Research on probabilistic methods for control system design

Calafiore, Giuseppe Carlo; Dabbene, Fabrizio; Tempo, Roberto

doi:10.1016/j.automatica.2011.02.029

Cited by 156 publications

(115 citation statements)

References 86 publications

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“…2) A scenario-based method, such as [16], [17],which evaluates the chance constraint by sampling. The first approach is computationally efficient but requires relatively strong assumptions on the probability distribution of w k .…”

Section: A General Formulation Of Ccmpcmentioning

confidence: 99%

Risk-limiting power grid control with an ARMA-based prediction model

Ono

Topcu

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-This paper is concerned with the risk-limiting operation of electric power grids with stochastic uncertainties due to, for example, demand and integration of renewable generation. The main contribution is incorporating autoregressive-moving-average (ARMA) type prediction models for the underlying uncertainties into chance-constrained, finitehorizon optimal control. This uncertainty model leads to a more (compared to existing work in literature) careful treatment of correlation in time which is significant especially in renewable generation yet has attracted limited attention. The paper first discusses how the resulting chance-constrained optimization problems can be solved computationally and demonstrates the effects of the use of the proposed prediction models through simulation-based case studies with realistic data.

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Section: A General Formulation Of Ccmpcmentioning

confidence: 99%

Risk-limiting power grid control with an ARMA-based prediction model

Ono

Topcu

et al. 2013

52nd IEEE Conference on Decision and Control

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“…After each observation, the CUSUM statistic for each region gives the likelihood of an anomaly in that region. We utilize this likelihood to design the weights of the cost functions in optimization problem (3). This ensures that the region with high likelihood of an anomaly is surveyed with a higher probability.…”

Section: B Adaptive Policymentioning

confidence: 99%

Stochastic surveillance strategies for spatial quickest detection

Srivastava

Bullo

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-We present stochastic vehicle routing policies for detection of any number of anomalies in a set of regions of interest. The autonomous vehicle collects information from a set of regions and sends it to a fusion center. The vehicle follows a randomized region selection policy at each iteration. Using the collected information, the fusion center runs an ensemble of cumulative sum (CUSUM) algorithms in order to detect the presence of an anomaly in any region. We first determine optimal stationary policies that result in quickest detection of all anomalies. We then study an adaptive policy that assigns higher selection probability to a region with higher likelihood of an anomaly. We provide a comparative study of these policies.

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“…On the other hand, stochastic MPC considers a more realistic description of uncertainty, which leads to less conservative control approaches at the expense of a more complex modelling of the disturbances. The stochastic approach is a mature theory in the field of optimization [3], but a renewed attention has been given to the stochastic programming techniques as powerful tools for control design (see, e.g., [4] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control for Water Transport Networks with Demand Forecast Uncertainty

Grosso

Ocampo‐Martínez

Puig³

2017

Advances in Industrial Control

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One of the challenges in water system management is the existence of different sources of uncertainty. The availability of historical data allows to accurately predict the behaviour of the system disturbances over large horizons, but still a meaningful degree of uncertainty is present. In previous chapters, the use of MPC to tackle the complex multi-variable interactions and large-scale nature of drinking-water network control is proposed. There are several examples of MPC applied to water systems in the literature, see, e.g., [2,7,10,16, 29, 30] and references therein.In a DWN, the main management purpose is the achievement of the highest level of consumer satisfaction and service quality in line with the prevailing regulatory framework, while making best use of available resources. Hence, networks must be reliable and resilient while being subject to constraints and to continuously varying conditions with both deterministic and probabilistic nature. Customer behaviour determines the transport and storage operations within the network, and flow demands can vary in both the long and the short term, often presenting time-based patterns in some applications. Therefore, a better understanding and forecasting of demands will improve both modelling and control of DWNs.While Chaps. 12 and 13 deal with the uncertainty in the classical way of feedforward action, this chapter focuses on the way that uncertainty can be faced by using stochastic-based approaches. The simplest way to do this is by ignoring the explicit influence of disturbances or using their expected value as done in the previous chapters. However, dealing with the demand uncertainty explicitly in the control model is expected to produce more robust control strategies. In [12], a reliability-based

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Research on probabilistic methods for control system design

Cited by 156 publications

References 86 publications

Risk-limiting power grid control with an ARMA-based prediction model

Risk-limiting power grid control with an ARMA-based prediction model

Stochastic surveillance strategies for spatial quickest detection

Stochastic Model Predictive Control for Water Transport Networks with Demand Forecast Uncertainty

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