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A tracking controller is developed for a class of uncertain nonlinear systems with bounded external disturbances and time-varying input delay. Inspired by predictor-based delay compensators, the current result exploits a linear transformation to map the time dependent control input to a modified control input that depends on both time and a spatial variable. This transformation enables the developed controller to be applied when the delay rate is upper bounded by an arbitrarily large bound, unlike previous robust control approaches. However, unlike previous predictor-based methods, the development of a novel auxiliary error signal that includes an integral of past control values facilitates a robust control design that is applicable to uncertain nonlinear systems. A Lyapunov-based stability analysis that uses Lyapunov-Krasovskii functionals is used to prove the developed controller yields exponential convergence of the tracking error to a steady-state residual (i.e., uniformly ultimately bounded (UUB) tracking errors). Numerical simulation results are also provided to illustrate the performance of the controller for different delay rates.

A tracking controller is developed for a class of uncertain Euler-Lagrange systems with bounded external disturbances and an uncertain time-varying input delay. A novel filtered tracking error signal is designed to obtain a delay-free input signal in the closed-loop error system to facilitate the control design and analysis. The designed novel filtered tracking error consists of a finite integral over a constant estimated delay interval of the past control inputs as a means to compensate for the input delay. The maximum tolerable error between unknown time-varying delay and a constant estimate of the delay can be determined based on the selection of the control gains to provide uniformly ultimately bounded convergence of the tracking error to the origin. Lyapunov-Krasovskii functionals are used in the Lyapunov-based stability analysis

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 examines the problem of link position tracking control of robot manipulators with bounded torque inputs. An adaptive, full-state feedback controller and an exact model knowledge, output feedback controller are designed to produce semi-global asymptotic link position tracking errors. Simulation results are provided to validate the theoretical concepts, and a comparative analysis demonstrates the benefits of the proposed controllers.