Effect of on-line optimization techniques on model predictive control and identification (MPCI)

Shouche, Manoj S.; Genceli, Hasmet; Nikolaou, Michael

doi:10.1016/s0098-1354(02)00091-1

Cited by 23 publications

(19 citation statements)

References 6 publications

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“…Through an example system, they demonstrated that the input signal excited the system sufficiently and that their approach is significantly better for identifying the plant than a normal mpc. A similar scheme termed Model Predictive Control and Identification (mpci) was introduced by Genceli and Nikolaou [8] and further developed in a number of papers by Nikolaou and coworkers, notably Shouche et al [9] and most recently Shouche et al [10]. This approach is based on requiring a persistently exciting input to the process, resulting in a noncovex optimization problem solved to optimality with a branch-and-bound approach developed by the authors.…”

Section: Introductionmentioning

confidence: 99%

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MPC-based dual control with online experiment design

Heirung

Foss

Ydstie

2015

Journal of Process Control

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Section: Introductionmentioning

confidence: 99%

“…One early mpc for systems with a finite impulse response, similar to those proposed by Shouche et al [10] and Larsson et al [7], was developed by Marafioti [18] and Marafioti et al [19]. In this approach, the dual character of the control is obtained by guaranteeing the input be persistently exciting.…”

Section: Introductionmentioning

confidence: 99%

MPC-based dual control with online experiment design

Heirung

Foss

Ydstie

2015

Journal of Process Control

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“…A similar scheme termed Model Predictive Control and Identification (M.P.C.I.) was introduced by Genceli and Nikolaou (1996) and further developed in a number of papers by Nikolaou and coworkers, notably Shouche, Genceli, Vuthandam, et al (1998) and most recently Shouche, Genceli, and Nikolaou (2002). This approach is based on requiring a persistently exciting input to the process, resulting in a noncovex optimization problem solved to optimality with a branch-and-bound approach developed by the authors.…”

Section: Introductionmentioning

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%

“…Around the same time the problem was approached from the point of view of Model Predictive Control and an approach termed Model Predictive Control and Identification (M.P.C.I.) was introduced by Genceli and Nikolaou (1996) and later extended in several publications by Nikolaou and coworkers, notably Shouche, Genceli, and Nikolaou (2002). The M.P.C.I.…”

Section: Introductionmentioning

confidence: 99%

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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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Multiobjective constrained MPC with simultaneous closed-loop identification

Aggelogiannaki¹,

Sarimveis²

2006

Int. J. Adapt. Control Signal Process.

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Model predictive control (MPC) methodologies are commonly used techniques for constrained control problems. In this paper, the principle of prioritized multiobjective optimization is incorporated in an adaptive MPC framework in order to improve the closed-loop performance in the case of time-varying systems. Instead of weighting the different control goals, the proposed methodology creates a hierarchy according to the importance of each objective and optimizes each one separately. In each optimization step a constraint is added, so that previous in rank objective functions maintain their optimal values. Adaptive capabilities are introduced in the proposed MPC formulation, by considering the persistent excitation requirement as a top priority objective, which is optimized first. The efficiency of the proposed MPC configuration is evaluated through three dynamic processes and the expected advantages are confirmed.

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Effect of on-line optimization techniques on model predictive control and identification (MPCI)

Cited by 23 publications

References 6 publications

MPC-based dual control with online experiment design

MPC-based dual control with online experiment design

Experimental Evaluation of a MIMO Adaptive Dual MPC

Multiobjective constrained MPC with simultaneous closed-loop identification

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