Evolution of Voronoi based fuzzy recurrent controllers
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Hybrid fuzzy logic control with genetic optimisation for a single-link flexible manipulator
Engineering Applications of Artificial Intelligence
A hierarchical neuro-fuzzy system to near optimal-time trajectory planning of redundant manipulators
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Machine Vision and Applications
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This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.