Fuzzy Control: Synthesis and AnalysisShehu S. Farinwata, Dimitar P. Filev, Reza Langari Fuzzy Control Synthesis and Analysis Edited by Shehu S. Farinwata Ford Motor Company, Research Laboratory, Dearborn, Michigan, USA Dimitar Filev Ford Motor Company, AMTDC, Redford, Michigan, USA Reza Langari Texas A & M University, College Station, Texas, USA Fuzzy techniques are used to cope with imprecision in the basic elements of a process under control. Written by an international team of researchers this edited volume covers the modeling, analysis and synthesis of fuzzy control systems. Features include: ? Comprehensive coverage of fuzzy dynamical systems, robustness, stability and sensitivity -- giving the reader a good grasp of the fundamentals of fuzzy control ? Focus on the analytical structures of new fuzzy modeling approaches based on the Takagi-Sugeno-Kang (TSK) or Takagi-Sugeno (TS) model ? Applications of fuzzy control to aircraft systems, rocket engines and automotive engines ? Problems and examples illustrating how fuzzy approaches may be applied to the modeling, analysis and synthesis of closed-loop systems Design and control engineers will value the advanced control techniques and new design and analysis tools presented. Postgraduates studying fuzzy control will find this book a useful reference on synthesis, systems analysis and advanced nonlinear control methods. |
Contents
Information Granularity in the Analysis and Design of Fuzzy | 3 |
Fuzzy Modeling for Predictive Control | 23 |
References | 91 |
Copyright | |
15 other sections not shown
Common terms and phrases
adaptive algorithm analysis application approach approximation assumed asymptotically bounded chapter closed-loop compensator complete components considered control rules defined definite derived described desired determined developed dynamic engine equation equilibrium error estimate example exists expression Figure fuzzy control systems fuzzy logic fuzzy model fuzzy sets Fuzzy Systems gain Gain sch given global identification IEEE Transactions inequalities initial input learning linear linguistic LMIS load Lyapunov matrix means measure membership functions method non-linear observer obtained operating output parameter passive performance plant polynomial positive possible presented problem Proceedings Proof properties proposed ramp rate reference region represented respectively robust rules satisfied scheduling sensitivity shown signal simulation space specific stability stability analysis structure synthesis T-S fuzzy temperature Theorem theory University variables vector weights zero