Stochastic Model Predictive Control of constrained linear systems with additive uncertainty

Magni, Lalo; Pala, Daniele; Scattolini, Riccardo

doi:10.23919/ecc.2009.7074737

Cited by 18 publications

(19 citation statements)

References 9 publications

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“…Our next aim is to remove averages in the cost function (49). For this purpose, we proceed as described in [16]. We recall the following properties.…”

Section: A Derivation Of the Smpc Problem (27)-(37)mentioning

confidence: 99%

“…As a whole, an upper bound to the cost function in (26a) is provided by the cost in (27). Our last aim is to account for probabilistic input constraints (26f) using the procedure proposed in [16]. In the following, for simplicity of notation, we neglect the index s and the time k appearing in (26f).…”

Section: A Derivation Of the Smpc Problem (27)-(37)mentioning

confidence: 99%

“…Wind profiles for v 0 = 20 and T I = 0.1: in blue the validation set and in red the predicted profile obtained using(16).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Model Predictive Controllers for Reduction of Mechanical Fatigue in Wind Farms

Riverso

Mancini

Sarzo

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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We consider the problem of dispatching WindFarm (WF) power demand to individual Wind Turbines (WT) with the goal of minimizing mechanical stresses. We assume wind is strong enough to let each WTs to produce the required power and propose different closed-loop Model Predictive Control (MPC) dispatching algorithms. Similarly to existing approaches based on MPC, our methods do not require changes in WT hardware but only software changes in the SCADA system of the WF. However, differently from previous MPC schemes, we augment the model of a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty in wind predictions over the MPC control horizon. This allows us to develop both stochastic and deterministic MPC algorithms. In order to compare different MPC schemes and demonstrate improvements with respect to classic open-loop schedulers, we performed simulations using the SimWindFarm toolbox for MatLab. We demonstrate that MPC controllers allow to achieve reduction of stresses even in the case of large installations such as the 100-WTs Thanet offshore WF.

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“…Our next aim is to remove averages in the cost function (49). For this purpose, we proceed as described in [16]. We recall the following properties.…”

Section: A Derivation Of the Smpc Problem (27)-(37)mentioning

confidence: 99%

Section: A Derivation Of the Smpc Problem (27)-(37)mentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Controllers for Reduction of Mechanical Fatigue in Wind Farms

Riverso

Mancini

Sarzo

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…inviolable), and it may be possible to improve performance by tolerating violations of constraints providing that the frequency of violations remains within allowable limits, namely soft constraints (see e.g. [69,70] or [71,73]). …”

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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“…In this paper, an output feedback algorithm for linear discrete-time systems affected by a possibly unbounded additive noise is proposed. In case the noise distribution is unknown, the chance constraints on the inputs and state variables are reformulated by means of the Chebyshev -Cantelli inequality [25], as originally proposed in [20] for the design of decentralized controllers and in [22] in the context of MPC. Later, this approach has also been considered in [36,12], and used to develop preliminary versions of the algorithm here proposed in [9,10].…”

Section: Introductionmentioning

confidence: 99%

An approach to output-feedback MPC of stochastic linear discrete-time systems

et al. 2015

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In this paper we propose an output-feedback Model Predictive Control (MPC) algorithm for linear discrete-time systems affected by a possibly unbounded additive noise and subject to probabilistic constraints. In case the noise distribution is unknown, the probabilistic constraints on the input and state variables are reformulated by means of the Chebyshev -Cantelli inequality. The recursive feasibility is guaranteed, the convergence of the state to a suitable neighbor of the origin is proved under mild assumptions, and the implementation issues are thoroughly addressed. Two examples are discussed in details, with the aim of providing an insight into the performance achievable by the proposed control scheme.

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Stochastic Model Predictive Control of constrained linear systems with additive uncertainty

Cited by 18 publications

References 9 publications

Model Predictive Controllers for Reduction of Mechanical Fatigue in Wind Farms

Model Predictive Controllers for Reduction of Mechanical Fatigue in Wind Farms

Discrete-Time Model Predictive Control

An approach to output-feedback MPC of stochastic linear discrete-time systems

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