An MPC approach for LPV systems in input-output form

Abbas, Hossam S.; Tóth, Roland; Meskin, Nader; Mohammadpour, Javad; Hanema, Jurre

doi:10.1109/cdc.2015.7402091

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…The online optimization problem involved is convex and can be solved by semidefinite programming tools to compute the optimal control action at each sampling instant. Overall, the proposed approach has overcome most of the critical issues of the works of Abbas et al, especially the computational complexity associated with the terminal cost and the offline controller. The performance of the proposed MPC scheme has been demonstrated on a simulation example of a MIMO CSTR system, showing its capability for reference tracking problems under operating points variation.…”

Section: Resultsmentioning

confidence: 95%

“…Accordingly, to apply the proposed MPC scheme, a discrete time LPV‐IO representation for the nonlinear description (71), in the operating region defined by the different levels of C 1 , is required. Such an LPV‐IO representation can be synthesized by either nonlinear to LPV conversion or system identification. An important disadvantage of the former way is that it delivers models suffering from a high level of model complexity in terms of nonlinear relationships, whereas the latter one appears to be attractive, to arrive at relatively simple descriptions of the plant.…”

Section: Numerical Examplementioning

confidence: 99%

“…However, it has been mainly formulated for LTI-IO models with few results guaranteeing stability, such as the infinite horizon generalized predictive control 18 or using the zero terminal set. 19 To cope with some of the critical issues discussed above, a robust MPC approach with a stability guarantee for LPV-IO models has been developed in the works of Abbas et al 20,21 Such a control approach enables the MPC control design directly based on the LPV-IO representations with constraints, for which only past values of the system output and input are required during implementation. The stability framework in the work of Mayne et al 13 has been considered in the work of Abbas et al, 21 which is based on 3 ingredients: a terminal cost, a terminal constraint set, and an offline controller.…”

Section: Introductionmentioning

confidence: 99%

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An improved robust model predictive control for linear parameter‐varying input‐output models

Abbas

Hanema

Tóth

et al. 2017

Intl J Robust & Nonlinear

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Summary This paper describes a new robust model predictive control (MPC) scheme to control the discrete‐time linear parameter‐varying input‐output models subject to input and output constraints. Closed‐loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal set, which are solved offline, for the underlying online MPC optimization problem. The main attractive feature of the proposed scheme in comparison with previously published results is that all offline computations are now based on the convex optimization problem, which significantly reduces conservatism and computational complexity. Moreover, the proposed scheme can handle a wider class of linear parameter‐varying input‐output models than those considered by previous schemes without increasing the complexity. For an illustration, the predictive control of a continuously stirred tank reactor is provided with the proposed method.

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Section: Resultsmentioning

confidence: 95%

Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An improved robust model predictive control for linear parameter‐varying input‐output models

Abbas

Hanema

Tóth

et al. 2017

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

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“…Since state variables do not appear in I/O formulations, the MPC algorithms must ensure bounded input to bounded output (BIBO) stabilization (instead of input-to-state). A first, preliminary results was presented in (Abbas et al, 2015), which lacked proofs for stability for any ξ. Then, later on, the method was "robustified" to solve the optimization procedure considering the whole E set in (Abbas et al, 2016(Abbas et al, , 2018.…”

Section: Input/output Formulationmentioning

confidence: 99%

Model predictive control design for linear parameter varying systems: A survey

Morato

Normey‐Rico

Sename

2020

Annual Reviews in Control

140

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Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods.

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“…• Dynamic output feedback algorithms have also been developed 29,30,31,32 . Some of these papers use an input/output LPV formulation.…”

Section: Linear Parameter Varying Systemsmentioning

confidence: 99%

An input‐to‐state stable model predictive control framework for Lipschitz nonlinear parameter varying systems

Morato

Normey-Rico

Sename

2020

Intl J Robust & Nonlinear

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This paper develops a state-feedback Model Predictive Control (MPC) framework for Nonlinear Parameter Varying (NLPV) systems with explicit Lipschitz nonlinearities along the input trajectory. Considering a guess for the evolution of the scheduling parameters along the prediction horizon, the proposed optimization procedure for the MPC design includes a terminal stage cost, a contracting terminal region constraint and nominal predictions scheduled with respect to this guess. The terminal region is taken so that it is monotonically decreasing to a predetermined set, which guarantees recursive feasibility of the algorithm with respect to a bound on the admissible uncertainties (introduced from the scheduling parameter guess). The terminal set is a scheduled robust control invariant set for Lipschitz nonlinear systems, computed through some proposed LMIs. The inclusion of the terminal ingredient also serves to demonstrate Input-to-State Quadratic Stability. This paper ends with a successful numerical simulation example of the technique applied to the control of a Semi-Active automotive suspension system equipped with Electro-Rheological dampers.

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An MPC approach for LPV systems in input-output form

Cited by 12 publications

References 8 publications

An improved robust model predictive control for linear parameter‐varying input‐output models

An improved robust model predictive control for linear parameter‐varying input‐output models

Model predictive control design for linear parameter varying systems: A survey

An input‐to‐state stable model predictive control framework for Lipschitz nonlinear parameter varying systems

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