Multi-criteria optimization in nonlinear predictive control

Laabidi, Kaouther; Bouani, Faouzi; Ksouri, Mekki

doi:10.1016/j.matcom.2007.04.002

Cited by 38 publications

(37 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the next step, one of these suboptimal solutions is selected. This selection is realized by specifying a weight vector for the objectives in [138][139][140][141] and by the satisficing trade-off method in [142].…”

Section: Online Multiobjective Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Peitz¹,

Dellnitz²

2018

Preprint

View full text Add to dashboard Cite

Abstract:Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

show abstract

Section: Online Multiobjective Optimizationmentioning

confidence: 99%

“…Laabidi et al [138,140], Artificial neural network (ANN) for state prediction, optimization via Garcìa et al [141] MOEA, selection of Pareto point via WS…”

Section: Algorithms Without Offline Phase -Approximation Of Entire Pamentioning

confidence: 99%

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Peitz¹,

Dellnitz²

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The manipulated variables for the process optimization are the temperature and the relative humidity of the drying air. Laabidi et al (2008) have worked on the development of a predictive control strategy with multicriteria optimization for a nonlinear multimodel system, in order to realize a set point tracking. A GA is used for the resolution of the multicriteria optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Predictive control with multiobjective optimization: Application to a sludge drying operation

Fuentes¹,

Ploteau²,

Glouannec³

2015

Computers & Chemical Engineering

View full text Add to dashboard Cite

“…The methodology to design and implement neural predictive controllers for nonlinear system has been developed in [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…The design methodology for predictive control of industrial processes via recurrent fuzzy neural networks is presented in [14]. In [15] the multilayer perceptron is used to identification of the nonlinear object and genetic algorithm is applied to solve the multi-criteria optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

Ranković

Radulović

Grujović

et al. 2014

INT J COMPUT COMMUN

View full text Add to dashboard Cite

In this paper the synthesis of the predictive controller for control of the nonlinear object is considered. It is supposed that the object model is not known. The method is based on a digital recurrent network (DRN) model of the system to be controlled, which is used for predicting the future behavior of the output variables. The cost function which minimizes the difference between the future object outputs and the desired values of the outputs is formulated. The function ga of the Matlab's Genetic Algorithm Optimization Toolbox is used for obtaining the optimum values of the control signals. Controller synthesis is illustrated for plants often referred to in the literature. Results of simulations show effectiveness of the proposed control system.

show abstract

Multi-criteria optimization in nonlinear predictive control

Cited by 38 publications

References 11 publications

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Predictive control with multiobjective optimization: Application to a sludge drying operation

Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

Contact Info

Product

Resources

About