Title :
Stable discrete-time neural control for robots using adaptive bounding techniques
Author :
Sun, Fuchun ; Sun, Zengqi
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
A neural network (NN)-based adaptive control law is proposed in this paper for the tracking control of an n-link robot manipulator with unknown dynamic nonlinearities. Locally-generalized neural networks are employed to approximate the plant nonlinearities, and a bound on the network reconstruction error is assumed to be unknown. The proposed NN-based adaptive control approach integrates a NN approach with an adaptive implementation of the dynamic sliding mode control, includes a simple estimation law for the upper bound on the NN reconstruction error and an additional control input to be updated as a function of the estimation. Using the Lyapunov stability theory, the uniform ultimate boundedness of the tracking error is proved
Keywords :
Lyapunov methods; adaptive control; discrete time systems; errors; manipulators; neurocontrollers; nonlinear control systems; stability; tracking; variable structure systems; Lyapunov stability theory; adaptive bounding techniques; adaptive control law; dynamic sliding mode control; estimation law; locally-generalized neural networks; n-link robot manipulator; network reconstruction error; neural network; plant nonlinearities; reconstruction error; robots; stable discrete-time neural control; tracking control; tracking error; uniform ultimate boundedness; unknown dynamic nonlinearities; upper bound; Adaptive control; Control nonlinearities; Error correction; Lyapunov method; Manipulator dynamics; Neural networks; Programmable control; Robot control; Sliding mode control; Upper bound;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.637336