Controller–observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system

Abstract

Rehabilitation is a hazardous task for a mechanical system, since the device has to interact with the human extremities without the hands-on experience the physiotherapist acquires over time. A gap needs to be filled in terms of designing effective controllers for this type of devices. In this respect, the paper describes the design of a model-based control for an electromechanical lower-limb rehabilitation system based on a Parallel Kinematic Mechanism. A controller observer was designed for estimating joint velocities, which are then used in a hybrid position/force control scheme. The model parameters are identified by customizing an approach based on identifying only the relevant system dynamics parameters. Findings obtained through simulations show evidence of improvement in tracking performance compared with those where the velocity was estimated by numerical differentiation. The controller is also implemented in an actual electromechanical system for lower-limb rehabilitation tasks. Findings based on rehabilitation tasks confirm the findings from simulations.

Sequential semidefinite optimization for physically and statistically consistent robot identification

“…Simulated joint positions and velocities are obtained via the numerical integration of (15). Note that C j (s) is usually known in robot identification, [21], but it can be identified by running the methodology described in [40] when necessary.…”

“…In Appendix B, we show that this relation holds if there is no modeling error and turns to X s = X nf . It is noted, that this DIDIM simulation procedure (15) requires the mass matrix H to be invertible along the simulated trajectory. The iteration procedure (18) does not, however, guarantee this requirement because it was not formally proven that the estimates of base parameters are physically consistent.…”

Force/position-based velocity control strategy for the lower limb rehabilitation robot during active training: design and validation

“…Ju et al developed a hybrid position/force controller for the rehabilitation robot combined with fuzzy logic to track the desired force along the preset motion direction ( Ju et al, 2005 ). Valera et al developed a hybrid control scheme based on the position/force information, which makes it possible to perform different lower limb rehabilitation exercises ( Valera et al, 2017 ). However, due to the position/force dynamic relationship being adjusted to increase robot compliance in impedance control, it increases the difficulty of guaranteeing the trajectory tracking effect of the robot in lower limb rehabilitation training ( Lv et al, 2017 ).…”