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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 ...
Chance-constrained predictive control of linear and jump-Markov linear systems with arbitrary (nonconvex or multimodal) disturbance distributions is considered in [93] . ...
doi:10.1109/mcs.2016.2602087
fatcat:k62qjtmbbzh4tcprcjufparf64
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 ...
We revisit the rather classical approach based on penalty functions, with the aim of designing a control scheme that meets some given probabilistic specifications. ...
INTRODUCTION Model predictive control (MPC) is a popular control strategy mainly for its ability to deal with multivariate systems and constraints in a systematic fashion. ...
doi:10.1016/j.ifacol.2020.12.362
fatcat:t4hsaz2l4fe3fmpsixag6ehfy4
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 method applies to arbitrary noise distributions, and for systems with linear or jump Markov linear dynamics, we show that the approximate problem can be solved using efficient mixed-integer linearprogramming ...
ACKNOWLEDGMENT Some of the research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, and at the Massachusetts Institute of Technology ...
doi:10.1109/tro.2010.2044948
fatcat:qjjnhug7i5hn7makakx2dx64lq
(Stochastic) Model Predictive Control – a Simulation Example
[article]
2023
arXiv
pre-print
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2021 pre-print arXiv:2101.12020v5 fatcat:f5lav7k3uremplw7jiudw7wdomOther Versions
A simple linear system subject to uncertainty serves as an example. The Matlab code for this stochastic Model Predictive Control example is available online. ...
This brief introduction to Model Predictive Control specifically addresses stochastic Model Predictive Control, where probabilistic constraints are considered. ...
The main purpose of this document is to introduce the idea of considering uncertainty in constraints within Model Predictive Control (MPC). ...
arXiv:2101.12020v6
fatcat:pt3kjqys6zbwtjltizw4o62kra
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. ...
Smith for providing the building control simulation model. ...
arXiv:1812.06860v2
fatcat:cnkvysb73ngmzmzfebiqbii67u
Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions
2016
2016 American Control Conference (ACC)
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This paper presents a stochastic model predictive control approach for a class of nonlinear systems with unbounded stochastic uncertainties. ...
The control approach aims to shape probability density function of the stochastic states, while satisfying input and joint state chance constraints. ...
STOCHASTIC NONLINEAR MODEL PREDICTIVE CONTROL This section presents the formulation of the Lyapunovbased SNMPC approach with joint state chance constraints. ...
doi:10.1109/acc.2016.7526514
dblp:conf/amcc/BuehlerP016
fatcat:zk2yrzwpnvhx5cmgqdu4lsqvc4
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. ...
Constraint-tightening for Chance Constraint Satisfaction We make use of PRS for the predicted error system (8b) according to Definition 5 in order to tighten the constraints such that chance constraints ...
doi:10.1109/cdc.2018.8619554
dblp:conf/cdc/HewingZ18
fatcat:b6agji6znvghjkkms6vdr7anpi
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 ...
The objective is to control the distributed stochastic system over a potentially large, but finite, task horizon N while satisfying the chance constraints (3) at every time step t. ...
arXiv:2004.02907v1
fatcat:ib4aerw4ujdqhnkjqblbxy4btm
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 this paper, we bring in a novel stochastic sampling-based design for differentiable predictive control architecture along with chance constraints for closed-loop state evolution and provide appropriate ...
arXiv:2203.01447v2
fatcat:mysvqaloqbdhleaw5xluvyzvhi
Chance-constrained model predictive control
1999
AIChE Journal
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This work focuses on robustness of model predictive control (MPC) with respect to satisfaction of process output constraints. A method of improving such robustness is presented. ...
The method relies on formulating output constraints as chance constraints using the uncertainty description of the process model. The resulting on-line optimization problem is convex. ...
David Olson and Kurt Bretthauer of the Business Analysis Dept., Texas A&M University for sharing their chance-constrained optimization experience with the authors. ...
doi:10.1002/aic.690450811
fatcat:yxb5hyv7t5hsxcvkzvfrin7vcu
Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2106.02801v1 fatcat:okvcuwmzh5bsbfvdg7kh7wcvoq
checking with stochastic obstacle model for 3DOF and 6DOF robotic systems. ...
The first step is to derive a surrogate problem of deterministic nonlinear optimal control (DNOC) with convex constraints by using gPC expansion and the distributionally-robust convex subset of the chance ...
TRACKING CONTROL USING STOCHASTIC MODEL PREDICTIVE CONTROL
Algorithm 2 : 2 Stochastic Model Predictive Control. ...
arXiv:2106.02801v2
fatcat:2iuigzxcibb43dr22sy74rtli4
Constraint-Tightening and Stability in Stochastic Model Predictive Control
[article]
2016
arXiv
pre-print
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Constraint tightening to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control is addressed. ...
A numerical example, demonstrating the efficacy of the proposed approach in comparison with classical, recursively feasible Stochastic MPC and Robust MPC, is provided. ...
PROBLEM SETUP In this section, we first describe the system to be controlled and introduce the basic Stochastic Model Predictive Control algorithm.
A. ...
arXiv:1511.03488v2
fatcat:zeop56otnzc3njmnmzxaqsluja
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 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 propose a stochastic MPC scheme using an optimization over the initial state for the predicted trajectory. ...
Both of these come with significant change to the interpretation of the chance constraints, either by considering the open-loop prediction, i.e., conditioning on the initial state of the system, or by ...
arXiv:2203.01844v2
fatcat:v352kvcqyfdb5goemp6rduqwjm
Recursively feasible stochastic predictive control using an interpolating initial state constraint – extended version
[article]
2022
arXiv
pre-print
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2022 accepted arXiv:2203.01073v2 fatcat:z3h5azzyrbhrtll3okpwcjmnga
We present a stochastic model predictive control (SMPC) framework for linear systems subject to i.i.d. additive disturbances. ...
State of the art SMPC approaches with closed-loop chance constraint satisfaction recursively initialize the nominal state based on the previously predicted nominal state or possibly the measured state ...
We study linear systems with possibly unbounded disturbances subject to chance constraints on states and inputs. ...
arXiv:2203.01073v1
fatcat:dys4c7ynlnd35kb4ba5xm6hbsy
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