Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC): Towards the ‘on-line’ optimization of highly complex control problems
作者： Juan José Valera García , Vicente Gómez Garay , Eloy Irigoyen Gordo , Fernando Artaza Fano , Mikel Larrea Sukia
DOI: 10.1016/J.ESWA.2011.12.052
关键词: Pareto principle 、 Genetic algorithm 、 Optimization problem 、 Nonlinear system 、 Mathematical optimization 、 Control system 、 Control theory 、 Model predictive control 、 Artificial neural network 、 Computer science 、 Multi-objective optimization
摘要: The benefits of using the Nonlinear Model Predictive Control (NMPC) for response optimization highly complex controlled plants are well known. Nevertheless complexity and associated high computational cost make its implementation reliability focus discussion. This paper proposes an Intelligent Multi-Objective NMPC (iMO-NMPC) scheme where several, often conflicting, control objectives can be competing simultaneously in problem. In iMO-NMPC, combination a Neural Network, Genetic Algorithm Fuzzy Inference System, help us nonlinear search near-optimal actions, aiming to fulfil all specified simultaneously. proposed adds expert stage that adaptively change degree importance (weight) each objective according state plant. Therefore, once multi-objective problem is solved at sampling time non-inferior solutions belonging set Pareto obtained, most appropriate one selected by weights inferred from stage. Some experimental results showing iMO-NMPC effectiveness details about over systems with low times also presented discussed this paper.
acm.org
elsevier.com
sci-hub.se 参考文章(61)
David E. Goldberg, Philip Segrest, Finite Markov chain analysis of genetic algorithms international conference on genetic algorithms. pp. 1- 8 ,(1987)
J.B. Rawlings, L.T. Biegler, Optimization approaches to nonlinear model predictive control ,(1991)
Advances in Computational Intelligence Springer. ,(2009) , 10.1007/978-3-642-03156-4
Ali Zilouchian, Mo Jamshidi, Intelligent Control Systems Using Soft Computing Methodologies CRC Press. ,(2000) , 10.1201/9781420058147
F. Allgöwer, T. A. Badgwell, J. S. Qin, J. B. Rawlings, S. J. Wright, Nonlinear Predictive Control and Moving Horizon Estimation — An Introductory Overview Advances in Control. pp. 391- 449 ,(1999) , 10.1007/978-1-4471-0853-5_19
G. Polya, A. Robson, How to Solve It ,(1945)
David B. Fogel, Zbigniew Michalewicz, How to Solve It: Modern Heuristics ,(2004)
Thrishantha Nanayakkara, Ferat Sahin, Mo Jamshidi, Intelligent Control Systems with an Introduction to System of Systems Engineering ,(2009)
Carlos Bordons, Eduardo F. Camacho, Control Predictivo: Pasado, Presente y Futuro Revista Iberoamericana De Automatica E Informatica Industrial. ,vol. 1, pp. 5- 28 ,(2004)
Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods ,(2000)