In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF) to improve the performance of inertial measurement units (IMUs) based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link position estimation (e.g., the foot). In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.
In this study, the authors deal with inertial measurement units subject to uncertainties. They propose an extended robust Kalman filter (ERKF) in a predictor-corrector form to estimate a rigid body attitude. The filter is developed based on regularisation and penalisation whose approaches present the advantage of encompassing in a unified framework all state and output uncertain parameters of the system. The ERKF is tuned based on two degree of freedom which belong to a certain interval known a-priori, useful for online applications. The attitude estimation system proposed takes into account a rigid body model formulated in terms of quaternions. Experimental results are presented based on a comparative study among the ERKF, the standard extended Kalman filter and an H ∞ filter.
In this paper, nonlinear dynamic equations of a wheeled mobile robot are described in the state-space form where the parameters are part of the state (angular velocities of the wheels). This representation, known as quasi-linear parameter varying, is useful for control designs based on nonlinear H ∞ approaches. Two nonlinear H ∞ controllers that guarantee induced L 2 -norm, between input (disturbances) and output signals, bounded by an attenuation level γ , are used to control a wheeled mobile robot. These controllers are solved via linear matrix inequalities and algebraic Riccati equation. Experimental results are presented, with a comparative study among these robust control strategies and the standard computed torque, plus proportional-derivative, controller.
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