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Abstract:Evaluating lower limb exoskeletons directly with human subjects can expose users to risk when actuator faults, joint misalignment, or unsuitable assistance occur. Therefore, captured human motion must first be converted into commands that are executable by the robot hardware and repeatable across trials. This paper presents a three-stage offline command generation framework for reproducing lower limb motion and torque on a suspended bipedal robot platform used as a robotic bench system for exoskeleton evaluation. First, State-Dependent Riccati Equation control is applied to the robot dynamic model to obtain a reference torque trajectory associated with measured lower limb motion. Second, parameterized optimization converts this reference into trapezoidal joint velocity commands subject to motor speed and acceleration limits. Third, a proportional-integral-derivative linear quadratic regulator (PID-LQR) compensation refines the command profiles using experimental tracking data. Walking and squatting motions recorded by a Vicon motion capture system are reproduced on the suspended robot to evaluate tracking accuracy and repeatability. The results show that the average root mean square error (RMSE) and standard deviation (STD) of joint angles across repeated trials remain below 3° and 0.15°, respectively. Comparisons of joint angles and torques further show that the proposed method achieves lower maximum RMSE and STD values than the two baseline controllers in all reported cases. These results indicate that the proposed three-stage control provides repeatable and actuator-feasible motion reproduction on a suspended bipedal robot platform as a preliminary test environment for lower limb exoskeleton research before tests involving human subjects.

Submission history

From: PingKong Huang [view email]
[v1] Thu, 5 Jun 2025 07:00:10 UTC (350 KB)
[v2] Tue, 5 May 2026 11:21:42 UTC (2,902 KB)
[v3] Thu, 30 Jul 2026 04:05:32 UTC (5,227 KB)