In this paper, three challenges often encountered when upper limb rehabilitation robots are integrated with impaired people are addressed. Firstly, estimation of Desired Intended Motion (DIM) of the robot’s wearer is achieved. Secondly, robust adaptive impedance control based on the Modified Function Approximation Technique (MFAT) is designed. Lastly, a new Integral Nonsingular Terminal Sliding Mode Control (INTSMC) is suggested. In particular, the integration of INTSMC enriches the system by ensuring continuous performance tracking of system’s trajectories, high robustness, fast transient response, finite-time convergence, and chattering reduction. Besides, the MFAT strategy approximates the dynamic model without collecting any prior knowledge of the lower and upper bounds of the dynamic model’s individual uncertainties. Furthermore, leveraging Radial Basis Function Neural Network (RBFNN) to link estimated DIM to the adaptive impedance control allows the upper limb robot to easily track the target impedance model. Finally, in efforts to validate the scheme in real-time, controlled experimental cases are conducted using the exoskeleton robot.

在本文中，解决了上肢康复机器人与残疾人集成时经常遇到的三个挑战。首先，实现了对机器人佩戴者的预期运动（DIM）的估计。其次，设计了基于修正函数逼近技术(MFAT)的鲁棒自适应阻抗控制。最后，建议使用新的整体非奇异终端滑模控制 (INTSMC)。特别是，INTSMC 的集成通过确保系统轨迹的连续性能跟踪、高鲁棒性、快速瞬态响应、有限时间收敛和减少颤动来丰富系统。此外，MFAT 策略在不收集动态模型个体不确定性上下界的任何先验知识的情况下逼近动态模型。此外，利用径向基函数神经网络 (RBFNN) 将估计的 DIM 链接到自适应阻抗控制，允许上肢机器人轻松跟踪目标阻抗模型。最后，为了实时验证该方案，使用外骨骼机器人进行受控实验案例。