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This paper addresses the problem of global output feedback, link position tracking control of robot manipulators. Specifically, a robust, Lyapunov-based controller is designed to ensure that the link position tracking error is globally uniformly ultimately bounded despite the fact that only link position measurements are available in the presence of incomplete model information (i.e., parametric uncertainty and additive bounded disturbances).
This paper presents a solution to the global adaptive partial state feedback control problem for rigid-link, flexible-joint (RLFJ) robots. The proposed tracking controller adapts for parametric uncertainty throughout the entire mechanical system while only requiring link and actuator position measurements. A nonlinear filter is employed to eliminate the need for link velocity measurements while a set of linear filters is utilized to eliminate the need for actuator velocity measurements. A backstepping control strategy is utilized to illustrate
global asymptotic link position tracking. An output feedback controller that adapts for parametric uncertainty in the link dynamics of the robot manipulator is presented as an extension. Experimental results are provided as verification of the proposed controller.
This paper addresses the link position setpoint control problem of n–link robotic manipulators with amplitude-limited control inputs. We design a global-asymptotic exact model knowledge controller and a semi-global asymptotic
controller which adapts for parametric uncertainty. Explicit bounds for these controllers can be determined; hence, the required input torque can be calculated a priori so that actuator saturation can be avoided. We also illustrate how the proposed control algorithm in
this paper can be slightly modified to produce a proportional-integral-derivative (PID) controller which contains a saturated integral term. Experimental results are provided to illustrate the improved performance of the proposed control strategy over a standard adaptive controller that has been artificially limited to account for torque saturation.
This paper provides a solution to the composite adaptive output feedback tracking control problem for robotic manipulators. The proposed controller utilizes an update law that is a composite of a gradient update law driven by the link position tracking error and a least squares update law driven by the prediction error. In order to remove the controller's dependence on link velocity measurements, a linear filter and a new prediction error formulation are designed. The controller provides semi-global asymptotic link position tracking performance. Experimental results illustrate that the proposed controller provides improved link position tracking error transient performance and faster parameter estimate convergence in comparsion to the same controller using a gradient update law.
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