This paper exposes a control architecture enabling rehabilitation of walking impaired patients with the lower-limb exoskeleton Atalante. Atalante's control system is modified to allow the patient to contribute to the walking motion through their efforts. Only the swing leg degree of freedom along the nominal path is relaxed. An online trajectory optimization checks that the muscle forces do not jeopardize stability. The optimization generates reference trajectories that satisfy several key constraints from the current point to the end of the step. One of the constraints requires that the center or pressure remains inside the support polygon, which ensures that the support leg subsystem successfully tracks the reference trajectory. As a result of the presented works, the robot provides a non-zero force in the direction of motion only when required, helping the patient go fast enough to maintain balance (or preventing him from going too fast). Experimental results are reported. They illustrate that variations of ±50% of the duration of the step can be achieved in response to the patient's efforts and that many steps are achieved without falling.

This paper describes a novel low-level controller for the lower-limb exoskeleton Atalante. The controller implemented on the commercialized product Atalante works under the assumption of full rigidity, performing position control through decentralized joint PIDs. However, this controller is unable to tackle the presence of flexibilities in the system, which cause static errors and undesired oscillations. We modify this controller by leveraging estimations of the position and velocity of the flexibilities, readily available on Atalante through the use of strapdown IMUs. Instead of considering feedback on the motor position only, we perform feedback on both the joint position and the flexibility angle, keeping a decentralized approach. This enables compensation of both the static error present at rest, and rapid damping of the oscillations. To tune the gains of the proposed controller, we use a linearized model of an elastic joint to which we apply a steady-state LQR, which creates desirable robustness to the flexible model. The proposed controller is experimentally validated through various single support experiments on Atalante, either empty or with a user. In all cases, the proposed controller outperforms the state-of- the-art controller, providing improved trajectory tracking and disturbance rejection.