Stochastic tube MPC with state estimation

Cannon, Mark; Cheng, Qifeng; Kouvaritakis, B.; Raković, Sas̆a V.

doi:10.1016/j.automatica.2011.08.058

Cited by 102 publications

(69 citation statements)

References 16 publications

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“…The introduction of state estimation into RMPC is well understood and uses lifting to describe the combined system and observer dynamics. In [85], these ideas are extended to include probabilistic information on measurement noise and the unknown initial plant state, and extends the approach of [84].…”

Section: Earlier Workmentioning

confidence: 99%

Discrete-Time Model Predictive Control

Li¹,

Xia²,

Fu³

et al. 2012

Advances in Discrete Time Systems

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Section: Earlier Workmentioning

confidence: 99%

Discrete-Time Model Predictive Control

Li¹,

Xia²,

Fu³

et al. 2012

Advances in Discrete Time Systems

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“…Output feedback [45], [52], [73], [77] Optimization algorithm Convex Qp/sOCp [43], [44], [47], [50]- [53], [55], [89], [147] sDp [69] stochastic programing [57], [58], [91] nonconvex optimization [31]- [35], [63]- [65], [120] in [43] such that the centers and scalings of the cross sections could vary with time [13], [15]. These stochastic tubes can be computed offline with respect to the states that guarantee satisfaction of chance constraints and recursive feasibility.…”

Section: Stochastic Tube Approachesmentioning

confidence: 99%

“…To obtain a computationally efficient algorithm, stochastic tubes with fixed ellipsoidal cross sections are used [59], [61], [62], [69], [73], [89], [91], [92], [147] nonlinear [31]- [35], [63]- [65], [110], [120] Type of uncertainties Time-varying uncertainties Time invariant uncertainties [33], [34], [65] Time-varying/invariant multiplicative uncertainties with additive disturbances [26], [42], [54], [59], [62], [89] additive [43]- [45], [47]- [53], [55], [56], [61], [73], [92], [147] Multiplicative [57], [58], [69] uncertainty propagation stochastic tube [26], [42]- [46] scenario/sample based [31], [32], [56], [59]- [63], [92], [93] gpC/Fp/gM …”

Section: Stochastic Tube Approachesmentioning

confidence: 99%

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

2016

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Stochastic Model Predictive ControlM 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 objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions { , , , } u u uN

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“…The occurrence of probabilistic constraints mainly arises from two reasons: one is that random uncertainty may have infinitely support such that constraints will be impossible to be obeyed with a probability of 100%, and the other is that probabilistic constraints can lead to considerably better performance when the corresponding deterministic constraints are over conservative. There are several interesting progresses on the research of the theory of SMPC recently (Cannon et al 2011;Oldewurtel et al 2013;Calafiore and Fagiano 2013;Kouvaritakis et al 2010;Cannon et al 2012), which readers can refer to.…”

Section: Introduction Of Smpcmentioning

confidence: 99%

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

Cheng

Ning

Xia

et al. 2015

International Journal of Production Research

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In the paper, a typical coal trade process is described and modelled, where one logistics enterprise with blending equipments lies in the core and two types of common contracts are elucidated to define constraints. A mixed-integer model is built and featured by addressing contract violation, blending operation, real time price information and arbitrarily distributed stochastic demands. To deal with the stochastic demands, probabilistic constraints are formed. Accordingly, stochastic model predictive control (SM-PC) strategy with both receding horizon and decreasing horizon formulations is developed to handle the probabilistic constraints and exploit the value of newest price information. By solving a series of mixed-integer linear programs, optimal coal trade decisions for the logistics enterprise can be obtained, including procurement decision, selling decision and operational decision of the blending equipments. Thorough simulation experiments are carried out and compared under three different strategies, which interpret the effectiveness of the proposed strategy.

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Stochastic tube MPC with state estimation

Cited by 102 publications

References 16 publications

Discrete-Time Model Predictive Control

Discrete-Time Model Predictive Control

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

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