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A New Adaptive Explicit Nonlinear Model Predictive Control Design for a Nonlinear MIMO System: An Application to Twin Rotor MIMO System

  • Control Theory and Applications
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Abstract

This paper proposed an adaptive explicit nonlinear model predictive control (AENMPC) technique using multiple estimation models with a convex combination framework [18] for a class of nonlinear MIMO systems. Here, the explicit solution for the control signal is obtained from an optimal performance index which can be formulated without online optimization. In this work, a closed-form control law is developed by approximating the tracking error in the receding horizon by its Taylor series expansion. The control performance of any model-based control technologies explicitly depends on the quality of the unknown system parameters hence an adaptive parameter estimator is used to estimate the system parameter online [16,17]. To ensure the boundedness of the estimated parameter within a predefined compact region, a projection based adaptive law is used [43]. Using an aerodynamic laboratory set-up, known as the twin-rotor multi-input multi-output system (TRMS), the effectiveness of the proposed control algorithm has been verified. The complete state information of the system to the proposed adaptive controller is given from an extended Kalman filter based state observer. The performance of the proposed adaptive control algorithm has been verified successfully in simulations as well as real-time experimental setup of the TRMS model and compared with an existing control approach.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, National Institute of Technology Nagaland, Dimapur, India

    Lakshmi Dutta & Dushmanta Kumar Das

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  1. Lakshmi Dutta
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Correspondence to Dushmanta Kumar Das.

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Lakshmi Dutta received her B.E. degree in instrumentation and control engineering from Dr. D.Y.Patil Pratishthan’s college of Engineering, Pune, India. She received an M.Tech degree in control and instrumentation engineering from Netaji Subhash Engineering college, West Bengal, India. Currently she is pursuing a Ph.D. degree in the Department of Electrical and Electronic Engineering from National Institute of Technology Nagaland, India. Her research interests include nonlinear estimation and adaptive model predictive control with application in control system.

Dushmanta Kumar Das is a Member of IEEE. He received his B.Tech. degree in electronics and instrumentation engineering from BPUT, Odisha, India. He received an his M.Tech and Ph.D. degrees in control system engineering from the National Institute of Technology Rourkela, India, in 2010 and 2015, respectively. Since 2013, he has been with National Institute of Technology Nagaland, India as an Assistant Professor. His research interests include control system, robotics, soft computing, machine learning, meta-heuristic optimization techniques, and application of optimization techniques.

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Dutta, L., Das, D.K. A New Adaptive Explicit Nonlinear Model Predictive Control Design for a Nonlinear MIMO System: An Application to Twin Rotor MIMO System. Int. J. Control Autom. Syst. 19, 2406–2419 (2021). https://doi.org/10.1007/s12555-020-0272-5

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