Model predictive control relevant identification: multiple input multiple output against multiple input single output

Lauri, David; Salcedo, J. V.; García-Nieto, Sergio; Martínez, Miguel Á.

doi:10.1049/iet-cta.2009.0482

Cited by 30 publications

(18 citation statements)

References 17 publications

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“…Huang & Wang (1999) extended the previous method, so that a general model structure (e.g., Box-Jenkins) could be employed. Some authors, such as (Gopaluni et al, 2003;Laurí et al, 2010), deal with the parameter estimation problem directly minimizing the MRI cost function, using nonlinear optimization techniques. In another approach, proposed by Gopaluni et al (2004), the focus is given to the noise model parameter estimation.…”

Section: Model Parameter Estimation Methodsmentioning

confidence: 99%

“…The main of them is that algorithms developed for the SISO (single-input single-output) processes can be directly generalized for the multivariable case. Nevertheless, if there are dynamic iterations between different outputs, the estimated model based on the diagonal form can present a larger bias error (Laurí et al, 2010). Alternatively, one can add elements outside the diagonal of F(q) , not necessarily monic polynomials, with the purpose of incorporating the dynamic iteration between the process outputs.…”

Section: Model Parameterizationmentioning

confidence: 99%

“…On the other hand, the model parameterization should be chosen carefully, to minimize numerical problems in the nonlinear optimization algorithm. In Laurí et al (2010) a "full-polynomial 2 form" ARX model…”

Section: Direct Optimization Of the Cost Functionmentioning

confidence: 99%

See 2 more Smart Citations

Model Predictive Control Relevant Identification

Romano¹,

Potts²,

Cortés³

2012

Frontiers in Advanced Control Systems

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Section: Model Parameter Estimation Methodsmentioning

confidence: 99%

Section: Model Parameterizationmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Control Relevant Identification

Romano¹,

Potts²,

Cortés³

2012

Frontiers in Advanced Control Systems

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“…The main advantages of the MPC are that the constraints may be explicitly specified into the problem formulation and the measured disturbances can be taken into consideration [15][16][17][18]. Constraints consist of ranges of possible MPC input-output due to manipulated variables, physical limitations, operating procedures or safety reasons, etc.…”

Section: 1) Lfc By Use Of Evsmentioning

confidence: 99%

“…The conventional MPC method is based on the current measurements and predictions of the future outputs [15][16][17][18][19]. The objective of the MPC is to determine a sequence of the control moves i.e., the manipulated input variable, so that the predicted response moves to the set point in an optimal manner.…”

Section: Overview Of Mpcmentioning

confidence: 99%

Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

Pahasa¹,

Ngamroo²

2017

Journal of Electrical Engineering and Technology

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-This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective modelbased prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

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Adaptive control and signal processing literature survey (No. 21)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk. 1. ADAPTIVE SYSTEMS Adetola V, Guay M. Performance improvement in adaptive control of linearly parameterized nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(9):2182-2186. Bartoszewicz A, Nowacka-Leverton A. ITAE optimal sliding modes for third-order systems with input signal and state constraints. IEEE Transactions on Automatic Control 2010; 55(8):1928-1932. Bresch-Pietri D, Krstic M. Delay-adaptive predictor feedback for systems with unknown long actuator delay. IEEE Transactions on Automatic Control 2010; 55(9):2106-2112. Chen W, Jiao L, Li R, Li J. Adaptive backstepping fuzzy control for nonlinearly parameterized systems with periodic disturbances. IEEE Transactions on Fuzzy Systems 2010; 18(4):674-685. Cheng C-C, Chien S-H, Shih F-C. Design of robust adaptive variable structure tracking controllers with application to rigid robot manipulators. Immersion and invariance adaptive control of nonlinearly parameterized nonlinear systems. IEEE Transactions on Automatic Control 2010; 55(9):2209-2214. Miller D, Mansouri N. Model reference adaptive control using simultaneous probing, estimation, and control. IEEE Transactions on Automatic Control 2010; 55(9):2014-2029. Mizumoto I, Ohdaira S, Iwai Z. Output feedback strict passivity of discrete-time nonlinear systems and adaptive control system design with a PFC. Automatica 2010; 46(9):1503-1509. Moreno-Valenzuela J, Santibáñez V, Orozco-Manrııquez E, González-Hernández L. Theory and experiments of global adaptive output feedback tracking control of manipulators. IET Control Theory and Applications 2010; 4(9):1639-1654. Plestan F, Shtessel Y, Brégeault V, Poznyak A. New methodologies for adaptive sliding mode control. International Journal of Control 2010; 83(9):1907-1919.Reinhartz-Berger I, Soffer P, Sturm A. Extending the adaptability of reference models.

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Model predictive control relevant identification: multiple input multiple output against multiple input single output

Cited by 30 publications

References 17 publications

Model Predictive Control Relevant Identification

Model Predictive Control Relevant Identification

Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

Adaptive control and signal processing literature survey (No. 21)

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