Sep 2026· International Journal of Precision Engineering and Manufacturing· Vol 27, pp. 3631 - 3639· 1 citation· 19 references
TL;DR
A physics-informed learning framework that explicitly separates stabilization from learning is proposed, in which closed-loop stability is guaranteed a priori by a PIV–SMC architecture, while a radial basis function (RBF) neural network learns only the residual open-loop acceleration required to realize the prescribed ideal acceleration dynamics.
Soft pneumatic actuators offer inherent compliance and safe interaction but remain difficult to model and control because of their highly nonlinear, distributed dynamics. We present a control-oriented data-driven modeling and control framework that decomposes actuator behavior into a nonlinear static equilibrium model...
The modeling and control of soft pneumatic manipulators present significant challenges due to their inherent compliance and history-dependent hysteresis. While the Koopman operator theory offers a promising solution by embedding these nonlinear dynamics into a linear framework, conventional Koopman approaches are limit...
Yuxuan Chen, Yun-Peng Zhu, Jian-Da Han et al.· IEEE Robotics and Automation...· 0 citations
Experimental results demonstrate that the proposed Fixed-Time control scheme reduces the steady-state tracking errors and vibration suppression, and fully validate the superiority and robustness of the proposed control strategy in balancing rapid convergence with vibration suppression.
He-Jia Gao, Tan-Yu Chen, Jiang-Xu Liu et al.· CAAI Artificial Intelligence...· 0 citations
A residual reinforcement learning DOB framework that pairs an analytical observer with an RL policy is proposed that derived and enforced a state-dependent action bound on the RL policy from an input-to-state stability (ISS) analysis such that the closed loop provably confines the tracking error to a certified envelope...
Jihong Kim, Joonhyuk Kwon, H. Kim et al.· 0 citations
Multi-motor synchronous control (MMSC) is essential for the integrated robotic joint, where synchronization performance directly impacts accuracy and stability in space-constrained applications. Traditional control methods often overlook this coupling and fail to balance disturbance rejection with energy optimization—a...
Lin Qiao, Gang Wang, Song Han et al.· Measurement science and tech...· 0 citations
In follow-up control of large-inertia turntables, directly applying a position-loop controller designed for point-to-point positioning often leads to frequent start–stop behaviors and pronounced velocity oscillations. To address this issue, this paper proposes a model predictive control (MPC)-based position-loop strate...
