During the development of wearable exoskeletons, evaluations involving human subjects pose inherent safety risks. Therefore, systematic testing is often conducted using robots that emulate human motion. However, reproducing human movements is challenging due to differences in robot structure and actuator characteristics. This study proposes a three-stage offline control strategy that uses motion-capture data and robot-specific properties to generate control commands for accurate motion replication. First, an optimal torque trajectory is generated via a State-Dependent Riccati Equation (SDRE) controller based on the dynamic model of the bipedal system. Second, joint velocity and acceleration command sequences are synthesized through parameterized optimization under actuator constraints. Finally, a data-driven PID-LQR offline controller refines these commands by minimizing the tracking error between the desired and executed motions. Experimental validation is performed on a suspended bipedal robot platform designed for the evaluation of gravity-counteracting exoskeletons. Motion-capture data collected from squatting and walking tasks are used for system assessment. The experimental results demonstrate high tracking fidelity, with an average root mean square error (RMSE) below 3 degrees. These results verify the effectiveness of the proposed three-stage control strategy for robot-based systematic testing of exoskeletons.

翻译：在可穿戴外骨骼的开发过程中，涉及人体实验的评估存在固有安全风险。因此，通常采用能模拟人类运动的机器人进行系统化测试。然而，由于机器人结构及执行器特性的差异，复现人体运动具有挑战性。本研究提出一种三阶段离线控制策略，利用运动捕捉数据和机器人特定属性生成控制指令，以实现精确的运动复现。首先，基于双足系统的动力学模型，通过状态相关黎卡提方程（SDRE）控制器生成最优力矩轨迹。其次，在执行器约束条件下，通过参数化优化合成关节速度与加速度指令序列。最后，数据驱动的PID-LQR离线控制器通过最小化期望运动与执行运动之间的跟踪误差来优化这些指令。实验验证在专为评估重力补偿外骨骼设计的悬挂式双足机器人平台上进行，采用从蹲起与行走任务中采集的运动捕捉数据评估系统。实验结果表明，该方案具有高跟踪保真度，平均均方根误差（RMSE）低于3度。这些结果验证了所提三阶段控制策略在基于机器人的外骨骼系统化测试中的有效性。