This paper describes the design and implementation of a novel adaptive control method to track a set of bioinspired reference trajectories. These references define anthropomorphic movements for an exoskeleton robot. The proposed controller implemented the adjustment laws for the variable gains of a state feedback (Proportional-Derivative type) structure. The method to adjust the adaptive gains was determined using a controlled Lyapunov function. The adaptation laws use velocity estimation based on a robust exact differentiator (RED) implemented as a variation of a distributed Super-Twisting algorithm. The adaptive gain controller was evaluated on a simulated exoskeleton structure. The set of simulations considered the presence of external disturbances and modeling uncertainties. The controller proved efficient in rejecting external perturbations/uncertainties affecting the exoskeleton. The proposed controller’s performance was superior to the one obtained if the standard fixed-gain proportional derivative controller was evaluated. As an additional benefit of the adaptive PD controller implementation, a controller power reduction of at least 14 [Formula: see text] concerning the non-adaptive version of the feedback controller was attained. An experimental evaluation of the proposed controller confirmed the benefits of the proposed controller with adaptive gains. The successful tracking of nine different biomechanically inspired reference trajectories justified the exoskeleton application, which could be used as a potential tool for rehabilitation purposes.
The aim of this study was to design an output-based automatic controller that solves the trajectory tracking of a tomographic image acquisition robotic system. The technique used to design the controller was the active disturbance rejection control. The design process included the adaptation of controller gains depending on the tracking error. The application of this controller induced a smoother approaching of the tomographic image acquisition robotic system states to the reference trajectories. The output-based controller design offered a better approximation of the non-modeled sections in tomographic image acquisition robotic system yielding a better tracking of the reference states needed in the tomographic acquisition of images with potential application in medical diagnosis or treatment. The introduction of adaptive gains enforced a virtual increment in the number of states included in the extended state observer. The controller used the estimated velocity states calculated by an extended state adaptive observer. A set of numerical evaluations executed over a simulated version of the tomographic image acquisition robotic system proved the efficiency of the adaptive tracking trajectory controller. The quality of control execution was evaluated by determining the root mean square index of the tracking error, and the controller was also evaluated on an experimental platform. This part of the study compared the results of implementing three controllers: a basic proportional–derivative controller, a generalized proportional integral controller based on a constant gain observer, and the controller proposed in this study with time-varying gains. This comparison showed an equally faster convergence for all the three control evaluations but with smaller oscillations in the case of the adaptive version as well as a smaller steady-state error.
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