Conditions for which linear MPC converges to the correct target

Shead, Leo; Muske, Kenneth R.; Rossiter, J.A.

doi:10.1016/j.jprocont.2010.09.001

Cited by 28 publications

(8 citation statements)

References 15 publications

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“…Thus, such factors could cause the optimisation (6.8) infeasible. In the literature, there are many studies regarding the problems of infeasibility in MPC, for example, the use of constraint-softening strategies (Scokaert & Rawlings, 1999;Zeilinger et al, 2014), steady-state target calculator (Rawlings et al, 2008;L. R. E. Shead et al, 2010).…”

Section: Discussion On Stability and Feasibilitymentioning

confidence: 99%

Blade-Pitch Control for Wind Turbine Load Reductions

Lio

2018

Springer Theses

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Large wind turbines are subjected to the harmful loads that arise from the spatially uneven and temporally unsteady oncoming wind. Such loads are the known sources of fatigue damage that reduce the turbine operational lifetime, ultimately increasing the cost of wind energy to the end users. In recent years, a substantial amount of studies has focused on blade pitch control and the use of real-time wind measurements, with the aim of attenuating the structural loads on the turbine blades and rotor. I also would like to thank my colleagues for some interesting discussions that helped my

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Section: Discussion On Stability and Feasibilitymentioning

confidence: 99%

Blade-Pitch Control for Wind Turbine Load Reductions

Lio

2018

Springer Theses

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“…in which x ∈ R n is the plant state, d nd ∈» is the plant disturbance, and v ∈ R p is a zero-mean white noise. Let We can combine the plant (8) and the MPC system (7) to obtain a description of the overall (plant and model) system:…”

Section: Plant Evolutionmentioning

confidence: 99%

Prediction Error Based Performance Monitoring, Degradation Diagnosis and Remedies in Offset‐Free MPC: Theory and Applications

Pannocchia¹,

Luca²,

Bottai³

2013

Asian Journal of Control

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We present in this paper a control performance monitoring method for linear offset-free model predictive control (MPC) algorithms, in which the prediction error sequence is used to detect whether the internal model and the observer are correct or not. When the prediction error is a white noise signal, revealed by the Ljung-Box test, optimal performance is detected. Otherwise, we use a closed-loop subspace identification approach to reveal the order of a minimal realization of the system from the deterministic input to the prediction error. When such order is zero, we prove that the model is correct and the source of suboptimal performance is an incorrect observer. In such cases, we suggest an optimization method to recalculate the correct augmented state estimator. If, instead, such order is greater than zero we prove that the model is incorrect, and re-identification is suggested. A variant for (large-scale) block-structured systems is presented, in which diagnosis and corrections are performed separately in each block. Two examples of different complexity are presented to highlight effectiveness and scalability of the method.

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“…Research on the NMSS model-based control has been reported in many different areas (Young and Willems 1972;Young, Behzadi, Wang, and Chotai 1987;Young, Lee, Chotai, Tych, and Chalabi 1994;Chotai, Young, Mckenna and Tych 1998;Lee, Taylor, Young and Chotai 1998;Taylor, McCabe, Young and Chotai 2000;Young 2000, 2001;Maciejowski 2002;Pannocchia and Rawlings 2003;Taylor and Shaban 2006;Shead, Muske, and Rossiter 2010;Dubljevic 2010;Grossmann, Stro¨hlein, Morari, and Morbidelli 2010;Gonzalez, Adam, Marcovecchio, and Odloak 2011;Jacob and Dhib 2011;Scheu and Marquardt 2011). It is one of the most widely recognised approaches for controller design.…”

Section: Introductionmentioning

confidence: 99%

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Zhang

Xue

Wang

et al. 2012

International Journal of Control

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Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method. IntroductionModel predictive control (MPC) has been a very active research area in both control theory and control engineering since it first appeared in the 1970s. The main focus is on the three most general MPC design methods: finite impulse response (FIR) or step response models, transfer function models and state-space models. The earlier FIR model-based MPC represented by dynamic matrix control (DMC) (Cutler and Ramaker 1980) and quadratic DMC (Garcia and Morshedi 1986) can only deal with stable processes and the design model order is usually high. The representative article of transfer function model-based MPC can be seen in generalised predictive control (GPC) by Clarke, Mohtadi, and Tuffs (1987), which can deal with both stable and unstable processes. The main disadvantage is that it is considered to be less effective for multi-variable processes and real-time application. The third category is MPC based on state-space models, and it has attracted much interest from researchers since it can effectively use the states' information for state feedback, constraints consideration and even for providing important information for the controlled process. A number of state-space model-based predictive control methods have been developed

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Conditions for which linear MPC converges to the correct target

Cited by 28 publications

References 15 publications

Blade-Pitch Control for Wind Turbine Load Reductions

Blade-Pitch Control for Wind Turbine Load Reductions

Prediction Error Based Performance Monitoring, Degradation Diagnosis and Remedies in Offset‐Free MPC: Theory and Applications

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

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