Abstract
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.
Highlights
Real-life control systems are subject to unpredictable disturbances that may severely decrease control performance
We present the experimental results of multi-loop Model Reference Adaptive Control (MRAC)-fractional-order proportional integral derivative (FOPID) control of magnetic levitation (ML) system by using a NARX reference model
Simulation and experimental studies were conducted and it was observed that NARX reference modeling enables more intelligent realization of multi-loop MRAC-FOPID control structures
Summary
Real-life control systems are subject to unpredictable disturbances that may severely decrease control performance. Autoregressive neural network models can online learn the dynamical responses of linear and nonlinear systems from sampled input and output data [21,22,23,24] It gains significant flexibility for multi-loop MRAC-FOPID control structures to employ in real applications. The reference model is automatically identified from data that are captured from the input and output of controlled systems online This point is an important contribution of this study to facilitate the use of multi-loop MRAC-FOPID control structures in practical control applications. The contribution of this research work can be stated as follows: a multiloop control method is proposed based on the FOPID retuning approach and computational intelligence in the form of an artificial neural network-based reference model used in the MRAC scheme for endowing the complete control system with greater robustness without deteriorating set-point tracking performance.
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