Model predictive control techniques for hybrid systems

Camacho, Eduardo F.; Ramı́rez, D.R.; Limón, Daniel; Peña, David Muñoz de la; Álamo, T.

doi:10.1016/j.arcontrol.2010.02.002

Cited by 145 publications

(57 citation statements)

References 93 publications

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“…gives the output bound for all the N subnetworks N i2 OE ; M is related with the Taylor expansion approximation error, which is defined in (25); ı is the upper bound of the neural networks approximation errors defined in (31); is the weighting factor for the predictive control optimization problem in (32); and D max ¹jU min j; jU max jº ; L D U max U min (40) denote the magnitude and the length of the operating range, respectively.…”

Section: Theoremmentioning

confidence: 99%

“…In the first step, sub-controllers are designed for each submodel on the basis of various linear control techniques. By far, the most popular control methodology for PWA systems is the multiple model predictive control [24][25][26][27][28][29][30][31]. Different from the conventional model predictive control for a single model, where the control signal is computed by minimizing a cost function that penalizes the future output tracking error and the variation in control signal, the switching among different local submodels and their corresponding controllers also need to be taken into consideration.…”

mentioning

confidence: 99%

“…where the second inequality can be derived by simply substituting (47) into (52) and the last inequality follows from the fact that p a 2 C b 2 6 a C b; 8a > 0; 8b > 0. Because we do not know what j and l exactly are, we can further relax the upper bound in (52) using definitions given in (39), (31), (40), and (25) to get rid of j and l. It is obvious thať j 2 6ˇ2; ı j1 6 ı; ı j 2 6 ı; ı l1 6 ı; ı l2 6 ı; j 6 ; l 6 ; M j 6 M; M l 6 M (53)…”

mentioning

confidence: 99%

See 2 more Smart Citations

Data‐driven identification and control of nonlinear systems using multiple NARMA‐L2 models

Yue

Chen

Gao

et al. 2017

Intl J Robust & Nonlinear

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Summary The multiple model approach provides a powerful tool for identification and control of nonlinear systems. Among different multiple model structures, the piecewise affine (PWA) models have drawn most of the attention in the past two decades. However, there are two major issues for the PWA model‐based identification and control: the curse of dimensionality and the computational complexity. To resolve these two issues, we propose a novel multiple model approach in this paper. Different from PWA models in which all dimensions of the regressor space are engaged in the partitioning, the key idea of the proposed multiple model architecture is to partition only the range of the control input u(k) at time k (the instant of interest in the control problem) into several intervals and identify a local model that is linear in u(k) within each interval. On the basis of Taylor's theorem, a theoretical upper bound for the approximation error of the model structure can also be obtained. With the proposed multiple model architecture, a switching control algorithm is derived to control nonlinear systems on the basis of the weighted one‐step‐ahead predictive control method and constrained optimization techniques. In addition, the upper bound for the tracking error using this switching control strategy is also analyzed rigorously under certain assumptions. Finally, both simulation studies and experimental results demonstrate the effectiveness of the proposed multiple model architecture and switching control algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Theoremmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Data‐driven identification and control of nonlinear systems using multiple NARMA‐L2 models

Yue

Chen

Gao

et al. 2017

Intl J Robust & Nonlinear

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“…8. Since output predictions are obtained by means of a PWA model, we have the so called Hybrid Model Predictive Control (HMPC) [14]. Such control law is defined by the following nonlinear programming problem, with nonlinear constraints, where ߠ are the parameters for the PWA model.…”

Section: Hybrid Model Predictive Control For Pwa Systemsmentioning

confidence: 99%

Hybrid Model Predictive Control as a LFC solution in Hydropower Plants

Donaisky

Oliveira

Santos

et al. 2015

MATEC Web of Conferences

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Abstract. For Electric Power System safety and stable operation, planning and analysis by using simulation environments are necessary. An important point for frequency stability analysis is, on one hand, an adequate representation of Load-Frequency Control (LFC) loops and, on the other hand, the design of advanced control strategies to deal with the power system dynamic complexity. Therefore, in this paper we propose to represent the group turbine/penstock, found in hydropower plants, in a Piecewise Affine (PWA) modelling structure. Based on such modelling, we also propose the use of a Hybrid Model Predictive algorithm to be use as a control law in LFC loops. Among the advantages of this PWA representation is the use of this model in the controller algorithm, thereby improving the Load-Frequency Control performance. Simulation results, on a 200 MW hydropower plant compares the performance of predictive control strategy presented with the classical PID control strategy in an isolated condition.

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“… h 1 , h 2 , h 3 -levels in respective tanks  S 1 ,S 2 ,S 3 -cross sections of tanks (tanks dimensions are equal (=S))  S 13 , S 23 -cross section of digital valves between tanks  S 01 , S 02 -cross section of Valves  q 1 , q 2 -inflow through pumps  V 13 ,V 23 -status of digital valves between tanks (0-closed, 1-opened)…”

Section: Hybrid 3-tank Systemmentioning

confidence: 99%