By T. Zheng
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Extra resources for Advanced Model Predictive Control
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). Control of systems integrating logic, dynamics, and constraints, Automatica 35: 407-427.
Figs. 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.
At times t = 5 secs and t = 15 secs , the TITO controller is requested for an increased and decreased ICE torques, respectively to which the fast MPC algorithm could precisely follow those commands, as illustrated in Figure 4(a). Similarly, there was an increased request for the CIMG torque (from 20 NM to 40 NM) at time t = 10 secs, and the controller has successfully delivered this torque request, as depicted in Figure 4(b). This is noted that our torque manager structure, as stated earlier, assumes that setpoint torque commands are provided by some sort of static optimisation algorithms.
Advanced Model Predictive Control by T. Zheng