(Ed) Levine īut still lack of research to find a control algorithm for J.M.Maciejowski W.García – Gabín, E.F CamachoĪnd D.Zambrano W.García – Gabín, E.FĬamacho D. The instability problem in Model Predictive Controlįor non minimum phase systems reported in some papers,ĭ. In order to solve this problem, Al-Duwaish H.Īnd Naeem,Wasif, Kaouther Laabidi and Faouziīouani as well as Authors have used GeneticĪlgorithms(GAs), J.M. Huge therefore the requirement of computing device isĪlso high. That mean the optimal problem is implemented during one The control signal computing is implemented online, However, it also has some disadvantages as following.įigure 1. The MPC has some advantages as described in. Theĭetail and advantages of this method were described in The principle of MPC is showed in Figure 1. Provides a wealth of information on application issuesįrom the point of view of MPC users was provided by Young,īartusiak, and Fontaine (2001), Downs (2001),Īnd Hillestad and Andersen (1994) reportedĭevelopment of MPC technology within operatingĬompanies. (2001) describe industrial MPC practice and futureĭevelopments from the vendor’s viewpoint. Process Control V Conference was presented by theĪuthors: Qin & Badgwell, 1997, Qin and Badgwell,Ģ000. Technology based on linear models at the 1996 Chemical & Zheng (2000) Kouvaritakis & Cannon, 2001 Severalīooks on MPC have recently been published by Allgower Rawlings, Meadows, and Muske (1994) Mayne Reviews of MPC theory include those of Garsíaa, Prett,Īnd Morari (1989) Ricker (1991) Morari and AĬomprehensive review of theoretical results on theĬlosed-loopbe havior of MPC algorithms was providedīy Rawlings, Rao, and Scokaert (2000). Introductory tutorial aimed at control practitioners. Techniques, Rawlings (2000) provided an excellent Theoretical developments and numerical solution Horizon estimation, including a summary of recent Qin, Rawlings, and Wright presented a moreĬomprehensive overview of nonlinear MPC and moving Introduction to theoretical and practical issues associated Several recent publications have provided a good
Multi-input, multi-output (MIMO) nonlinear process. Successfully applied in industrial process, especially the Model Predictive Control (MPC) has been widely and Index Terms - Model Predictive Control (MPC) Takagi Sugeno Fuzzy Model (TS) Genetic Algorithms (GAs) Branch and Bound (B&B), Multiple Inputs-Multiple Output (MIMO) Single Input-Single Output (SISO) Adaptive Fuzzy Model Predictive Control (AFMPC) Global Asymptotical Stable (GAS) Continuous Stirrer Tank Reactor (CSTR). The simulation results for the Continuous Stirrer Tank Reactor (CSTR), nonlinear uncertain dynamical system and nolinear DC motor are used for verifying the proposal method. This method is used for nonlinear systems with non-minimum phase (CSTR), uncertain dynamical systems and nonlinear DC motor. The method to tune parameters of the model predictive controller based on Lyapunov stability theorem is presented in this paper to bring higher control performance and guaranty Global Asymptotical Stable (GAS) for the closed-loop system. The plant to be used as predictive model is simulated by Takagi-Sugeno Fuzzy Model, and the optimization problem is solved by a Genetic Algorithms or Branch and Bound. 1, Dai Co Viet, Hai Ba Trung, Hanoi, -this paper introduces a method to design a robust adaptive predictive control based on Fuzzy model. Adaptive Fuzzy Model Predictive Control for Non-minimum Phase and Uncertain Dynamicalģ9 Tran Hung Dao, Hoan Kiem, Hanoi, Xuan Minhĭerpartment of Automatic Control, Hanoi University of Science and Technology