By T. Zheng
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Additional resources for Advanced Model Predictive Control
The controller-agent is realized by some control algorithm that is operational only under particular operating conditions of the plant being controlled. Moreover, the controller-agent’s action consist of the analytical Fast Nonlinear Model Predictive Control using Second Order Volterra Models Based Multi-agent Approach 35 optimal control sequence elaborated in each sub-system after having learned the trajectory of the control to follow and by minimizing a local cost function. The individual solutions or controller-agents are combined into one overall solution.
7. D. & Anderson, J. (2010). Electric and Hybrid Cars: A History, 2nd Edition, McFarland & Co Inc. ; 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.
When linear constraints are taken into account, the solution can be found using quadratic programming techniques. With the introduction of a nonlinear model into MPC scheme, a nonlinear programming technique (NLP) has to be solved at each sampling time to compute the future manipulated variables in on-line optimization that is generally nonconvex which make their implementation difficult for real time control. During the past decade significant theoretical results as well as advances in the implementation strategies of NMPC have been obtained and NMPC has been successfully applied in practice to relatively slow plants, mainly in the process industry.
Advanced Model Predictive Control by T. Zheng