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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 ...
MPC of stochastic linear systems has been investigated in a series of articles [50] - [53] . A discrete-time system subject to unbounded disturbances with bounded variance is considered in [50] . ...
doi:10.1109/mcs.2016.2602087
fatcat:k62qjtmbbzh4tcprcjufparf64
Recursively Feasible Stochastic Model Predictive Control using Indirect Feedback
[article]
2019
arXiv
pre-print
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We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. ...
Chance constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. ...
ACKNOWLEDGMENTS The authors would like to thank A. Bollinger, C. Gaehler, J. Lygeros, and R. Smith for providing the building control simulation model. ...
arXiv:1812.06860v2
fatcat:cnkvysb73ngmzmzfebiqbii67u
Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance
[article]
2020
arXiv
pre-print
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Linear Model Predictive Control (MPC) has been successfully used for generating feasible walking motions for humanoid robots. ...
In this letter, we propose for the first time to use linear stochastic MPC (SMPC) to account for uncertainties in bipedal walking. ...
Stochastic (OCP) formulation and control objectives Consider the following discrete-LTI prediction model subject to additive stochastic disturbance w t : x t+i+1|t = Ax t+i|t + Bu t+i|t + w t+i , (15a) ...
arXiv:2005.07555v2
fatcat:x3vxbzysgrc7tdnbtom7itzcty
A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback
2008
2008 47th IEEE Conference on Decision and Control
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This paper deals with model predictive control of uncertain linear discrete-time systems with polytopic constraints on the input and chance constraints on the states. ...
We show that in the presence of chance constraints and stochastic disturbances, this closed-loop formulation can be used together with a tractable approximation of the chance constraints to further increase ...
INTRODUCTION This paper deals with solving a model predictive control (MPC) problem for the class of discrete-time linear systems subject to stochastic disturbances. ...
doi:10.1109/cdc.2008.4738806
dblp:conf/cdc/OldewurtelJM08
fatcat:24jdkoinhzf55hfll4rlpsyezy
Stochastic Model Predictive Control using Initial State Optimization
[article]
2022
arXiv
pre-print
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Considering linear discrete-time systems under unbounded additive stochastic disturbances subject to chance constraints, we use constraint tightening based on probabilistic reachable sets to design the ...
We show that the stabilizing control scheme can guarantee constraint satisfaction in closed loop, assuming unimodal disturbances. ...
For linear time-invariant systems with general additive stochastic disturbances, we have shown that standard guarantees can still be obtained. ...
arXiv:2203.01844v2
fatcat:v352kvcqyfdb5goemp6rduqwjm
Learning Stochastic Parametric Differentiable Predictive Control Policies
[article]
2022
arXiv
pre-print
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linear systems subject to nonlinear chance constraints. ...
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods ...
In particular, we consider the linear quadcopter model 1 with x k ∈ R 12 , and u k ∈ R 4 , subject to additive uncertainties ω k ∈ R 12 ∼ N (0, 0.02 2 ). ...
arXiv:2203.01447v2
fatcat:mysvqaloqbdhleaw5xluvyzvhi
Discrete-Time Model Predictive Control
[chapter]
2012
Advances in Discrete Time Systems
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Linear Model Predictive Control MPC has become an attractive feedback strategy, especially for linear processes. "y now, linear MPC theory is quite mature. ...
More than years after model predictive control MPC or receding horizon control RHC appeared in industry as an effective tool to deal with multivariable constrained control problems, a theoretical basis ...
With respect to other results on networked control, in [ ], stability and disturbance attenuation issues for a class of linear networked control systems subject to data losses mod-Advances in Discrete ...
doi:10.5772/51122
fatcat:5ucjd5ecprcrtdtcihz2bw6lkq
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control
2010
IEEE Transactions on robotics
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In this paper, we present a novel method for chance-constrained predictive stochastic control of dynamic systems. ...
This uncertainty arises due to uncertain state estimation, disturbances, and modeling errors, as well as stochastic mode transitions such as component failures. ...
