Abstract

A novel fuzzy inference system is introduced with desirable approximation properties for highly nonlinear systems that can be expressed in linear parameter varying form. This fuzzy inference system uses a hashing function to eliminate unnecessary computations and is compared with existing fuzzy inference systems. Furthermore, an application to nonlinear systems state estimation is provided, and the result is compared with that of the extended Kalman filter. The main benefit of this novel fuzzy inference system is its suitability to resource-constrained embedded control and estimation applications. Furthermore, multidimensional sampling is applied to the state-space variables and it is shown that (de)fuzzification in control systems and (de)modulation in communication systems are analogous. Finally, the values of fuzzy submodels as quantum mechanical objects are explored, for stability analysis and feedback controller synthesis for a class of nonlinear systems, using artificial intelligence approaches. Simulations confirm the effectiveness of the proposed approach.