Stochastic model predictive control: Insights and performance comparisons for linear systems

Serón, María M.; Goodwin, Graham C.; Carrasco, Diego S.

doi:10.1002/rnc.4106

Cited by 11 publications

(10 citation statements)

References 35 publications

(50 reference statements)

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“…Actually, Robust MPC and average Stochastic MPC based on linear models are closely related. 30 Now, consider that w k is a random variable generated by a stochastic process W k with a given distribution. It is assumed that w k has a bound support such that w k ∈ W, ∀k ≥ 0.…”

Section: Stochastic Mpc With Individual Chance Constraintsmentioning

confidence: 99%

Robust MPC for linear systems with bounded disturbances based on admissible equilibria sets

Santos

Cunha

2021

Intl J Robust & Nonlinear

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This article presents a robust model predictive control (MPC) for piecewise constant reference tracking based on a constrained linear model and a terminal constraint defined from the admissible equilibria set. The new robust MPC algorithm based on nominal predictions ensures recursive feasibility and convergence to an optimal target, but the terminal constraint is derived from an admissible equilibria set with a suitable disturbance translation. The computation of the proposed terminal constraint is simple because no subset verification is required and the number of half-spaces of the proposed terminal constraint is fixed a priori. A zonotope disturbance bound description is used to simplify the computation of the admissible equilibria. Furthermore, it is shown that the proposed strategy can be directly extended to other MPC algorithms based on artificial targets, as a stochastic MPC based on the individual chance constraints, for instance. Two case studies are used to illustrate the usefulness of the proposed robust MPC for piecewise constant set-point tracking based on nominal predictions.

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Section: Stochastic Mpc With Individual Chance Constraintsmentioning

confidence: 99%

Robust MPC for linear systems with bounded disturbances based on admissible equilibria sets

Santos

Cunha

2021

Intl J Robust & Nonlinear

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“…The fourth paper studies several instances of both deterministic and stochastic MPC for linear systems in substantial depth. Explicit computations of the associated control laws indicate that, under certain conditions, the deterministic and stochastic formulations lead to identical results, thus providing new insights into the performance of several variants of stochastic MPC considered in the literature.…”

Section: Overview Of Articlesmentioning

confidence: 99%

Stochastic predictive control

Quevedo

Chatterjee

2019

Intl J Robust & Nonlinear

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“…A similar role in social networks is played by stubborn agents [Yildiz et al, 2013, Parsegov et al, 2017. The clustered structure of a network substantially simplifies its structure as a control system and enables relatively simple (low-dimensional) controllers, which can operate in uncertain conditions, as exemplified by modern methods of robust and stochastic model predictive control (MPC) [Mayne et al, 2000, Mesbah, 2018, Seron et al, 2018, adaptive control [Fradkov et al, 1999, Fradkov, 2007, data-driven and learning-based control [Benosman, 2018].…”

Section: Clusters and Control Of Networkmentioning

confidence: 99%

Evolution of clusters in large-scale dynamical networks

Proskurnikov

Granichin

2018

CAP

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Recent tremendous progress in electronics, complexity theory and network science provides new opportunities for intellectual control of complex large-scale systems operating in turbulent environment via networks of interconnected miniature devices, serving as actuators, sensors and data processors. Actual dynamics of the resulting control systems are too sophisticated to be examined controlled by traditional methods, which primarily deal with ordinary differential equations. However, their complexity can be dramatically reduced by fast processes, organizing the elementary units of the system (called agents) into relatively small number of clusters. The clusters emerge and deteriorate in response to changes in the environment, and the processes of their formation and destruction are very short in time. During the periods of the clusters’ existence, the system’s dynamics is essentially low-dimensional due to synchronization between the agents in each cluster. An enormously complicated system is thus reduced to a finite-dimensional model with time-varying structure of the state vector. The low-dimensionality of the reduced model allows to control it by using classical methods, e.g. model-predictive or adaptive control. This philosophy of complex systems control is illustrated on an experimental setup, called the “airplane with feathers”. The wings of this airplane are equipped with arrays of microsensors, microcomputers, and microactuators (“feathers”). The feathers self-organize into clusters by using a multi-agent consensus protocol; the aim of this coordination is to reduce the perturbing forces, affecting the airplane in a turbulent flow.

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Stochastic model predictive control: Insights and performance comparisons for linear systems

Cited by 11 publications

References 35 publications

Robust MPC for linear systems with bounded disturbances based on admissible equilibria sets

Robust MPC for linear systems with bounded disturbances based on admissible equilibria sets

Stochastic predictive control

Evolution of clusters in large-scale dynamical networks

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