Simultaneous Constrained Model Predictive Control and Identification of DARX Processes

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

doi:10.1016/s0005-1098(98)80005-8

Cited by 63 publications

(45 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although both Volterra series and NARMAX models represent input-output relations, the Volterra series give an explicit representation while the NARMAX model gives an implicit representation, which is often of a much more compact form. A large class of systems can be described using the NARMAX model by selecting different forms of the functions F , for example the nonlinear DARX model (Shouche et al 1998). …”

Section: Narmax Modelmentioning

confidence: 99%

An iterative orthogonal forward regression algorithm

Guo

Billings

et al. 2014

International Journal of Systems Science

View full text Add to dashboard Cite

--A novel iterative learning algorithm is proposed to improve the classic orthogonal forward regression (OFR) algorithm in an attempt to produce an optimal solution under a purely OFR framework without using any other auxiliary algorithms. The new algorithm searches for the optimal solution on a global solution space while maintaining the advantage of simplicity and computational efficiency. Both a theoretical analysis and simulations demonstrate the validity of the new algorithm.

show abstract

Section: Narmax Modelmentioning

confidence: 99%

An iterative orthogonal forward regression algorithm

Guo

Billings

et al. 2014

International Journal of Systems Science

View full text Add to dashboard Cite

show abstract

“…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%

MPC-based dual control with online experiment design

Heirung

Foss

Ydstie

2015

Journal of Process Control

View full text Add to dashboard Cite

“…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 way of approaching this issue is to design a controller that actively explores the plant by ensuring a certain level of excitation, either constantly or when needed. Shouche, Genceli, Vuthandam, et al (1998) combined M.P.C. with system identification for autoregressive systems with an exogenous input (A.R.X.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of a MIMO Adaptive Dual MPC

Kumar¹,

Heirung²,

Patwardhan³

et al. 2015

IFAC-PapersOnLine

View full text Add to dashboard Cite

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...

show abstract

Simultaneous Constrained Model Predictive Control and Identification of DARX Processes

Cited by 63 publications

References 29 publications

An iterative orthogonal forward regression algorithm

An iterative orthogonal forward regression algorithm

MPC-based dual control with online experiment design

Experimental Evaluation of a MIMO Adaptive Dual MPC

Contact Info

Product

Resources

About