Robust cooperative distributed MPC: A multi-model approach

Sencio, Rafael R.; Odloak, Darci

doi:10.1016/j.jprocont.2022.07.003

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…2) The error system is obtained and the Lyapunov function S(e(t)) = eT(t)Pe(t) is constructed. The feedback control rate K and the set of perturbation invariants Z are obtained using linear matrix inequalities (Rafael and Darci, 2022).…”

Section: Design Of the Rmpc Controllermentioning

confidence: 99%

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

Tang,

Wang,

Yan

et al. 2023

Front. Energy Res.

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This paper studies the effect of different turbulent wind speeds on the operation of wind turbines. The proportion of wind power in the field of new energy generation has increased massively and has gained wide application and attention. However, the smooth operation and the stability of the output power of the wind power generation system are susceptible to wind speed fluctuations. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. The 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed. Therefore, the stable controller for wind power output is the robust model predictive controller with parameter improvement.

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Section: Design Of the Rmpc Controllermentioning

confidence: 99%

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

Tang,

Wang,

Yan

et al. 2023

Front. Energy Res.

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“…For large-scale linear systems with interconnected dynamics, an improved iterative solution of the cooperative distributed MPC algorithm is proposed in [15]. For the setpoint tracking and the zone control problem, [16] presents two algorithms for a robust cooperative distributed MPC based on a multi-model approach. The update sequences of the entire subsystems are parallel in these research results, and the optimization problems are solved multiple times in each control period.…”

Section: Introductionmentioning

confidence: 99%

Robust tube‐based distributed MPC for PWA systems

2023

IET Control Theory & Appl

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A robust tube‐based distributed model predictive control method is proposed for spatially interconnected systems with constraints and disturbances. The system contains multiple discrete‐time piecewise affine subsystems, which are coupled to each other through states. The predictive states of each subsystem are dependent on its states, inputs, and neighbouring states, and shrinking constraints are constructed to deal with the disturbances and the model mismatches. The differences between the predicted states at the current time instant and the optimal states at the previous time instant are constrained, which improves the accuracy of the predicted model and enhances the control performance. The conditions which ensure the recursive feasibility of optimization problems and the asymptotic stability of closed‐loop system are obtained. The effectiveness of the proposed method is verified by the simulation results.

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“…Numerical results in a simulated two reactors-separator system are presented along with the comparison of CPU (central processing unit) times spent by the proposed strategies. The first two algorithms proposed in Chapter 4 have been published in Sencio and Odloak (2022).…”

Section: Outline Of the Thesismentioning

confidence: 99%

“…The proposed methods are applied to two case studies and compared to the centralized robust MPC. The first two algorithms proposed in this chapter have been published in Sencio and Odloak (2022).…”

Section: Introductionmentioning

confidence: 99%

Model predictive control based on the output prediction-oriented model: a dual-mode approach, and robust distributed algorithms.

Sêncio¹

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for their valuable comments and suggestions that helped me to improve the text.I am extremely grateful to Guilherme for his constant review of my texts, for our fruitful exchange of experience in MATLAB, CasADi and L A T E X, and for the numerous conversations we had about control theory and the stability ingredients of different MPC formulations.I would like to thank Prof. Thiago Costa for inviting me to collaborate on papers we published together during this period. I would also like to thank him for being the one who introduced me to this fascinating world of Model Predictive Control.Thanks should also go to my friends from the Department of Telecommunications and Control Engineering, Fábio, Leonardo, Mateus and Gabriel, for our exchange of knowledge on control theory, and for the great time we spent together during our trip to take the LMI course at UNICAMP and during the CBA 2018. I am also grateful for always being welcome in their laboratory and for the espressos we had together at LCA.

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Robust cooperative distributed MPC: A multi-model approach

Cited by 5 publications

References 29 publications

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

Robust tube‐based distributed MPC for PWA systems

Model predictive control based on the output prediction-oriented model: a dual-mode approach, and robust distributed algorithms.

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