Abstract
This paper presents a more efficient control algorithm for selecting suitable firing nodes and removing unimportant input variables automatically to improve the computational efficiency, reduce the number of firing rules and achieve good performance for the nonlinear control systems. A novel function-link fuzzy cerebellar model articulation controller (FLFC) is designed for uncertain nonlinear systems based on a multiple attribute decision-marking method known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed control system is comprised a TOPSIS FLFC (TFLFC) and a robust compensator. The parameters of the proposed TFLFC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so the system's stability is guaranteed. Finally, the proposed control system is used to control an inverted pendulum system and a master-slave Duffing-Holmes chaotic system to illustrate its favorable control performance.
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