【智能控制理论讲解】CMAC-application.pdfVIP

22 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 30, NO. 1, FEBUARY 2000 Optimal Design of CMAC Neural-Network Controller for Robot Manipulators Young H. Kim and Frank L. Lewis, Fellow, IEEE Abstract— This paper is concerned with the application neural-network based, closed-loop control can be found [12]. of quadratic optimization for motion control to feedback For indirect or identification-based, robotic-system control, sev- control of robotic systems using cerebellar model arithmetic eral neural network and learning schemes can be found in the lit- computer (CMAC) neural networks. Explicit solutions to the Hamilton–Jacobi–Bellman (H–J–B) equation for optimal control erature. Most of these approaches consider neural networks as of robotic systems are found by solving an algebraic Riccati equa- very general computational models. Although a pure neural-net- tion. It is shown how the CMAC’s can cope with nonlinearities work approach without a knowledge of robot dynamics may be through optimization with no preliminary off-line learning phase promising, it is important to note that this approach will not be required. The adaptive-learning algorithm is derived from Lya- very practical due to high dimensionality of input–output space. punov stability analysis, so that both system-tracking stability and error convergence can be guaranteed in the closed-loop system. In this way, the training or off-line learning process by pure con- The filtered-tracking error or critic gain and the Lyapunov nectionist models would require a neural network of impractical function for the nonlinear analysis are derived from the user input