Title :
Neuro-fuzzy control of underwater robot based on disturbance compensation
Author :
Hongli Chen ; Wei Zheng ; Xiaodong Gai
Author_Institution :
Autom. Inst., Harbin Eng. Univ., Harbin, China
Abstract :
Adaptive nonlinear output feedback control of GD-FNN(generalized dynamic fuzzy neural network) for underwater robot motion control using forming filter for wave disturbance is presented. This method completely construct nonlinear and uncertain parts of underwater robot by online adaptive learning algorithm without knowing fuzzy neural structure and training phase in advance. Output feedback control guarantees initial stability of system. The speed that controller needs is got by dynamic compensation and Kalman filter, which solve coupling problem between freedom. Closed-loop stability of system is proved by Lyapunov and structure of controller is simple without accurate mathematical model of system. Finally, an underwater robot model is adopted to verify the effectiveness of the method.
Keywords :
Kalman filters; Lyapunov methods; adaptive control; closed loop systems; feedback; fuzzy control; fuzzy neural nets; interference suppression; mobile robots; motion control; neurocontrollers; nonlinear control systems; remotely operated vehicles; underwater vehicles; Kalman filter; Lyapunov method; adaptive learning algorithm; adaptive nonlinear output feedback control; closed loop system; disturbance compensation; forming filter; generalized dynamic fuzzy neural network; neuro-fuzzy control; robot motion control; system stability; underwater robot; wave disturbance; Artificial neural networks; Force; Kalman filters; Lead; Mathematical model; Robots; Trajectory; GD-FNN; Kalman filter; forming filter; nonlinear output feedback; underwater robot;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5553950
