TL;DR
A two-stage framework that integrates robust physical parameter identification with residual compensation is proposed that effectively attenuates measurement noise and outliers, yielding a significant reduction in torque prediction errors.
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...
Nonlinear model predictive control (NMPC) is widely used in robotics due to its ability to handle nonlinear dynamics and explicitly enforce state and input constraints by solving an optimal control problem online. However, standard NMPC implementations typically rely on direct time discretization and evaluate costs and...
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...
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.
A contact-responsive high-gain motion controller (CRMC) that ensures accurate tracking in free motion while adapting to unknown interactions, even without joint torque sensing is presented.
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