Robust output feedback model predictive control for linear systems via moving horizon estimation

Sui, Dan; Feng, Liuyang; Hovd, Morten

doi:10.1109/acc.2008.4586533

Cited by 37 publications

(18 citation statements)

References 23 publications

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“…x,ŵ,v denote the estimated variables of system (4) andx * , W * the optimizers of problem (3)-(4).cb, O, W as given below and in (10).…”

Section: B Moving Horizon Estimatormentioning

confidence: 99%

“…A method for obtaining an RPI set bounding the estimation error was presented for the case of unconstrained state estimation (Luenberger observer [8] and unconstrained MHE [10], [11]), where the error dynamics are described by a single linear time invariant (LTI) system, i.e. A err,i = A err and D i = D.…”

Section: Bounding the Estimation Errormentioning

confidence: 99%

“…The proposed method consists of three main steps ( [9], [10], [11]): i) first the Karush-Kuhn-Tucker (KKT) conditions for (9) are formed, ii) the error dynamics for the estimation error at time T −N are obtained, and iii) the error dynamics at time T , which is the current time of the estimation, are obtained.…”

Section: B Dynamics Of the Estimation Errormentioning

confidence: 99%

“…The methodology proposed here addresses this problem by 1. formulating the dynamics that govern the estimation error, 2. using these dynamics to find the set that bounds the error, and then 3. incorporating the bounding set into the constraints of the MPC thus robustifying against the estimation error [8]. This methodology has previously been applied using the Luenberger observer [8] and the unconstrained MHE [9], [10], [11]. In [9] the error dynamics and bounds were obtained for the unconstrained MHE, however for a formulation of MHE not including the estimation of the process noise.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous constrained Moving Horizon state Estimation and Model Predictive Control by multi-parametric programming

Voelker

Kouramas

Pistikopoulos

2010

49th IEEE Conference on Decision and Control (CDC)

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In this work, we demonstrate how constrained Moving Horizon Estimation (MHE) and Model Predictive Control (MPC) can be simultaneously addressed via multiparametric programming. First, we present a method for obtaining the error dynamics of constrained MHE for linear, time-invariant systems by solving the constrained optimization problem of the MHE by multi-parametric programming methods. Set-theoretical methods are then used to derive bounds on the estimation error -it is shown that the estimation error is bounded in an invariant set for the error dynamics described by a set of linear inequalities. The error dynamics and the error bounds can then be used for the design of a robust output feedback explicit/multi-parametric MPC yielding a simultaneous estimation and control design which is illustrated with an example of robust tube-based MPC.

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“…x,ŵ,v denote the estimated variables of system (4) andx * , W * the optimizers of problem (3)-(4).cb, O, W as given below and in (10).…”

Section: B Moving Horizon Estimatormentioning

confidence: 99%

Section: Bounding the Estimation Errormentioning

confidence: 99%

Section: B Dynamics Of the Estimation Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous constrained Moving Horizon state Estimation and Model Predictive Control by multi-parametric programming

Voelker

Kouramas

Pistikopoulos

2010

49th IEEE Conference on Decision and Control (CDC)

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“…Note that if the underlying system is linear and subject to linear constraints, a linear Luenberger observer can be deployed. On this direction, model predictive control (MPC) strategies were proposed in [14], [26]. A common point of MPC strategies is that suitable terminal constraints, describing a robust positively invariant set, are imposed at the end of the prediction horizon, leading to robust stability [15].…”

Section: Introductionmentioning

confidence: 99%

Robust output feedback control: Convex lifting based approach

Nguyễn

Olaru

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Abstract-This paper presents a design method for robust output feedback control of linear, discrete-time, invariant systems affected by both state and output additive disturbances. The method relies on the so-called convex lifting, which is defined on the N −step controllable set. It is proven that the proposed method guarantees the recursive feasibility and robust stability in the sense that the closed loop converges to a given robust positively invariant set as time tends to infinity. Moreover, the method only requires the resolution of a linear programming problem at each sampling instant. Finally, a numerical example is considered to illustrate the methodology.

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