Persistently exciting model predictive control

Marafioti, Giancarlo; Bitmead, Robert R.; Hovd, Morten

doi:10.1002/acs.2414

Cited by 103 publications

(85 citation statements)

References 18 publications

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“…Nonetheless, re-identification under closed-loop has its technical challenges since the nature of feedback control will treat any input stimulus designed to elicit the system response as a disturbance, which the control system will try to eliminate. The basic approach is to inject a 'dither' signal uncorrelated to the disturbance (Genceli & Nikolaou, 1996;Rathouský & Havlena, 2013;Marafioti, Bitmead, & Hovd, 2014). Typically, this dither signal is used to perturb the system setpoints (Zhu & Butoyi, 2002) although other approaches have been explored (Sotomayor, Odloak, and Moro, 2009).…”

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Model-predictive control for non-domestic buildings: a critical review and prospects

Rockett

Hathway

2016

Building Research & Information

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Model-predictive control (MPC) has recently excited a great deal of interest as a new control paradigm for non-domestic buildings. Since it is based on the notion of optimisation, MPC is, in principle, well-placed to deliver significant energy savings and reduction in carbon emissions compared to existing rule-based control systems. In this paper, we critically review the prospects for buildings MPC and, in particular, the central role of the predictive mathematical model that lies at its heart; our clear emphasis is on practical implementation rather than control-theoretic aspects, and covers the role of occupants as well as the form of the predictive model. The most appropriate structure for such a model is still an open question, which we discuss alongside the development of the initial model, and the process of updating the model during the building's operational life. The importance of sensor placement is highlighted alongside the possibility of updating the model with occupants' comfort perception. We conclude that there is an urgent need for research on the automated creation and updating of predictive models if MPC is to become an economically-viable control methodology for non-domestic buildings. Finally, more evidence through operating full scale buildings with MPC is required to demonstrate the viability of this method.

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Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Model-predictive control for non-domestic buildings: a critical review and prospects

Rockett

Hathway

2016

Building Research & Information

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“…formulation. A similar approach is taken by Marafioti, Bitmead, and Hovd (2014), where the resulting optimal control problem can be formulated as a quadratic programming problem. These approaches are in this context best described as actively adaptive controllers.…”

Section: Dual Controlmentioning

confidence: 99%

“…One early M.P.C. for systems with a finite impulse response, similar to those proposed by Shouche, Genceli, and Nikolaou (2002) and Larsson, Annergren, et al (2013), was developed by Marafioti (2010) and Marafioti, Bitmead, and Hovd (2014). In this approach, the dual character of the control is obtained by guaranteeing the input be persistently exciting.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of a MIMO Adaptive Dual MPC

Kumar¹,

Heirung²,

Patwardhan³

et al. 2015

IFAC-PapersOnLine

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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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“…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%