TAILIEUCHUNG - TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 1, JANUARY 2001

Technology has affected society and its surroundings in a number of ways. In many societies, technology has helped develop more advanced economies (including today's global economy) and has allowed the rise of a leisure class. Many technological processes produce unwanted by-products, known as pollution, and deplete natural resources, to the detriment of the Earth and its environment. Various implementations of technology influence the values of a society and new technology often raises new ethical questions. Examples include the rise of the notion of efficiency in terms of human productivity, a term originally applied only to machines, and the challenge of. | 54 IEEE TRANSACTIONS ON NEURAL NETWORKS VOL. 12 NO. 1 JANUARY 2001 Neural Network-Based Adaptive Controller Design of Robotic Manipulators with an Observer Fuchun Sun Member IEEE Zengqi Sun Senior Member IEEE and Peng-Yung Woo Member IEEE Abstract A neural network NN -based adaptive controller with an observer is proposed in this paper for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies. Index Terms Adaptive control neural networks NNs observer robot stability. I. Introduction Robotic manipulators are complicated nonlinear dynamical systems with inherent unmodeled dynamics and unstructured uncertainties. These dynamical uncertainties make the controller design for manipulators a difficult task in the .

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