Knowledge of human-exoskeleton interaction 1 forces is crucial to assess user comfort and effectiveness 2 of the interaction. The subject-exoskeleton collaborative 3 movement and its interaction forces can be predicted in 4 silico using computational modeling techniques. We devel-5 oped an optimal control framework that consisted of three 6 phases. First, the foot-ground (Phase A) and the subject-7 exoskeleton (Phase B) contact models were calibrated 8 using three experimental sit-to-stand trials. Then, the collab-9 orative movement and the subject-exoskeleton interaction 10 forces, of six different sit-to-stand trials were predicted 11 (Phase C). The results show that the contact models were 12 able to reproduce experimental kinematics of calibration 13 trials (mean root mean square differences (RMSD) coor-14 dinates ≤ 1.1°and velocities ≤ 6.8°/s), ground reaction 15 forces (mean RMSD≤ 22.9 N), as well as the interaction 16 forces at the pelvis, thigh, and shank (mean RMSD ≤ 5.4 N). 17 Phase C could predict the collaborative movements of pre-18 diction trials (mean RMSD coordinates ≤ 3.5°and veloc-19 ities ≤ 15.0°/s), and their subject-exoskeleton interaction 20 forces (mean RMSD ≤ 13.1 N). In conclusion, this optimal 21 control framework could be used while designing exoskele-22 tons to have in silico knowledge of new optimal movements 23 and their interaction forces.

Background Currently, control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Furthermore, assistance is often provided by imposing these desired trajectories using impedance controllers. However, lower-limb exoskeletons are also a promising solution for mobility problems of individuals in daily life. To develop an assistive exoskeleton which allows the user to be autonomous, i.e. in control of his motions, remains a challenge. This paper presents a model-based control method to tackle this challenge. Methods The model-based control method utilizes a dynamic model of the exoskeleton to compensate for its own dynamics. After this compensation of the exoskeleton dynamics, the exoskeleton can provide a desired assistance to the user. While dynamic models of exoskeletons used in the literature focus on gravity compensation only, the need for modelling and monitoring of the ground contact impedes their widespread use. The control strategy proposed here relies on modelling of the full exoskeleton dynamics and of the contacts with the environment. A modelling strategy and general control scheme are introduced. Results Validation of the control method on 15 non-disabled adults performing sit-to-stand motions shows that muscle effort and joint torques are similar in the conditions with dynamically compensated exoskeleton and without exoskeleton. The condition with exoskeleton in which the compensating controller was not active showed a significant increase in human joint torques and muscle effort at the knee and hip. Motor saturation occurred during the assisted condition, which limited the assistance the exoskeleton could deliver. Conclusions This work presents the modelling steps and controller design to compensate the exoskeleton dynamics. The validation seems to indicate that the presented model-based controller is able to compensate the exoskeleton.

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When walking robots and exoskeletons make multiple independent contacts, the inverse dynamics problem requires additional knowledge about the contact forces and moments. To avoid measuring the contact forces and moments, many inverse dynamics controllers for walking robots optimize an objective like minimizing torques or contact forces. In order to get a solution closer to the real solution, the underlying physical principles need to be included. This is achieved by relying on the minimization of complementary energy, which is a well known method in structural engineering. The proposed method relies on physical properties (stiffness) to obtain the additional knowledge to solve the contact forces and moments. In addition, it has the same form as the methods used in the literature. The validation on a bilateral lower-limb exoskeleton shows that the proposed method is able to predict the contact forces and moments sufficiently well, while being robust against modelling errors. Modelling the dominating flexibilities suffices to achieve adequate results, making this method especially interesting for series elastic actuated robots.