A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions

This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via F/T sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1 degree-of-freedom testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload.Comment: Accepted in IEEE/ASME Transactions on Mechatronics (T-MECH

Development of rotary hydro-elastic actuator with robust internal-loop-compensator-based torque control and cross-parallel connection spring

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A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions

This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot, which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via force/torque (F/T) sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1-DoF testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload

Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots

This paper proposes a coupled-error-based formation control algorithm for the rapid formation completion of multi-robot systems. We consider a multi-robot system with omni-directional robots with swerve-driving mechanisms and a communication system with minimized constraints. This paper introduces a coupled error that links the distance errors with the leading robot and the following robot through a coupling ratio. We propose a controller using the coupled error to achieve the control objectives of this paper. Unlike existing results that only use the information of the preceding robot, this algorithm couples the information of both the preceding robot and one’s follower. Using the proposed error-coupling-based formation control algorithm, multi-robot systems can quickly establish formations for collaboration, allowing tasks to commence swiftly and reducing deformations in formations due to speed variations. With stability analysis and simulation results for the practical application of the proposed algorithm, the approach has been verified to improve the speed of both the completion of the formation and overall system trajectory tracking, balancing trade-offs between them