Title :
Fuzzy Logic-based Neural Modeling and Robust Control for Robot
Author :
Liu, Zhi ; Zhang, Yun
Author_Institution :
Dept. of Autom., Guangdong Univ. of Technol., Guangzhou
Abstract :
A fuzzy logic-based neural modeling and robust control method is presented for biped robots. The dynamic inversion of biped robots is used to compensate the complex nonlinearity of the dynamic system and is approximated by the fuzzy neural network. To handle the construction errors of FNN, the Hinfin controller is designed to attenuate the effects of the construction errors to a prescribed level. By integrating the Hinfin method and the fuzzy logic-based neural modeling technique together, the proposed control method can guarantee the robust performance of the closed loop system. Simulation results show that the proposed method is effective
Keywords :
Hinfin control; adaptive control; closed loop systems; control nonlinearities; control system synthesis; fuzzy control; fuzzy neural nets; inverse problems; legged locomotion; neurocontrollers; nonlinear dynamical systems; robot dynamics; Hinfin controller design; adaptive control; biped robots; closed loop system; dynamic inversion; dynamic system; fuzzy logic; fuzzy neural network; inverse system; neural modeling; nonlinearity compensation; robust control; Control systems; Error correction; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Legged locomotion; Orbital robotics; Robot control; Robotics and automation; Robust control; Adaptive control; Fuzzy neural networks; Inverse system method; Robotic control; Robust control;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713736
