By T. Zheng
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Extra resources for Advanced Model Predictive Control
Wachter, A. T. (2000). Active set vs. interior point strategies for model predictive control, Proc. , Vol. 6, pp. 4229-4233. C. & Rizzoni, G. (2000). 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.
5480 . 1 . 0088 39 (27) The result of modelisation is reported in figure 3. These results showed the application Chiu algorithm for the classification which has a better quality of local approximation of the system. Fig. 3. 1 Set point tracking The proposed concept as seen in section 3 is used, to control the nonlinear system. The tuning parameters of the multi-agent consists of the parameters values of each agent given by: N 1 = 1 ; N 2 = 5 ; N u = 1 ; R1 = R2 = 4; . Assuming for the sake of simplicity but without loss of generality, the prediction and control horizons are the same for each agent.
3 Convex optimization approach In order to avoid solving nonconvex optimization problem, MAMPC optimization procedure, a method for convex NMPC was also developed in this chapter book. The performance of the proposed controllers is evaluated by applying to the same process and the attention has been focused on multi-agent model predictive control approach as a possible way to resolve non-convex optimization tasks. 5. The nonlinear programming algorithm (NLP) cannot find a solution for the optimization problem.