Actuators

Integral Sliding Mode Control (ISMC) is widely employed in motor position control systems due to its robustness against uncertainties. However, its control performance is critically dependent on the selection of the switching gain. Although Disturbance Observer-Based Control (DOBC) is commonly adopted as an effective alternative for uncertainty compensation, it may exhibit limitations when high gains are required, potentially leading to system instability. To address these issues, this study proposes a Radial Basis Function Neural Network (RBF-NN)-based supervisory learning approach designed to minimize switching gain requirements. The effectiveness of the proposed scheme is validated through comparative simulations and laboratory experiments, specifically under scenarios involving system parameter uncertainties and sinusoidal disturbances with unknown offsets. Both simulation and experimental results demonstrate the superior performance of the proposed RBF-NN approach in terms of switching gain reduction and tracking error norms compared to a conventional ISMC and a DOBC-based cascade P–PI controller.

High-precision torque regulation is essential to ensure reaction wheel systems meet the stringent attitude control requirements of modern spacecraft. In three-phase half-bridge brushless DC (BLDC) drives, non-ideal back-electromotive force (back-EMF) waveforms cause pronounced conduction interval torque ripple, leading to inaccurate and unstable output torque. To address this problem, this article proposes a composite torque control strategy integrating an Adaptive Nonsingular Fast Terminal Sliding-Mode Observer (ANFTSMO) with an Adaptive Sliding-Mode Controller (ASMC). The ANFTSMO achieves precise back-EMF estimation and electromagnetic torque reconstruction by eliminating singularities, reducing chattering, and adaptively adjusting observer gains. Meanwhile, the ASMC employs an adaptive switching gain function to achieve asymptotic current convergence with suppressed chattering, thereby ensuring accurate current tracking. System stability is verified via Lyapunov analysis. Simulation and experimental results demonstrate that, compared with conventional constant-current control, the torque smoothness and disturbance rejection of the proposed method are improved, enabling precise and stable reaction wheel torque delivery for high-accuracy spacecraft attitude regulation.

This paper presents the design, development, and evaluation of a proof-of-concept arm swing facilitator device (ASFD) to promote proper arm swing during gait training. Although coordinated arm swing plays a critical role in human locomotion and neurorehabilitation, few wearable systems have been developed to integrate it into gait training. The ASFD was designed to test the feasibility of generating torque at the shoulder joint to initiate arm flexion–extension motion while allowing other shoulder degrees of freedom to move freely. The device induced cyclic arm motion at 1 Hz, producing sufficient torque while maintaining ergonomic criteria, such as a large workspace and back-mounted actuation to minimize arm load. The system incorporated a double-parallelogram mechanism to expand the workspace and a two-stage pulley–belt transmission to amplify torque. Testing showed that the ASFD produced up to 15 N·m and 11 N·m torques in static and dynamic load tests, respectively. Kinematic and experimental analyses confirmed sufficient motion freedom, except for some constraints in rotation. Human subject experiment demonstrated that the ASFD successfully induced arm swing within the 0.8–1.2 Hz frequency range and torques below 11 N·m. The ASFD met its design objectives, establishing a foundation for future development aimed at gait rehabilitation applications.