Generalized predictive control tuning by controller matching

Tran, Nhat Quang; Özkan, Leyla; Backx, Acpm Ton

doi:10.1016/j.jprocont.2014.10.002

Cited by 30 publications

(13 citation statements)

References 26 publications

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“…From a desired closed-loop bandwidth ω mpc , several methods can be used to find the tuning parameters Q and R of MPC. Rowe and Maciejowski (2000b), Shah and Engell (2011) and Tran et al (2014) aim at matching the MPC to a desired H ∞ controller while Lee and Yu (1994) tunes the Kalman filter and disturbance model to obtain the desired bandwidth. Tran et al (2012Tran et al ( ) andÖzkan et al (2012 show that the weighting factors of the input energy and output energy are correlated.…”

Section: Bottom Layer: From Closed-loop Bandwidth To Weighting Matricesmentioning

confidence: 99%

“…The de-tuning method is based on the analysis of the sensitivity and complementary sensitivity functions in the frequency domain. A number of works (Rowe and Maciejowski (2000b), Rowe and Maciejowski (2000a), Shah and Engell (2011) and Tran et al (2014)) investigated the matching of MPC to an H ∞ controller, so that the MPC can inherit the robustness of the H ∞ controller when constraints are inactive. On the other hand, Han et al (2006) used min-max algorithms when…”

Section: Introductionmentioning

confidence: 99%

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A model-free approach for auto-tuning of model predictive control

Tran

Scholten

Özkan

et al. 2014

IFAC Proceedings Volumes

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Section: Bottom Layer: From Closed-loop Bandwidth To Weighting Matricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model-free approach for auto-tuning of model predictive control

Tran

Scholten

Özkan

et al. 2014

IFAC Proceedings Volumes

Self Cite

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“…The GPC methods have been successfully applied in many industrial processes, such as active power filters, supercritical coal‐fired power, motor, quadcopter vehicle, and so on, and show good performance and a certain degree of robustness . Further theory research on GPC methods is constantly developing . All of these studies and applications indicate that the GPC methods possess many advantages, such as easily modelling, good dynamic control effects, strong robustness, and so on.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive decoupling control strategy for a gas flow facility based on active disturbance rejection generalized predictive control

Chen

Zhao

et al. 2018

Can J Chem Eng

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A comprehensive decoupling control (CDC) method, based on the active disturbance rejection generalized predictive control (ADRC-GPC) technology, is proposed in this paper for square multi-input multi-output (MIMO) systems. The central idea of this approach is that the total disturbance, which includes the partial coupling information among loops, the internal uncertainties, and the external disturbance, can be estimated by output through the extended state observer (ESO) and compensated in the feedback loop by feedback control law. Such estimating and cancelling can readily decouple the primal coupling system into multiple single-input single output (SISO) subsystems with the form of cascade integrators. Then the generalized predictive controller is designed for each subsystem. The proposed design is effectively used in the gas flow facility. Mathematical simulations and experiments show that compared with the proportional integral derivative (PID) method, the ADRC-GPC controller can achieve better dynamic performance, decoupling ability, disturbance rejection capability, and performance robustness.

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“…However, the GPC algorithm requires solved Diophantine equations and a matrix inversion operation, which led to excessive online calculation; on the other hand, since the time delay is random, the parameter selection for the GPC algorithm requires a larger prediction step size, thereby increasing the calculation time. Furthermore because the network delay is uncertain, the GPC algorithm requires a larger prediction step, thus increasing the computing time, reducing the real-time performance of the system [16].…”

Section: Introductionmentioning

confidence: 99%

Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

Tian¹,

Li²,

Wang³

et al. 2015

Algorithms

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Abstract:The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved.

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Generalized predictive control tuning by controller matching

Cited by 30 publications

References 26 publications

A model-free approach for auto-tuning of model predictive control

A model-free approach for auto-tuning of model predictive control

A comprehensive decoupling control strategy for a gas flow facility based on active disturbance rejection generalized predictive control

Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

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