Robust model predictive control: A survey

Bemporad, Alberto; Morari, Manfred

doi:10.1007/bfb0109870

Cited by 846 publications

(556 citation statements)

References 50 publications

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“…Parametric uncertainty representation The uncertainty introduced by varying parameter γ can be expressed in several ways (Kothare et al, 1996), (Bemporad and Morari, 1999). In this application, the parameter is measured and available at any k, so it is possible to express the parameter-varying system dynamics within a polytopic uncertainty representation (Bokor and Balas, 2005): this means that any operating system dynamics at a given speed value within the working rangeĀ(1/v k ) =Ā(γ k ), will be expressed as an affine combination of the two extreme system realizations at the speed limits.…”

Section: Auto-steering System Modelmentioning

confidence: 99%

On the impact of additive disturbances on Auto-Steering Systems

et al. 2017

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Several control techniques are available for the automotive systems and their design is often based on the available measurements of different parameters and the integration of the hard input and performance constraints. The objective of the present study is to revisit the closed loop dynamics of an auto-steering system to offer an evaluation of the impact of the curvature of the road. From a theoretical point of view, robust positive invariance theory is presented in order to perform the analysis of the effects that a bounded parameter-varying additive disturbance has on a linear parameter-varying controller used to ensure stability of a model predictive control strategy.

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Section: Auto-steering System Modelmentioning

confidence: 99%

On the impact of additive disturbances on Auto-Steering Systems

et al. 2017

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“…This trend makes decoupling different modes, specifying different objectives and designing controllers based on paired input/output channels more difficult. Model predictive control (MPC) has proved to be an effective tool to deal with multivariable constrained control problems [12]. As wind turbines are MIMO systems [5] with constraints on inputs and outputs, using MPC seems to be effective.…”

Section: A Wind Turbine Controlmentioning

confidence: 99%

“…Polytopic uncertainty and additive disturbances are common ways to include uncertainties in robust MPC formulation [12]. However here we have employed norm-bounded uncertainty to model our system [19]:…”

Section: B Minimax Mpc Formulationmentioning

confidence: 99%

Robust model predictive control of a wind turbine

Mirzaei

Poulsen

Niemann

2012

2012 American Control Conference (ACC)

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Abstract-In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition, a new sensor is introduced in the EKF to give faster estimations. Wind speed estimation error is used to assess uncertainties in the linearized model. Significant uncertainties are considered to be in the gain of the system (B matrix of the state space model). Therefore this special structure of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally simulation results are presented. First a comparison between PI and robust MPC is given. Afterwards simulations are done for a realization of turbulent wind with uniform profile based on the IEC standard.

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“…These are key issues in MPC: performance benefits are achieved by operating close to constraint boundaries, but when the state evolution no longer matches the predictions, constraint violation and infeasibility can result. Many methods have been developed to endow conventional synchronous MPC with robustness [19,1]. For use with MMPC, we have adopted the constraint tightening approach [11,8,27,25], in which the constraints of the optimization are modified to retain a margin for future feedback action.…”

Section: The Basic Ideamentioning

confidence: 99%

“…Let N = (N u − 1)m + 1 where N u is the control horizon, a design parameter which will later be used to denote the number of control moves to be optimized per input channel of the original system (1). The N -step prediction model at time k for the system described by (4) is…”

Section: Preliminarymentioning

confidence: 99%

Multiplexed model predictive control

et al. 2012

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This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires online optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem.

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Robust model predictive control: A survey

Cited by 846 publications

References 50 publications

On the impact of additive disturbances on Auto-Steering Systems

On the impact of additive disturbances on Auto-Steering Systems

Robust model predictive control of a wind turbine

Multiplexed model predictive control

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