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Nelles O , Predictive control based on local linear fuzzy models, International Journal of system Science, 29 679-697(1998). , and Henk B.. Effective Optimization for Fuzzy Model Predictive Control . IEEE Transactions on fuzzy systems, Vol. 12, No. R and Abotto M and Szeifert F and Nagy L. (2000) Kanev, S and Vergaegen, M. “Controller e-configuration for non-linear systems”, Control Engineering Practice, Vol. 8 No. 11,(2000) pp. 1223-35. R, De Schutter B. R. P.

The control performance of MAMPC algorithm is Fast Nonlinear Model Predictive Control using Second Order Volterra Models Based Multi-agent Approach 47 evaluated by illustrative comparison with general NMPC. All the results prove that MAMPC approach is a fairly promising algorithm by delivering significantly improved control. The performance of the proposed controllers is evaluated by applying to single input-single output control of non linear system. Theoretical analysis and simulation results demonstrate better performance of the MAMPC over a conventional NMPC based on sequential quadratic programming in tracking the setpoint changes as well as stabilizing the operation in the presence of input disturbances.

Mechatronic design and control of hybrid electric vehicles, IEEE/ASME Trans. On Mechatronics, 5(1): 58-72. ; Frasca, R. ; . (2007). Explicit Hybrid Model Predictive Control of the dc-dc Boost Converter, IEEE Power Electronics Specialists Conference, PESC 2007, Orlando, Florida, USA, pp. 2503-2509 Bemporad, A. (2004). Hybrid Toolbox - User’s Guide. ; Morari, M; Dua, V. N. (2002). The explicit linear quadratic regulator for constrained systems, Automatica 38: 3-20. Bemporad, A. & Morary, M. (1999).

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