Model predictive control with integrated experiment design for output error systems

Larsson, Christian A.; Annergren, Mariette; Hjalmarsson, Håkan; Rojas, Cristian R.; Bombois, Xavier; Mesbah, Ali; Moden, Per Erik

doi:10.23919/ecc.2013.6669533

Cited by 41 publications

(33 citation statements)

References 18 publications

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“…Thus, the designed control inputs will have some form of dual effect to enable generating informative closedloop data for model uncertainty reduction (Wittenmark, 1995). Larsson et al (2013) presented a MPC approach with integrated control-oriented experiment design, where the intended control application of the model is explicitly accounted for in input design (Hjalmarsson, 2005). This paper addresses the problem of probabilistic model uncertainty handling in the context of stochastic predictive control (Mesbah, 2016) of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control with Integrated Experiment Design for Nonlinear Systems

Bavdekar

Mesbah

2016

IFAC-PapersOnLine

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Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control with Integrated Experiment Design for Nonlinear Systems

Bavdekar

Mesbah

2016

IFAC-PapersOnLine

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“…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%

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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“…However, a systematic approach to integrating control design and smart excitation experiments enables the analysis necessary for demonstrating important properties such as convergence and stability. Larsson, Annergren, et al (2013) developed an M.P.C. that performs identification experiments while controlling the plant.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of a MIMO Adaptive Dual MPC

Kumar¹,

Heirung²,

Patwardhan³

et al. 2015

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Most decisions based on predictions from a model have uncertain outcomes. The uncertainty may be exogenous or endogenous to the modeled process, or both, and can greatly affect the degree to which the decision-maker's goals are met. In this thesis I study optimal control problems for systems with endogenous uncertainty that can be reduced by manipulating the system input. When the decision-maker can improve performance through actively reducing uncertainty ("active learning"), there is a dual nature to the optimal sequence of decisions; the decisions or inputs must direct the process toward the desired state and also ensure that information-rich data be generated so that decision-relevant uncertainty is resolved.The dual-control problem can be defined as that of minimizing the expected output errorwhere y * is the output reference, Y (t + N − 1 | t) represents future information up to time t + N − 1 in addition to all past information, and the model for the system output y is not fully known. My coworkers and I propose a novel reformulation technique for a probabilisticallyconstrained stochastic dual-control problem and show that the optimal strategy for minimizing this cost function involves active exploration of the plant to generate informative data. It is consequently necessary that the model that informs the decision-making include how future data resolve uncertainty. The reformulation permits practical algorithms for true dual control for a class of systems, allows new interpretation of earlier approaches, and may guide approximate dual-control designs where no exact results are obtainable. In addition to useful algorithms, this thesis contains a number of conceptual insights. In particular, the most recent results provide a foundation from which we argue that the conventional dual-control interpretation involving a trade-off between control and exploration is a false dichotomy. These derivations clearly show, for a specific system class, that control of the nominal model and a specific form of uncertainty reduction are both necessary components of the optimal control, vii as opposed to separate entities in which uncertainty reduction can be sacrificed for increased control performance.I consider the approaches to dual control presented in this thesis as falling into one of the following three categories: minimization of (i) a heuristic objective that is different from, yet still reduces, the dual objective ( * ); (ii) a systematic approximation of ( * ); and (iii) an exact reformulation of ( * ). This main contributions in this thesis are taken from the following three papers: Each of the three main papers introduce control designs that involve solving a finite-horizon optimal-control problem (O.C.P.) at every sampling instant, with the initial state set to the current state of the plant. Common to the algorithms is their foundation in model-predictive control (M.P.C.). The approaches each involve augmenting the standard O.C.P. in M.P.C. with cost-function terms and constraints, with the result that the c...

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Model predictive control with integrated experiment design for output error systems

Cited by 41 publications

References 18 publications

Stochastic Model Predictive Control with Integrated Experiment Design for Nonlinear Systems

Stochastic Model Predictive Control with Integrated Experiment Design for Nonlinear Systems

Stochastic predictive control with adaptive model maintenance

Experimental Evaluation of a MIMO Adaptive Dual MPC

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