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Least-restrictive robust periodic model predictive control applied to room temperature regulation
2013
Automatica
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State-feedback model predictive control (MPC) of constrained discrete-time periodic affine systems is considered. ...
The periodic systems' states and inputs are subject to periodically time-dependent, hard, polyhedral constraints. Disturbances are additive, bounded and subject to periodically time-dependent bounds. ...
Fig. 5 . 5 Soft state constraints. Nominal periodic MPC without guarantees of state constraint satisfaction. Nominal (solid, blue), with stochastic disturbances (dashed, green). q = 0. ...
doi:10.1016/j.automatica.2013.05.009
fatcat:daedaptwkfat3fsajd4jjhtdr4
A Comparative Study of Stochastic Model Predictive Controllers
2020
Electronics
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A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. ...
systems with additive disturbances. ...
The aim of this article is to present a comparative study of two stochastic model predictive controllers, for linear systems with parametric and additive uncertainties, belonging to the SMPC and SCMPC ...
doi:10.3390/electronics9122078
fatcat:j4krd6em4nadtbnzfvobvxdnbu
Application of robust model predictive control to a renewable hydrogen-based microgrid
2016
2016 European Control Conference (ECC)
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Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems. ...
In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive ...
Remark 1: The presence of the additive stochastic disturbance may lead to infeasibility when the constraints on the states and inputs, and the risk of violation of constraints are not suitably chosen. ...
doi:10.1109/ecc.2016.7810454
dblp:conf/eucc/VelardeMOB16
fatcat:jjur67r5xvbutdpqrl43f233nu
Constraint-Tightening and Stability in Stochastic Model Predictive Control
[article]
2016
arXiv
pre-print
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A numerical example, demonstrating the efficacy of the proposed approach in comparison with classical, recursively feasible Stochastic MPC and Robust MPC, is provided. ...
Constraint tightening to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control is addressed. ...
For linear systems with additive stochastic disturbance, the system is usually decomposed into a deterministic, nominal part and an autonomous system involving only the uncertain part. ...
arXiv:1511.03488v2
fatcat:zeop56otnzc3njmnmzxaqsluja
Stochastic model predictive control approaches applied to drinking water networks
2016
Optimal control applications & methods
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To cope with uncertainty in system disturbances due to the stochastic water demand/consumption, and optimize operational costs, this paper proposes three stochastic model predictive control (MPC) approaches ...
CC-MPC is a stochastic control strategy that provides robustness in terms of probabilistic (chance) constraints, such that the probability of violation of any operational requirement or physical constraint ...
approach (i.e., best, worst and average), and the CE-MPC with an average disturbance. ...
doi:10.1002/oca.2269
fatcat:zt2bb3toynaozp6cvbycoktugu
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research
2016
IEEE Control Systems
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Stochastic Model Predictive Control M odel predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. ...
The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control ...
Stochastic Tube Approaches Stochastic tube approaches to MPC of linear systems with additive, bounded disturbances are presented in [43] and [44] with the objective of minimizing the infinite-horizon ...
doi:10.1109/mcs.2016.2602087
fatcat:k62qjtmbbzh4tcprcjufparf64
On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid
2017
Journal of Power Sources
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The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. ...
Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller ...
. • Joint chance constraints, which take into account an unique risk of constraint violation for all stochastic constraints. ...
doi:10.1016/j.jpowsour.2017.01.015
fatcat:vtu56mftznhlhp7wlxjhy5wfyy
Stochastic Time-Varying Model Predictive Control for Trajectory Tracking of a Wheeled Mobile Robot
2021
Frontiers in Energy Research
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It is proved that, with the proposed stochastic MPC, the tracking error of the closed-loop system is asymptotically average bounded. A simulation example is provided to support the theoretical result. ...
The wheeled mobile robot is supposed to subject to additive stochastic disturbance with known probability distribution. ...
AUTHOR CONTRIBUTIONS WZ designed the main algorithm, implemented the simulation example, produced the main results, and wrote the main parts of the paper. ...
doi:10.3389/fenrg.2021.767597
fatcat:rm4a7grtrzdp5jve2lrou4dahe
On the Assessment of Tree-Based and Chance-Constrained Predictive Control Approaches applied to Drinking Water Networks
2014
IFAC Proceedings Volumes
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In both approaches, a model predictive controller is used to optimise the expectation of the operational cost of the disturbed system. ...
control (TB-MPC). ...
Given the stochastic nature of future disturbances, the prediction model (3b) involves exogenous additive uncertainty, hence, the compliance of constraints for a given control input cannot be ensured. ...
doi:10.3182/20140824-6-za-1003.01648
fatcat:rb5m6plilzcfbfzpn7y3okiia4
Chance-constrained model predictive control for drinking water networks
2014
Journal of Process Control
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This paper addresses a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problem with ...
violation of constraints. ...
Acknowledgements This work has been partially supported by the EU Project EFFINET ...
doi:10.1016/j.jprocont.2014.01.010
fatcat:zkuizbdddvgf7n5e24kubo6fwm
A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System
2022
Energies
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violation rate compared to robust MPC with a similar energy utilization due to the incorporation of the stochastic characteristics of wind speed. ...
The robust MPC is designed by utilizing the min–max framework to track steady-state optimum operating reference trajectory with the deterministic constraint of output power, while the stochastic MPC is ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en15134814
fatcat:ofc5bzceorgl7d5vsbwpipumii
Data-driven distributionally robust MPC using the Wasserstein metric
[article]
2021
arXiv
pre-print
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A data-driven MPC scheme is proposed to safely control constrained stochastic linear systems using distributionally robust optimization. ...
Distributionally robust constraints based on the Wasserstein metric are imposed to bound the state constraint violations in the presence of process disturbance. ...
PROBLEM FORMULATION In this section we derive POB affine control laws Ben-Tal, El Ghaoui and Nemirovski (2009) for a discrete-time linear time-invariant (LTI) dynamical system with additive disturbance ...
arXiv:2105.08414v1
fatcat:7vljoznogvgm3dgzyudrzdfzmy
Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism
2015
IEEE Transactions on Control Systems Technology
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Index Terms-Building energy system, nonlinear system, stochastic model predictive control (SMPC). ...
In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. ...
The authors would like to thank the anonymous reviewers for their helpful comments on the original version of the manuscript. ...
doi:10.1109/tcst.2014.2313736
fatcat:md2ny5mkg5bsvm4s2sixard2fm
Handbook of Model Predictive Control [Bookshelf]
2020
IEEE Control Systems
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This handbook enables the reader to gain a panoramic viewpoint of MPC theory and practice as well as provides a state-of-the art overview of new and exciting areas of application at the forefront of MPC ...
This book provides a thorough and comprehensive reference of the underlying theory, implementation, and applications of MPC. ...
Various formulations are presented for deterministic systems, including those with and without terminal ingredients and those with average constraints, where average performance is optimized. ...
doi:10.1109/mcs.2020.3005257
fatcat:2jxwcbu2nbgjfcygqky7yphnmu
Stochastic Model Predictive Control for Water Transport Networks with Demand Forecast Uncertainty
[chapter]
2017
Advances in Industrial Control
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The availability of historical data allows to accurately predict the behaviour of the system disturbances over large horizons, but still a meaningful degree of uncertainty is present. ...
In previous chapters, the use of MPC to tackle the complex multi-variable interactions and large-scale nature of drinking-water network control is proposed. ...
The DWN is considered as a stochastic constrained system subject to deterministic hard constraints on the control inputs and linear joint chance constraints on the states. ...
doi:10.1007/978-3-319-50751-4_14
fatcat:uoynpp6rcjda3p56pz7ibefple
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