Fuzzy Control of Multivariable Processes
نویسندگان
چکیده
Most processes in industry are complex mul-tivariable systems with nonlinear and time-varying dynamics. These processes are often diicult to model. However, often much expert knowledge and process data is available. Fuzzy modeling and control techniques can eeciently integrate this information. However, most of the systems considered in literature are of low-order and SISO or MISO systems. For MIMO systems, complications occur because the size of the rule bases and computational load increases dramatically for higher order systems. Within the FAMIMO ESPRIT project see acknowledgements , new algorithms are being developed for the indentiication and control of MIMO systems. The main research items are: MIMO model identiication Fuzzy heterogeneous control Pseudo inversion of fuzzy models Fuzzy adaptive control Model based predictive control Methods for stability analysis Development of software tools New developments from our group and the partners in FAMIMO will be presented. In Delft, a software toolbox for MIMO Takagi-Sugeno 44 fuzzy model identiication is developed 11. Two methods for MBPC using these models are investigated. Namely, local linearized control 3 and a Branch & Bound algorithm. Both methods are incorporated in an internal model control IMC 2 scheme. Two benchmarks are used to compare the developed methods : Wastewater treatment of a paper factory Direct injection engine Acknowledgements: This work has been done
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