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

[EN] 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 customising 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.This work was partially financed by the Plan Nacional de I+D, Comision Interministerial de Ciencia y Tecnologia (FEDERCICYT) under the project DPI2013-44227-R and by the Instituto U. de Automatica e Informatica Industrial (ai2) of the Universitat Politecnica de Valencia.Valera Fernández, Á.; Díaz-Rodríguez, M.; Vallés Miquel, M.; Oliver, E.; Mata Amela, V.; Page Del Pozo, AF. (2017). Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system. International Journal of Control. 90(4):702-714. https://doi.org/10.1080/00207179.2016.1215529S702714904Åström, K. J., & Murray, R. M. (2010). Feedback Systems. doi:10.2307/j.ctvcm4gdkAtkeson, C. G., An, C. H., & Hollerbach, J. M. (1986). 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An adaptive cruise control for connected energy-saving electric vehicles

We present an energy-saving cooperative adaptive cruise control (eco-CACC), which minimizes the energy consumption of autonomous electric vehicles. The approach leverages a trajectory preview from the preceding vehicle, and conciliates inter-vehicular distance reduction and speed profile smoothing. The problem is tackled with a nonlinear MPC approach. Rather than tracking a reference trajectory, our approach allows variations of distance and speed between vehicles, as long as the powertrain energy consumption is minimized and collision avoidance is guaranteed. Simulations show that this formulation can successfully handle real-world driving conditions, with limited computational complexity