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Computationally Efficient Nonlinear MPC for Discrete System with Disturbances

Keerthi Chacko*, Janardhanan Sivaramakrishnan, and Indra Narayan Kar
International Journal of Control, Automation, and Systems, vol. 20, no. 6, pp.1951-1960, 2022

Abstract : Nonlinear Model Predictive Controller (NMPC) is intensive in online computation. We propose an efficient formulation for reducing its computational requirements. The proposed algorithm avoids stability-related terminal costs, constraints, and varies the prediction horizon after a simple check. Further, we use a condition based on negative contraction to handle undesirable effects of disturbance on the algorithm. The stability analysis for the proposed algorithm in a Monotonically weighted NMPC framework without stability related constraints is derived. Simulation and experimental validation on benchmark systems illustrate a significant reduction in the average computation time compared to the Monotonically Weighted NMPC without much loss in performance.

Keyword : Computation reduction, nonlinear process, optimization, predictive control, varying horizon.

 
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