Introduction

Modeling practical physical systems frequently results in complex nonlinear systems, which poses great difficulties regarding system analysis and synthesis. Local linearization is a typical method used for the analysis and synthesis of nonlinear systems. However, it has been well recognized that the resulted local linearization model is valid only for a certain range of operating conditions, and can only guarantee the local stability of the original nonlinear system. Another approach, fuzzy control, emerged and developed following the first paper on fuzzy sets [243], has attracted great attention from both the academic and industrial communities. The reason lies much in its effectiveness in obtaining nonlinear control systems, especially when knowledge of the plant or even the precise control action of the situation is unknown. Thus, fuzzy control has even been found to have many applications in industrial systems and processes, see for example, [5, 7, 9, 12, 13, 14]. In fact, fuzzy control has proved to be a successful control approach for complex nonlinear systems. Fuzzy control has even been suggested as an alternative approach to conventional control techniques.Garrett Cullity and Berys Gau