The variable stiffness actuator (VSA) has been equipped on many new generation of robots because of its superior performance in terms of safety, robustness and flexibility. However, the control of robots with joints driven by variable stiffness actuators is challenging due to the inherited highly nonlinear dynamics. In this paper, a novel disturbance observer based adaptive neural network control is developed for robotic systems with variable stiffness joints and subject to model uncertainties.

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... By utilizing a high dimensional integral-type Lyapunov function, adaptive neural network control is designed to approximate the model uncertainties, and a disturbance observer is integrated to compensate for the nonlinear VSA dynamics, as well as the neural network approximation errors and external disturbance. The semiglobally uniformly ultimately boundness of the closedloop control system has been theoretically established. Simulation and extensive experimental studies have also been performed to verify the effectiveness of the proposed approach.