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Computationally efficient infinite-horizon indefinite model predictive control with disturbance preview information
journal contributionposted on 2023-01-26, 09:08 authored by Siyuan Zhan, Wen-Hua ChenWen-Hua Chen, Thomas SteffenThomas Steffen, John Ringwood
This paper proposes a model predictive control (MPC) scheme for maximising the benefit of a useful disturbance by exploiting preview information of the disturbance, in the context of goal-oriented operation. For a constrained system, subject to a persistent, bounded, and predictable disturbance, rather than attenuating the influence of disturbance, the proposed MPC aims to utilise the disturbance to optimise high-level economic criteria, e.g., profitability and productivity, which are normally represented by an indefinite cost function. For linear time-invariant systems, after examining the influence of the future disturbance profile, a computationally efficient finite-horizon convex approach is proposed to approximate the solution of the original possibly non-convex infinite-horizon optimisation problem. Then, a receding-horizon implementation is developed, taking into account the recursively updated disturbance prediction, and the recursive feasibility and input-to-state stability of the implementation are established. Numerical examples are provided to verify the efficacy of the proposed method.
Goal-Oriented Control Systems (GOCS): Disturbance, Uncertainty and Constraints
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- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
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Publisher statementThis is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0.