Tube based Quasi-min-max Output Feedback MPC for LPV Systems

Yang, Seung-Han; Tan, Kok Kiong; Lee, Tong Heng

doi:10.3182/20120710-4-sg-2026.00010

Cited by 24 publications

(37 citation statements)

References 14 publications

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“…We choose

\overset{x}{true} (0) = {[0.27, - 0.18]}^{T}

, and α 0 =100. The tube‐based quasi‐min‐max approach that is proposed in is also applied for comparison. For the tube‐based method, the observer gains are L 1 =[−1.00,−0.05] T , L 2 =[−1.00,8.84 × 10 −6 ] T , and the controller gain is K = [−3.62,−6.74], all of which are computed offline.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…We will first propose a sufficient condition to guarantee the inequality (16), which is usually called the stability constraint. Consider the following parameter-dependent control law,…”

Section: Satisfaction Of Constraintsmentioning

confidence: 99%

“…The authors of and proposed the so‐called tube‐based RMPC strategy. The results were extended to output feedback RMPC of LPV systems in Su et al (2012) , where the observer gain is determined offline while the state feedback gain is computed online. It is shown that the system performance can be optimized online based on the nominal prediction model, that is, ignoring the disturbance in the predicted system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances

Yang¹,

Gao²,

Feng

et al. 2016

Int. J. Robust. Nonlinear Control

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Summary An observer‐based output feedback predictive control approach is proposed for linear parameter varying systems with norm‐bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in an ℋ∞‐type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input‐to‐state stable observer, the closed‐loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.

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“…We choose

\overset{x}{true} (0) = {[0.27, - 0.18]}^{T}

Section: Simulation and Discussionmentioning

confidence: 99%

“…We will first propose a sufficient condition to guarantee the inequality (16), which is usually called the stability constraint. Consider the following parameter-dependent control law,…”

Section: Satisfaction Of Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances

Yang¹,

Gao²,

Feng

et al. 2016

Int. J. Robust. Nonlinear Control

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show abstract

“…• A first paper on LPV TMPC was proposed by Su et al (2012), where an output feedback algorithm is proposed, whose results is slightly superior to that of a min-max technique, thereby named quasimin-max;…”

Section: Tube-based Methodsmentioning

confidence: 99%

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

Morato

Normey‐Rico

Sename

2020

Annual Reviews in Control

128

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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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“…4,5 Over the last two decades, research activities in robust MPC for LPV systems have become an important branch, eg, state feedback robust MPC with measurable system states [6][7][8][9][10] and output feedback robust MPC with unknown system states. [11][12][13][14][15][16][17][18][19][20][21][22] In robust MPC control problems, it is important to know the current system state information to ensure reliable operations. Unfortunately, not all system states can be exactly measured in some practical processes, and only system output measurements are available.…”

Section: Introductionmentioning

confidence: 99%

An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

Ping

Yang

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary The present paper addresses an observer‐based output feedback robust model predictive control for the linear parameter varying system with bounded disturbance and noise subject to input and state constraints. The main contribution is that the on‐line convex optimization problem not only simultaneously optimizes the observer and controller gains to stabilize the augmented closed‐loop system but also incorporates the refreshment of bounds of the estimation error set. The optimization problem steers the nominal augmented closed‐loop system to converge to the origin, and the real augmented closed‐loop system bounded within robust positive invariant set converges to a neighborhood of the origin such that recursive feasibility of the optimization and robust stability of the controlled system are ensured. Two numerical examples are given to illustrate the effectiveness of the method.

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Tube based Quasi-min-max Output Feedback MPC for LPV Systems

Cited by 24 publications

References 14 publications

An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances

An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances

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

An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

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