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An accurate method is proposed to measure the electrical parameters describing the experimental charge‐discharge curves of Li‐ion batteries and a pack through an equivalent electric dynamic model based on heuristic optimization algorithms (eg, differential evolution approach). The accuracy of these parameters is critical for on‐board state of charge and Health (SOH) estimations of numerous applications and devices. The procedure is constructed, based on the simple Thévenin electrical model. Charge‐discharge experimental curves collected for different chemistry and capacity batteries are described with the model, revealing better estimation results with respect to other algorithms such as impedance‐based methodologies (decreasing the error until 64.19%) and particle swarm optimization based approaches (decreasing the error until 94.02%). The proposal was assessed with different chemistries, capacities and with a 28 battery pack (LiFeMnPO4, 100 Ah) used in an electric vehicle. [ABSTRACT FROM AUTHOR]

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]

In this article, the problem of robust trajectory tracking, for a parallel robot is tackled via an observer-based active disturbance rejection controller. The proposed design method is based on purely linear disturbance observation and linear feedback control techniques modulo nonlinear input gain injections and cancellations. The estimations are carried out through Generalized Proportional Integral ( GPI) observers, endowed with output integral injection to ease the presence of possible zero mean measurement noise effects. As the lumped (both exogenous and endogenous) disturbance inputs are estimated, they are being used in the linear controllers for on-line disturbance cancellation, while the phase variables are being estimated by the same GPI observer. The estimations of the phase variables are used to complete a linear multivariable output feedback controller. The proposed control scheme does not need the exact knowledge of the system, which is a good alternative to classic control schemes such as computed torque method, reducing the computation time. The estimation and control method is approximate, ensuring small as desired reconstruction and tracking errors. The reported results, including laboratory experiments, are better than the results provided by the classical model-based techniques, shown to be better when the system is subject to endogenous and exogenous uncertainties. [ABSTRACT FROM AUTHOR]