Title :
Robust backstepping control of robotic systems using neural networks
Author_Institution :
Dept. of Electr. Eng., Texas Univ., San Antonio, TX, USA
Abstract :
Neural network (NN) controllers for the robust backstepping control of robotic systems in both continuous and discrete-time are presented. Control input is selected to achieve tracking performance for unknown nonlinear systems. Tuning methods are derived for the NN based on the delta rule. Novel weight tuning algorithms for the NN are obtained that are similar to ε-modification in the case of continuous-time adaptive control. Uniform ultimate boundedness of the tracking error and the weight estimates are presented without using the persistency of excitation (PE) condition. Certainty equivalence is not used and a regression matrix is not computed. No learning phase is needed for the NN and initialization of the network weights is straightforward
Keywords :
adaptive control; cerebellar model arithmetic computers; continuous time systems; discrete time systems; function approximation; neurocontrollers; robots; robust control; tracking; tuning; ϵ-modification; continuous-time adaptive control; delta rule; neural network controllers; robust backstepping control; tracking error; tracking performance; tuning methods; uniform ultimate boundedness; unknown nonlinear systems; weight tuning algorithms; Adaptive control; Backstepping; Control systems; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Robot control; Robust control;
Conference_Titel :
Decision and Control, 1998. Proceedings of the 37th IEEE Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
0-7803-4394-8
DOI :
10.1109/CDC.1998.760816