Model predictive control for stochastic systems by randomized algorithms

Batina, I.

doi:10.6100/ir573294

2004

DOI: 10.6100/ir573294

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Model predictive control for stochastic systems by randomized algorithms

I. Batina¹

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Cited by 12 publications

References 105 publications

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Stochastic MPC with Imperfect State Information and Bounded Controls

Hokayem¹,

Cinquemani

Chatterjee³

et al. 2010

UKACC International Conference on CONTROL 2010

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This paper addresses the problem of output feedback Model Predictive Control for stochastic linear systems, with hard and soft constraints on the control inputs as well as soft constraints on the state. We use the so-called purified outputs along with a suitable nonlinear control policy and show that the resulting optimization program is convex. We also show how the proposed method can be applied in a receding horizon fashion. Contrary to the state feedback case, the receding horizon implementation in the output feedback case requires the update of several optimization parameters and the recursive computation of the conditional probability densities of the state given the previous measurements. Algorithms for performing these tasks are developed.

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Stochastic MPC with Imperfect State Information and Bounded Controls

Hokayem¹,

Cinquemani

Chatterjee³

et al. 2010

UKACC International Conference on CONTROL 2010

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Optimal, Robust Predictive Control of Nonlinear Systems under Probabilistic Uncertainty using Particles

Blackmore

Williams

2007

2007 American Control Conference

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In this paper we present a novel method for robust, optimal control of nonlinear systems under probabilistic uncertainty. The method extends a previous approach for linear systems that approximates the distribution of the predicted system state using a finite number of particles. We couple this particle-based approach with a nonlinear solver that does not take into account uncertainty to give a new method for nonlinear, robust control. Any solution returned by the algorithm is guaranteed to be ǫ-close to a local optimum of the nonlinear stochastic control problem.

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Adaptively constrained stochastic model predictive control applied to security constrained optimal power flow

Oldewurtel

Roald

Andersson

et al. 2015

2015 American Control Conference (ACC)

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The number of renewable energy sources in elec tricity grids is growing and with it the associated uncertainty. For power system operation a good tradeoff between secu rity and cost becomes increasingly important. We consider the problem of including uncertainty from renewable in-feed in a DC security constrained optimal power flow (SCOPF) by formulating chance constraints. The chance-constrained SCOPF is solved repeatedly and can be understood as a chance-constrained Model Predictive Control (MPC) problem, where the time coupling comes from storage in the system and the associated dynamics. The main focus of the paper is to reformulate the problem in a non-conservative way, i.e., to exploit the predefined violation level of the chance constraints based on their empirical closed-loop violations in order to achieve a good tradeoff between operational security and cost. We use and extend a recently proposed adaptive stochastic MPC scheme that starts with a standard conservative chance constrained reformulation and then adapts the formulation of the constraints online based on the empirical violation level.The proposed approach is demonstrated on a 5-bus network.

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Model predictive control for stochastic systems by randomized algorithms

Cited by 12 publications

References 105 publications

Stochastic MPC with Imperfect State Information and Bounded Controls

Stochastic MPC with Imperfect State Information and Bounded Controls

Optimal, Robust Predictive Control of Nonlinear Systems under Probabilistic Uncertainty using Particles

Adaptively constrained stochastic model predictive control applied to security constrained optimal power flow

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