Adaptive control of NTV plants without persistent excitations: an application in robotics




Yuan, Jing

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Adaptive control of a nonlinear time varying (NTV) plant, such as a robotic manipulator, is intended to tolerate the unmodeled disturbances and the uncertain parameters of the dynamic model. Most of the previous research has been focused on NTV plants with bounded and "slowly-varying" plant terms. Almost all adaptive controllers require persistent excitations to guarantee stable tracking in the presence of unmodeled disturbances. The new adaptive controllers developed in this work provide stable and robust performance without persistent excitations and the "slowly-varying" assumption. Moreover, the uncertainties of a NTV plant model are not required to be bounded. This allows one to treat some potentially unbounded dynamics as disturbances. Stability and robustness analysis of adaptive controllers under the relaxed conditions is an essential part of this study. A major problem arising in robotic control is parameter uncertainty. The linear parameterization approach is also implemented in this work to deal with the parameter uncertainty. An innovative algorithm for determining the manipulator "regressor" (a coefficient matrix in parameter-linearized form of robot dynamics) is developed. Based on this algorithm a robust self-tuning controller is designed. The control law is proved to be robust with respect to parameter errors and disturbances. The robustness of the controller relaxes the requirement for the parameter estimator, and leads to a stable system without persistent excitations.



Robots, control systems, Nonlinear control theory, Control theory