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The problem of robust control applied to adjust the configuration of an ankle prosthesis based on disturbance estimation has been addressed in this study. Active disturbance rejection control was the paradigm used for controlling the robotic prosthesis by means of a direct active estimation. Based on this active estimation, the robust controller implemented the disturbance cancellation providing a fast converge to the origin of the tracking error. The uncertainties affecting the prosthesis dynamics were identified by a high‐order extended state high gain observer. This identification was used to force the tracking between the actual position and force needed in the ankle prosthesis and some reference values obtained by a biomechanical gait cycle analysis. Therefore, the estimated states were used to implement a robust output feedback controller that was effective to reject actively the perturbations. This rejection implemented within the controller forced the trajectory tracking to a small vicinity of the origin. A strategy based on composite Lyapunov function served to prove that tracking problem for the prosthesis was successfully solved despite the switching nature of the gait cycle. The controller was implemented in numerical simulations for showing the convergence of the tracking error. The convergence of this tracking error to the region around the origin was obtained within the first second of simulation. [ABSTRACT FROM AUTHOR]

A flatness based approach is proposed for the linear Active Disturbance Rejection Control (ADRC) stabilization of a nonlinear inertia wheel pendulum (IWP) around its unstable equilibrium point, subject to unmodelled dynamics and disturbances. The approach exploits the cascade structure, provided by the flatness property, of the tangent linearization of the underactuated system which allows designing a high gain linear cascaded Extended State Observer (ESO) of the Generalized Proportional Integral (GPI) type. This class of linear observers is employed to build an Active Disturbance Rejection Control controller with a lower order of complexity regarding other ADRC classic schemes. Experimental results demonstrate the effectiveness and feasibility of the proposed approach, as well as a better behavior with respect to a classic control technique in the presence of disturbances. [ABSTRACT FROM AUTHOR]

This study addressed the problem of robust control of a biped robot based on disturbance estimation. Active disturbance rejection control was the paradigm used for controlling the biped robot by direct active estimation. A robust controller was developed to implement disturbance cancelation based on a linear extended state observer of high gain class. A robust high-gain scheme was proposed for developing a state estimator of the biped robot despite poor knowledge of the plant and the presence of uncertainties. The estimated states provided by the state estimator were used to implement a feedback controller that was effective in actively rejecting the perturbations as well as forcing the trajectory tracking error to within a small vicinity of the origin. The theoretical convergence of the tracking error was proven using the Lyapunov theory. The controller was implemented by numerical simulations that showed the convergence of the tracking error. A comparison with a high-order sliding-mode-observer-based controller confirmed the superior performance of the controller using the robust observer introduced in this study. Finally, the proposed controller was implemented on an actual biped robot using an embedded hardware-in-the-loop strategy. [ABSTRACT FROM AUTHOR]

In this article, the trajectory tracking control of a solar tracking system is tackled by means of an adaptive active disturbance rejection control scheme. The state and disturbance estimation system is based on the combination of a time varying identification system and an adaptive observer. The stability and robustness of the controller is mathematically tested by means of the second method of Lyapunov, and its effectiveness is experimentally tested in a robotic test bed, achieving both lower energy consumption and better tracking results with respect to a PID-based controller. [ABSTRACT FROM AUTHOR]