An Active Disturbance Rejection Control based on Generalized Proportional Integral observer (ADRC with GPI observer) was developed to control the gait of a bipedal robot with five degrees of freedom. The bipedal robot used is a passive point feet which produces an underactuated dynamic walking. A virtual holonomic constraint is imposed to generate online smooth trajectories which were used as references of the control system. The proposed control strategy is tested through numerical simulation on a task of forward walking with the robot exposed to external disturbances. The performance of ADRC with GPI observer strategy is compared with a feedback linearization with proportional-derivative control. A stability test consisting on analyzing the existence of limit cycles using the Poincaré's method revealed that asymptotically stable walking was achieved. The proposed control strategy effectively rejects the external disturbances and keeps the robot in a stable dynamic walking.
This paper presents a disturbance rejection control strategy for hybrid dynamic systems exposed to model uncertainties and external disturbances. The focus of this work is the gait control of dynamic bipedal robots. The proposed control strategy integrates continuous and discrete control actions. The continuous control action uses a novel model-based active disturbance rejection control (ADRC) approach to track gait trajectory references. The discrete control action resets the gait trajectory references after the impact produced by the robot's support-leg exchange to maintain a zero tracking error. A Poincaré return map is used to search asymptotic stable periodic orbits in an extended hybrid zero dynamics (EHZD). The EHZD reflects a lower-dimensional representation of the full hybrid dynamics with uncertainties and disturbances. A physical bipedal robot testbed, referred to as Saurian, is fabricated for validation purposes. Numerical simulation and physical experiments show the robustness of the proposed control strategy against external disturbances and model uncertainties that affect both the swing motion phase and the support-leg exchange.
In this paper, a data-driven method for disturbance estimation and rejection is presented. The proposed approach is divided into two stages: an inner stabilization loop, to set the desired reference model, together with an outer loop for disturbance estimation and compensation. Inspired by the active disturbance rejection control framework, the exogenous and endogenous disturbances are lumped into a total disturbance signal. This signal is estimated using an on-line algorithm based on a datadriven predictor scheme, whose parameters are chosen to satisfy high robustness-performance criteria. The above process is presented as a novel enhancement to design a disturbance observer, which constitutes the main contribution of the paper. In addition, the control strategy is completely presented in discrete time, avoiding the use of discretization methods for its digital implementation. As a case study, the voltage control of a DC-DC synchronous buck converter affected by disturbances in the input voltage and the load is considered. Finally, experimental results that validate the proposed strategy and some comparisons with the classical disturbance observer-based control are presented.
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