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This paper presents a robust controller for tracking control of a direct-drive robot. The proposed controller consists of two portions: a computed torque method which precompensates for dynamics of the modeled plant and an [H.sub.[infinity]] controller which postcompensates for residual errors which are not completely removed by the computed torque method. Experimental methods for identifying appropriate model structure and parameters are presented, and three specific controller types are compared. Using the robot designed in our laboratory, the combined controller reduced tracking errors by one half compared to computed torque control alone, and by one sixth compared to conventional independent joint control.

This paper proposes a robust sensor fusion algorithm to accurately track the spatial location and motion of a human under various dynamic activities, such as walking, running, and jumping. The position accuracy of the indoor wireless positioning systems frequently suffers from non-line-of-sight and multipath effects, resulting in heavy-tailed outliers and signal outages. We address this problem by integrating the estimates from an ultra-wideband (UWB) system and inertial measurement units, but also taking advantage of the estimated velocity and height obtained from an aiding lower body biomechanical model. The proposed method is a cascaded Kalman filter-based algorithm where the orientation filter is cascaded with the robust position/velocity filter. The outliers are detected for individual measurements using the normalized innovation squared, where the measurement noise covariance is softly scaled to reduce its weight. The positioning accuracy is further improved with the Rauch–Tung–Striebel smoother. The proposed algorithm was validated against an optical motion tracking system for both slow (walking) and dynamic (running and jumping) activities performed in laboratory experiments. The results show that the proposed algorithm can maintain high accuracy for tracking the location of a subject in the presence of the outliers and UWB signal outages with a combined 3-D positioning error of less than 13 cm. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel method for simultaneous 3-D trajectory tracking and lower body motion capture (MoCap) under various dynamic activities such as walking and jumping. The proposed method uses wearable inertial sensors fused with an ultrawideband localization system using a cascaded Kalman filter-based sensor fusion algorithm, which consists of a separate orientation filter cascaded with a position/velocity filter. In addition, to further improve the joint angle tracking accuracy, anatomical constraints are applied to the knee joint. Currently, available self-contained inertial tracking methods are not only drift-prone over time but also their accuracy is degraded under fast movements with unstable ground contact states due to the lack of external references. However, our experimental results, which benchmark the system against a reference camera-based motion tracking system, show that the proposed fusion method can accurately capture the dynamic activities of a subject without drift. The results show that the proposed system can maintain similar accuracies between fast and slow motions in lower body MoCap and 3-D trajectory tracking. The obtained accuracies of the system for 3-D body localization and knee joint angle tracking for fast motions were less than 5 cm and 2.1°, respectively. [ABSTRACT FROM PUBLISHER]