Adaptive Model-Based Hybrid Control of Geometrically Constrained Robot Arms

This paper reports comparative experiments with a new model-based adaptive force control algorithm for robot arms. This controller provides simultaneous position and force trajectory tracking of a robot arm whose tool tip is in point contact with a smooth rigid surface. The algorithm is provably stable with respect to the commonly accepted rigid-body nonlinear dynamical model for robot arms. Comparative experiments show the new adaptive model-based controller to provide performance superior to that of both (i) non model-based controllers and (ii) non adaptive controllers over a wide range of operating conditions. I

Power Assist Control Based on Human Motion Estimation Using Motion Sensors for Powered Exoskeleton without Binding Legs

In this study, we propose a novel power assist control method for a powered exoskeleton without binding its legs. The proposed method uses motion sensors on the wearer’s torso and legs to estimate his/her motion to enable the powered exoskeleton to assist with the estimated motion. It can detect the start of walking motion quickly because it does not prevent the motion of the wearer’s knees at the beginning of the walk. A nine-axis motion sensor on the wearer’s body is designed to work robustly in very hot and humid spaces, where an electromyograph is not reliable due to the wearer’s sweat. Moreover, the sensor avoids repeated impact during the walk because it is attached to the body of the wearer. Our powered exoskeleton recognizes the motion of the wearer based on a database and accordingly predicts the motion of the powered exoskeleton that supports the wearer. Experiments were conducted to prove the validity of the proposed method