Generalized predictive control tuning by controller matching

Tran, Quang Nhat; Octaviano, Ryvo; Özkan, Leyla; Backx, A.C.P.M.

doi:10.1109/acc.2014.6858951

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…It tackles the output error in the case of steady-state at the cost of a higher computational demand. An alternative to the DMC approach is represented by the Generalized Predictive Control (GPC), adopted in the study by Ref (Zambelli et al, 2012;Errouissi et al, 2016;Tran et al, 2015;Brown et al, 2013). This is a robust adaptive control algorithm but it presents strong limitations in dealing with non-linear systems.…”

Section: Introductionmentioning

confidence: 99%

State feedback predictive control for nonlinear hydro-turbine governing system

Yi¹,

Zhiwang²,

Chen

et al. 2017

Journal of Vibration and Control

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The present work introduces a novel predictive control strategy for the analysis of the dynamic performance of hydro-turbine governing systems based on fuzzy logic. Firstly, a six-dimensional non-linear dynamic model of the system is defined. The defined model is applied to a realistic case-study, aiming to investigate the dynamic behavior of the system. In order to deal effectively with the non-linearity of the system under study, the T-S fuzzy approach is adopted. The results demonstrated through the use of the discrete Lyapunov function and Schur complements of matrices suggest that the closed-cycle control system can achieve a global asymptotic stability state. The second part focuses on the quantification of the impact of sudden changes in operating conditions on the overall performance. The numerical results indicate that proposed predictive control method can ensure the performance of the system to be reliable and robust to external inferences. In addition to this, the approach proposed has unquestionable advantages over the traditional PID and model predictive controllers with regard to non-linear systems applications.

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Section: Introductionmentioning

confidence: 99%

State feedback predictive control for nonlinear hydro-turbine governing system

Yi¹,

Zhiwang²,

Chen

et al. 2017

Journal of Vibration and Control

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“…This can be done "by hand" based on intuition, or by gridding a limited number of parameters and analysing simulations (the approach in [17]), or using global optimisation routines to tune the cost weights to minimise a high-level heuristic functions evaluated over closed-loop simulations (as in [23]). If a good linear control law is already known and the requirement is simply to "upgrade" it with constraint handling, controller matching or reverse-engineering can be applied [50]- [52]. The VH examples use T x s , and are tuned so that convergence happens in approximately half an orbit.…”

Section: Cost Function Structure and Tuningmentioning

confidence: 99%

A tutorial on model predictive control for spacecraft rendezvous

Hartley

2015

2015 European Control Conference (ECC)

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This tutorial paper provides a review of recent advances in the field of spacecraft rendezvous using model predictive control (MPC), an advanced optimal control strategy based on on-line constrained optimisation of control inputs based on predictions of future trajectories. Firstly, the rendezvous objectives, and the generic constrained MPC problem formulation are summarised. This is followed by a discussion of how to select the three key ingredients used in an MPC design: the prediction model, the constraints and the cost function. Since MPC implementation relies on finding the solution to constrained optimisation problems in real-time, computational aspects are also briefly examined. The paper concludes with conjecture on ways the use of MPC in this application could be further advanced.

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“…Hartley and Maciejowski [9] discussed the condition where all the states of the system are not available, and the loop-shaping technique is used to overcome the drawbacks of the earlier proposed controller-matching technique of MPC tuning. Tran et al [10] have tried to achieve exact matching of GPC with the LTI controller which needs to satisfy a certain rank condition. The optimisation problem for the tuning of the weighting matrix is complicated for active constraints.…”

Section: Introductionmentioning

confidence: 99%

Pre‐computed GPC of a DC–DC buck converter

Manjari

Majhi

Ramana

2019

IET Circuits, Devices & Systems

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This study presents the control mechanism of a DC-DC buck converter using generalised predictive control by approximating the plant model to a stable first-order plus dead time (FOPDT) model. As most of the higher order plant dynamics can be represented by an FOPDT model, this study is limited only to a stable FOPDT dynamics system. In this work, a relation between FOPDT model parameters and weights of the cost function is derived. The controller design procedure is then verified by implementing voltage mode control of the DC-DC buck converter in the case of inactive constraints. Jury's stability criteria are used to analyse the stability of the closed-loop system. The implemented algorithm is then verified using simulation in MATLAB/Simulink, as well as the hardware implementation of the pre-computed control algorithm.

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Generalized predictive control tuning by controller matching

Cited by 12 publications

References 19 publications

State feedback predictive control for nonlinear hydro-turbine governing system

State feedback predictive control for nonlinear hydro-turbine governing system

A tutorial on model predictive control for spacecraft rendezvous

Pre‐computed GPC of a DC–DC buck converter

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