Hierarchical reinforcement learning for safe output tracking: managing constraints of dependent internal states in unknown discrete-time systems

Reinforcement-Learning Robust Tracking Control of Discrete-Time Systems with Diagonal Scaling

This paper addresses a robust tracking problem for linear discrete-time systems by proposing a reinforcement learning (RL) control method based on a diagonal-scaling strategy, offering a solution tailored to the demands of enhanced reliability. To overcome a common limitation in policy-iteration-based RL design, namely...

Reinforcement Learning-Based Predefined-Time Consensus Control for Uncertain Multirobot Systems with Improved Prescribed Performance Function

Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation

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...

Hierarchical Reinforcement Learning Control of a Quadruple Tank Plant Under Partial Observability

Experimental results in simulation and on real hardware demonstrate that the decentralized–supervised architecture can achieve comparable or improved aggregate tracking performance relative to a centralized policy, while preserving decentralized proposal generation and enabling execution-time supervisory coordination u...

Stabilizing Dynamical Systems with Model-Free Control: A Deep Q-Network Approach

This approach demonstrates the potential of DQN in scenarios where system assumptions and prior knowledge are impractical or unavailable, while acknowledging that the empirical success in the benchmark environment does not constitute formal control-theoretic stability guarantees.

Decoupled Time-Delay Koopman Learning for Adaptive Model Predictive Control of Soft Manipulators

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...