TL;DR
Hardware identification, sensitivity assessment, and three repeated hardware trials show that RL-PALS reduces the 99th-percentile absolute tracking error and establishes recursive nonemptiness of the feasible correction set.
Bounded residual learning is supported as a practical robustness-enhancement strategy for simulation-based rehabilitation robot control and will motivate further constraint-aware and experimental validation.
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...
This paper presents a hybrid, constraint-aware tracking architecture for quadrotor UAVs that explicitly targets the failure mode arising from the combination of actuator saturation and loss-of-effectiveness (LOE) faults. While model-based controllers augmented with adaptive terms can compensate for matched uncert...
Robotic systems frequently operate under parametric uncertainties and constrained communication bandwidths, motivating data-driven control architectures that ensure optimal performance without explicit model identification. Conventional adaptive dynamic programming (ADP) methods for output-feedback control typically re...
This paper proposes an adaptive prescribed-time tracking control strategy based on the dynamic surface technique for hydraulic servo systems subject to time-varying parameters, external disturbances, and output constraints. First, a state-constrained transformation function is introduced to convert the strict output co...
This paper proposes a multi joint robotic arm visual servo robust control strategy based on neural networks and daptive command filtering, aiming to solve dynamic uncertainty and nonlinear problems. By constructing an image plane feature point error model and deriving the image Jacobian matrix, a mapping relationship b...
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