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Applied Optimal Estimation, MIT Press, MA, USA. S. P. (1993). Kalman Filtering: Theory and Practice, Prentice-Hall. Hofman, T. V. (2004). Energy analysis of hybrid vehicle powertrains, In Proc. Of the IEEE Int. Symp. On Vehicular Power and Propulsion, Paris, France. Husain, I. (2003). Electric and Hybrid Vehicles: Design Fundamentals, 1st edn, CRC Press. B. J. (2000). HEV control strategy for real-time optimization of fuel economy and emissions, In Proc. of the Future Car Congress, Washington DC, USA.

6 and 7 present the evolutions of the set point, the outputs obtained, respectively, with the presence of load disruption and noise. Fig. 6 shows the evolutions of the set point, the outputs signals obtained with both NMPC and MAMPC control strategy. It is clear from this figure that the presence of load disruptions, from iteration70 to iteration 90 and from iteration 120 to iteration 140, does not lead to a correct pursuit. Thus, the presence of load disruptions has more effect on NMPC control than the MAMPC strategy.

In order to avoid solving nonconvex optimization problem, MAMPC (Multiagent model predictive control) optimization procedure, a method for convex NMPC was also developed in this chapter book. Theoretical Fast Nonlinear Model Predictive Control using Second Order Volterra Models Based Multi-agent Approach 31 analysis and simulation results demonstrate better performance of the MAMPC over a conventional NMPC based on sequential quadratic programming (SQP) in tracking the set point changes as well as stabilizing the operation in the presence of input disturbances.

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