Optimal Impedance Voltage-Controller for Electrically Driven Robots

This paper presents a novel optimal impedance voltage-controller for Electrically Driven Lower Limb Rehabilitation Robots (EDLR). To overcome the dynamical complexities, and handle the uncertainties, the proposed method employs an expected forward model of the actuator. The existing value of lamped uncertainty is represented by the output difference of the model and system. A voltage-controller, which compensates for the uncertainties, is designed based on this uncertainty estimator. Parameters of the controller are optimized using genetic algorithms. Key contributions of this paper are I) uncertainty estimation through the expected model’s output, II) overcoming the changes in motors’ parameters, III) introducing a class of closed-loop system termed as “Repeatable”, and IV) designing an optimal impedance voltage-controller that is non-sensitive to the parameter variations. Significant merits of the approach are swift calculations, efficiency, robustness, and guaranteed stability. Furthermore, the simplicity of design, ease of implementation and model-free independent joint structure of the approach are noticeable. The method is compared with an adaptive robust sub-controller and a Taylor-series-based adaptive robust controller, through simulations in passive range of motion and active assistive rehabilitation exercises. The results show the superiority of the proposed method in tracking performance and the time of calculations.