Noise-tolerant zeroing neural network control for a novel compliant actuator in lower-limb exoskeletons

To enhance the safety of direct physical interaction between humans and lower-limb exoskeletons, the actuator of the lower-limb exoskeleton should possess compliance, force controllability, and back-drivability. In this paper, a novel compact compliant force control actuator with worm gear and gear rack as transmission modes is designed to provide flexible power to the lower-limb exoskeleton. The worm gear is utilized to amplify the torque generated by the motor in the compact compliant actuator, while the gear rack is established to improve the bearing capacity. Yet, the friction is occurred between the worm gear, rack and other mechanical configurations which may lead to non-negligible force control error of the compliant actuator. A noise-tolerant zeroing neural network controller is proposed to suppress noises. Additionally, theoretical proofs are provided for the convergence performance of the noise-tolerant zeroing neural network controller, as well as the suppression of constant noise, linear disturbances, and bounded random noise. The proposed noise-tolerant zeroing neural network controller is validated on the compliant actuator through numerical simulations, experimental results, and comparison experiments, demonstrating its effectiveness in suppressing various noise and improving the convergence, stability, and robustness of the compliant actuator system. Additionally, the controller is further verified through walking experiments conducted on a knee robot.