MPC-based dual control with online experiment design

Heirung, Tor Aksel N.; Foss, Bjarne A.; Ydstie, B. Erik

doi:10.1016/j.jprocont.2015.04.012

Cited by 56 publications

(41 citation statements)

References 25 publications

(44 reference statements)

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“…The deterministic formulation relies on the input design cost function (8) and the gPC-based prediction model (12). The state chance constraints (4c) are approximated by convex second-order cone constraints [21] (e.g., see [22] for the details).…”

Section: Deterministic Formulation For Ix-smpcmentioning

confidence: 99%

“…Proof: The input design cost function J i (see (8)) can be explicitly defined in terms of the decision variables π.…”

Section: Deterministic Formulation For Ix-smpcmentioning

confidence: 99%

“…To this end, an explicit approach to dual control (see [4]), which involves reformulation of the dual control problem to a tractable optimal control problem with some form of system probing feature, is adopted. Explicit dual control approaches commonly integrate the model-based control design problem with an input design problem to generate sufficiently informative closed-loop data for model adaptation [5], [6], [7], [8], [9], [10]. In a predictive control setting, this typically entails incorporating some measure of the Fisher information matrix into the optimal control problem.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic predictive control with adaptive model maintenance

Bavdekar

Ehlinger

Gidon

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-The closed-loop performance of model-based controllers often degrades over time due to increased model uncertainty. Some form of model maintenance must be performed to regularly adapt the system model using closed-loop data. This paper addresses the problem of control-oriented model adaptation in the context of predictive control of stochastic linear systems. A stochastic predictive control approach is presented that integrates stochastic optimal control with control-oriented input design in order to confer some degree of probing effect to the control inputs. The probing effect will enable generating informative closed-loop data for (online) control-oriented model maintenance. In a simulation study, the performance of the proposed stochastic predictive control approach with integrated input design is demonstrated on a atmospheric-pressure plasma jet with potential biomedical applications.

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Section: Deterministic Formulation For Ix-smpcmentioning

confidence: 99%

“…Proof: The input design cost function J i (see (8)) can be explicitly defined in terms of the decision variables π.…”

Section: Deterministic Formulation For Ix-smpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic predictive control with adaptive model maintenance

Bavdekar

Ehlinger

Gidon

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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show abstract

“…The former aims at exciting the system dynamics to maximize the information content of the input-output data, whereas in control the primary objective is typically to suppress disturbances and perturbations. For linear systems, model-based control strategies have recently been proposed that integrate experiment design with predictive control (Marafioti et al, 2013;Larsson et al, 2015;Heirung et al, 2015). In these control strategies, some measure of the information content of system outputs is incorporated into the optimal control problem.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control with Integrated Experiment Design for Nonlinear Systems

Bavdekar

Mesbah

2016

IFAC-PapersOnLine

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“…It can be shown that the corresponding controller excites the system for improved state estimates. A related approach toward dual MPC has been proposed in [24,25], where the MPC objective is augmented by a term that-similar to optimal experiment design [19,46,54]-penalizes the predicted parameter error variance. In [26] the same authors develop a way to predict and optimize future parameter estimation errors for single-input-single-output finite impulse response systems based on non-convex quadratically constrained quadratic programming formulations.…”

mentioning

confidence: 99%

Self-Reflective Model Predictive Control

Houska¹,

Telen²,

Logist³

et al. 2017

SIAM J. Control Optim.

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Abstract. This paper proposes a novel control scheme, named self-reflective model predictive control, which takes its own limitations in the presence of process noise and measurement errors into account. In contrast to existing output-feedback MPC and persistently exciting MPC controllers, the proposed self-reflective MPC controller does not only propagate a matrix-valued state forward in time in order to predict the variance of future state-estimates, but it also propagates a matrix-valued adjoint state backward in time. This adjoint state is used by the controller to compute and minimize a second order approximation of its own expected loss of control performance in the presence of random process noise and inexact state estimates. The properties of the proposed controller are illustrated with a small but non-trivial case study.

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MPC-based dual control with online experiment design

Cited by 56 publications

References 25 publications

Stochastic predictive control with adaptive model maintenance

Stochastic predictive control with adaptive model maintenance

Stochastic Model Predictive Control with Integrated Experiment Design for Nonlinear Systems

Self-Reflective Model Predictive Control

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