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

Alagöz, Barış Baykant; Tepljakov, Aleksei; Petlenkov, Eduard; Yeroğlu, Celaleddin

doi:10.3390/a13020038

Cited by 11 publications

(12 citation statements)

References 32 publications

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“…From this perspective, disturbance rejection is a key factor in robust control performance. Any improvement in disturbance rejection performance is considered as a benefit for the overall control system [40] . At the same time, load disturbance rejection is the main issue with IMC controllers and for this reason, disturbance rejection and robustness issues will be tackled in this section.…”

Section: Numerical Resultsmentioning

confidence: 99%

An alternative design approach for Fractional Order Internal Model Controllers for time delay systems

Muresan

Birs

Keyser

2021

Journal of Advanced Research

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Section: Numerical Resultsmentioning

confidence: 99%

An alternative design approach for Fractional Order Internal Model Controllers for time delay systems

Muresan

Birs

Keyser

2021

Journal of Advanced Research

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“…The MIT rule presents a shortcoming of being sensitive to output amplitude of the reference input [5,11]. Very high amplitudes may cause instability of the system [5] and very low amplitudes diminish effectiveness of MIT rule to respond disturbances [11]. In [11], several alternative multi-loop control structures have been suggested to deal with these drawbacks.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Stability and convergence conditions of the multi-loop MRAC-FOPID control system was summarized as follows in [11]:…”

Section: Inner Loopmentioning

confidence: 99%

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A NARX Model Reference Adaptive Control Scheme: Improved Disturbance Rejection Fractional-Order PID Control of an Experimental Magnetic Levitation System

et al. 2020

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Real control systems require robust control performance to deal with unpredictable and altering operating conditions of real-world systems. Improvement of disturbance rejection control performance should be considered as one of the essential control objectives in practical control system design tasks. This study presents a multi-loop Model Reference Adaptive Control (MRAC) scheme that leverages a nonlinear autoregressive neural network with external inputs (NARX) model in as the reference model. Authors observed that the performance of multi-loop MRAC-fractional-order proportional integral derivative (FOPID) control with MIT rule largely depends on the capability of the reference model to represent leading closed-loop dynamics of the experimental ML system. As such, the NARX model is used to represent disturbance-free dynamical behavior of PID control loop. It is remarkable that the obtained reference model is independent of the tuning of other control loops in the control system. The multi-loop MRAC-FOPID control structure detects impacts of disturbance incidents on control performance of the closed-loop FOPID control system and adapts the response of the FOPID control system to reduce the negative effects of the additive input disturbance. This multi-loop control structure deploys two specialized control loops: an inner loop, which is the closed-loop FOPID control system for stability and set-point control, and an outer loop, which involves a NARX reference model and an MIT rule to increase the adaptation ability of the system. Thus, the two-loop MRAC structure allows improvement of disturbance rejection performance without deteriorating precise set-point control and stability characteristics of the FOPID control loop. This is an important benefit of this control structure. To demonstrate disturbance rejection performance improvements of the proposed multi-loop MRAC-FOPID control with NARX model, an experimental study is conducted for disturbance rejection control of magnetic levitation test setup in the laboratory. Simulation and experimental results indicate an improvement of disturbance rejection performance.

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“…Several observations concerning the LRM selection are mentioned. According to the classical control rules in model reference control [46], the LRM dynamics must be correlated with the bandwidth of the system (1). The time-delay of (1) and its possible nonminimum-phase (NMP) character must be accounted for inside the LRM as they should not be compensated.…”

mentioning

confidence: 99%

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

Rădac

Borlea

2021

Energies

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In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable systems with unknown dynamics. For the observable system, a new state representation in terms of input/output (IO) data is derived. Consequently, the Virtual State Feedback Tuning (VRFT)-based solution is redefined to accommodate virtual state feedback control, leading to an original stability-certified Virtual State-Feedback Reference Tuning (VSFRT) concept. Both VSFRT and AI-VIRL use neural networks controllers. We find that AI-VIRL is significantly more computationally demanding and more sensitive to the exploration settings, while leading to inferior LRMO tracking performance when compared to VSFRT. It is not helped either by transfer learning the VSFRT control as initialization for AI-VIRL. State dimensionality reduction using machine learning techniques such as principal component analysis and autoencoders does not improve on the best learned tracking performance however it trades off the learning complexity. Surprisingly, unlike AI-VIRL, the VSFRT control is one-shot (non-iterative) and learns stabilizing controllers even in poorly, open-loop explored environments, proving to be superior in learning LRMO tracking control. Validation on two nonlinear coupled multivariable complex systems serves as a comprehensive case study.

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

Cited by 11 publications

References 32 publications

An alternative design approach for Fractional Order Internal Model Controllers for time delay systems

An alternative design approach for Fractional Order Internal Model Controllers for time delay systems

A NARX Model Reference Adaptive Control Scheme: Improved Disturbance Rejection Fractional-Order PID Control of an Experimental Magnetic Levitation System

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

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