This scenario approach can be applied to chance-constrained predictive control problems with non-Gaussian noise and linear system dynamics. ...
doi:10.1109/tro.2010.2044948
fatcat:qjjnhug7i5hn7makakx2dx64lq
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
[article]
2020
arXiv
pre-print
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In this work, we propose a distributed stochastic model predictive control (DSMPC) scheme for dynamically coupled linear discrete-time systems subject to unbounded additive disturbances that are potentially ...
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties, due to the use of robust formulations that are ...
In model predictive control (MPC), resulting uncertainties are often modeled through additive disturbances acting on the system. ...
arXiv:2004.02907v1
fatcat:ib4aerw4ujdqhnkjqblbxy4btm
A probabilistic validation approach for penalty function design in Stochastic Model Predictive Control
2020
IFAC-PapersOnLine
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In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive assumption on either ...
This is due to the fact that the sample complexity for both probabilistic design problems depends on the prediction horizon in a logarithmic way, unlike scenario-based approaches which exhibit linear dependence ...
CONCLUSIONS stochastic model predictive controller able to account for the effects of additive stochastic disturbances is presented in this paper. ...
doi:10.1016/j.ifacol.2020.12.362
fatcat:t4hsaz2l4fe3fmpsixag6ehfy4
An improved constraint-tightening approach for Stochastic MPC
2015
2015 American Control Conference (ACC)
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2014 pre-print arXiv:1409.8183v1 fatcat:e4e6mhnzwrag3dpqjc4x4ex2n4
The online computational complexity of the resulting Model Predictive Control (MPC) algorithm is similar to that of a nominal MPC with terminal region. ...
The problem of achieving a good trade-off in Stochastic Model Predictive Control between the competing goals of improving the average performance and reducing conservativeness, while still guaranteeing ...
Ongoing work includes relaxing the assumption of identically and independently distributed disturbance to e.g. ...
doi:10.1109/acc.2015.7170855
dblp:conf/amcc/LorenzenADT15
fatcat:lmctny3kzncxvh3oxtjclaesry
Stochastic Model Predictive Control for Linear Systems Using Probabilistic Reachable Sets
2018
2018 IEEE Conference on Decision and Control (CDC)
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In this paper we propose a stochastic model predictive control (MPC) algorithm for linear discrete-time systems affected by possibly unbounded additive disturbances and subject to probabilistic constraints ...
Two examples illustrate the approach, highlighting closed-loop chance constraint satisfaction and the benefits of the proposed controller in the presence of unmodeled disturbances. ...
INTRODUCTION Robust model predictive control (MPC) methods are wellestablished for dealing with bounded disturbances in a principled way [1] . ...
doi:10.1109/cdc.2018.8619554
dblp:conf/cdc/HewingZ18
fatcat:b6agji6znvghjkkms6vdr7anpi
Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism
2015
IEEE Transactions on Control Systems Technology
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This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. ...
Index Terms-Building energy system, nonlinear system, stochastic model predictive control (SMPC). ...
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
Stochastic model predictive control with joint chance constraints
2017
International Journal of Control
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This article considers the stochastic optimal control of discrete-time linear systems subject to (possibly) unbounded stochastic disturbances, hard constraints on the manipulated variables, and joint chance ...
Hard input constraints are guaranteed by saturating the disturbances that appear in the control law parametrization. ...
AFigure 2 : 2 tractable convex optimization program is derived to solve receding-horizon stochastic model predictive control of linear systems with (possibly) unbounded stochastic disturbances. ...
doi:10.1080/00207179.2017.1323351
fatcat:6twj4dibffaqxcrjnrbtnzz7gu
Stochastic Model Predictive Control Using Simplified Affine Disturbance Feedback for Chance-Constrained Systems
2020
IEEE Control Systems Letters
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This letter covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and chance constraints on their state and control input. ...
We propose a simplified control parameterization under the framework of affine disturbance feedback, and we show that our method is equivalent to parameterization over the family of state feedback policies ...
Content may change prior to final publication. Citation information: DOI 10.1109/LCSYS.2020.3042085, IEEE Control Systems Letters ...
doi:10.1109/lcsys.2020.3042085
fatcat:sxvivjcrjzdq3mt2x74ufwprae
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