Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation application of the Berkeley Lower Extremity Exoskeleton (BLEEX). Since the SAC algorithm can greatly reduce the complexity of exoskeleton system, it is widely used in human augmentation applications. Nevertheless, the performance of the SAC algorithm depends on the accuracy of dynamic model parameters. In this paper, we propose a novel Model-based control with Interaction Predicting (MIP) strategy to lower dependency on the accurate dynamic model of the exoskeleton. The MIP consists of an interaction predictor and a model-based controller. The interaction predictor can predict motion trajectories of the pilot and substitute for the pilot to drive the exoskeleton through an impedance model. In proposed strategy, the model-based controller not only amplify the forces initiated by the interaction predictor, but more importantly the forces imposed by the pilot to correct the errors between the predictive motion trajectory and the intended motion trajectory of the pilot. Illustrative simulations and experimental results are presented to demonstrate the efficiency of the proposed strategy. Additionally, the comparisons with traditional model-based control algorithm are also presented to demonstrate the efficiency and superiority of the proposed control strategy for lowering dependency on dynamic models.

灵敏度放大控制（SAC）算法首先在伯克利下肢外骨骼（BLEEX）的增强应用中提出。由于SAC算法可以大大降低外骨骼系统的复杂性，因此被广泛用于人体增强应用中。尽管如此，SAC算法的性能取决于动态模型参数的准确性。在本文中，我们提出了一种新颖的基于模型的控制与交互预测（MIP）策略，以降低对外骨骼精确动态模型的依赖性。MIP由交互预测器和基于模型的控制器组成。相互作用预测器可以预测飞行员的运动轨迹并替代飞行员通过阻抗模型来驱动外骨骼。在拟议的策略中，基于模型的控制器不仅放大了交互预测器所发起的力，而且更重要的是，飞行员施加的力来校正飞行员的预测运动轨迹与预期运动轨迹之间的误差。给出了说明性的仿真和实验结果，以证明所提出策略的效率。此外，还与传统的基于模型的控制算法进行了比较，以证明所提出的控制策略的有效性和优越性，从而降低了对动态模型的依赖性。说明性的仿真和实验结果被提出来证明所提出的策略的效率。此外，还与传统的基于模型的控制算法进行了比较，以证明所提出的控制策略的有效性和优越性，从而降低了对动态模型的依赖性。说明性的仿真和实验结果被提出来证明所提出的策略的效率。另外，还提出了与传统的基于模型的控制算法的比较，以证明所提出的用于降低对动态模型的依赖性的控制策略的效率和优越性。