Anis SAKLY, Ali THAMALLAH, Faouzi M’SAHLI

Constrained multiobjective PSO and T-S fuzzy models for predictive control

Multiobjective optimization problems are still a challenging area in the field of control system engineering. Inthis context, the current study describes a new multivariable predictive control scheme formulated by using the T-S fuzzymodeling method and a new constrained multiobjective PSO algorithm. The T-S fuzzy modeling technique is appliedto forecast the behaviors of the nonlinear system. It also aims at establishing some conditions so that the proposedcontrol loop is asymptotically stable. The obtained experimental results show that the combination of the philosophy ofthe T-S fuzzy model and multiobjective PSO is very good in the controlling of nonlinear multivariable processes. Thesatisfactory tracking results with small values of MRE demonstrate the proof of capability of the proposed algorithmand the accuracy of the T-S modeling approach. Meanwhile, experimental results show that, compared with the onesobtained with standard MPC, the proposed method is of high effectiveness in term of the control increments optimizationand the errors in the presence of disturbances.

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Nery Júnior GA, Martins MF, Kalid R. A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty. ISA T 2014; 53: 560-567.
Clarke DW, Mohtadi C, Tuffs PS. Generalized predictive control—Part I. The basic algorithm. Automatica 1987; 23: 137-148.
Venkatesh S, Ramkumar K, Guruprasath M, Srinivasan S, Balaş VE. Generalized predictive controller for ball mill grinding circuit in the presence of feed-grindability variations. Stud Inform Control 2016; 25: 29-38.
Ben Abdennour R, Ksouri M, Favier G. Application of fuzzy logic to the on-line adjustment of the parameters of a generalized predictive controller. Intell Autom Soft Com 1998; 4: 197-213.
Laabidi K, Bouani F, Ksouri M. Multi-criteria optimization in nonlinear predictive control. Math Comput Simulat 2008; 76: 363-374.
Khouaja A, Garna T, Ragot J, Messaoud H. Nonlinear predictive controller based on S-PARAFAC Volterra models applied to a communicating two-tank system. Int J Control 2015; 88: 1456-1471.
McNamara P, Negenborn RR, De Schutter B, Lightbody G. Weight optimisation for iterative distributed model predictive control applied to power networks. Eng Appl Artif Intel 2013; 26: 532-543.
Vallerio M, Van Impe J, Logist F. Tuning of NMPC controllers via multi-objective optimisation. Comput Chem Eng 2014; 61: 38-50.
Deb K. Multi-Objective Optimization Using Evolutionary Algorithms. New York, NY, USA: John Wiley & Sons, 2001.
Ben Hassen N, Saadaoui K, Benrejeb M. Lead-lag controller design for time delay systems using genetic algorithms. Stud Inform Control 2017; 26: 87-94.
Reyes-Sierra M, Coello CC. Multi-objective particle swarm optimizers: a survey of the state-of-the-art. International Journal of Computational Intelligence Research 2006; 2: 287–308.
Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE International Conference on Neural Networks; 27 November–1 December 1995; Perth, Australia. New York, NY, USA: IEEE Press. pp. 1942-1948
Thi Thom H, Ming Yuanc C, Quoc Tuan V. A novel perturbed particle swarm optimization-based support vector machine for fault diagnosis in power distribution systems. Turk J Elec Eng & Comp Sci 2018; 26: 518-529.
Toader FT. A hybrid algorithm for job shop scheduling problem. Stud Inform Control 2015; 24: 171-180. [15] Francesco B. Constrained Optimal Control of Linear and Hybrid Systems. New York, NY, USA: Springer, 2003.
Chadli M, Borne P. Multiple Models Approach in Automation: Takagi-Sugeno Fuzzy Systems. 1st ed. New York, NY, USA: Wiley-ISTE, 2012.
Garriga JL, Soroush M. Model predictive control tuning methods: a review. Ind Eng Chem Res 2010; 49: 3505-3515.
Wojsznis W, Gudaz J, Blevins T, Mehta A. Practical approach to tuning MPC based on practical approach to tuning MPC. ISA T 2003; 42: 149-162.
Bemporad A, Muñoz De La Peña D. Multiobjective model predictive control. Automatica 2009; 45: 2823-2830 .
Zavala VM, Flores-Tlacuahuac A. Stability of multiobjective predictive control: a utopia-tracking approach. Automatica 2012; 48: 2627-2632.
Boulkaibet I, Belarbi K, Bououden S, Marwala T, Chadli M. A new T-S fuzzy model predictive control for nonlinear processes. Expert Syst Appl 2017; 88: 132-151.
Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE T Syst Man Cyb 1985; SMC-15: 116-132.
Fossard AJ, Normand-Cyrot D. Nonlinear Systems Stability and Stabilization 2. London, UK: Chapman & Hall, 1996.
Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002; 6: 182-197.
Johansson KH. The quadruple-tank process: a multivariable laboratory process with an adjustable zero. IEEE T Control Syst T 2000; 8: 456-465.
Lee HJ, Park JB, Joo YH. Digital fuzzy set-point regulating chaotic systems: intelligent digital redesign approach. In: Halang ZL, Halang WA, Halang CG. Integration of Fuzzy Logic and Chaos Theory. Berlin, Germany: Springer, 2006. pp. 157-183.
Camacho EF, Bordons C. Model Predictive Control. Berlin, Germany: Springer, 1999.
Miranda H, Cortes P, Yuz JI, Rodriguez J. Predictive Torque Control of Induction Machines Based on State-Space Models. IEEE T Ind Electron 2009; 56: 1916-1924.