suggest that a multi-loop model reference (ML-MR) adaptive control structure could be an effective solution to improve the disturbance rejection performance of classical Proportional Integral Derivative (PID) control loops
To create a design from scratch for a first order system model in the form of G(s) = τsK+1 e−Ls, an analytical tuning scheme for the ML-MR PID-Massachusetts Institute of Technology (MIT) control structure can be proposed as follows: Step 1: Design a closed loop PID control system according to the Tavakoli–Tavakoli PID tuning rule that can be rearranged for a standard PID controller function in parallel form as follows [29]: kp = (1/K)

Summary

Introduction

Control systems encounter unpredictable disturbances in real-world control applications. Previous studies on the ML-MR PID-MIT control structure aim to maintain the initial well-tuned control performance of an existing closed loop control system For this reason, the reference model is taken as the transfer function of the inner loop; that is, Tm (s) = T (s). To create a design from scratch for a first order system model in the form of G(s) = τsK+1 e−Ls , an analytical tuning scheme for the ML-MR PID-MIT control structure can be proposed as follows: Step 1: Design a closed loop PID control system according to the Tavakoli–Tavakoli PID tuning rule that can be rearranged for a standard PID controller function in parallel form as follows [29]: kp = (1/K) This optimal tuning rule implements ITAE and its control performance have been shown previously [29]. ML-MR PID-MIT control structure design control of responses the plant with zero time delay (G1 (s)for

PID-MIT control structure can significantly

Comparison of G disturbance

Multi-Loop

Discussions and Conclusions

Results