Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 1649-1654· 0 citations· 20 references
Abstract
This paper proposes a Lyapunov-guided post-action shielding mechanism for deep reinforcement learning (DRL) controllers under bounded actuation disturbances. In addition, an energy-safety requirement is formulated as a one-step energy threshold constraint that keeps the predicted next-state energy proxy within a prescribed limit, using a bounded-disturbance worst-case check. Simulation results show that the proposed mechanism substantially reduces constraint violations under actuation noise while preserving the nominal policy behavior whenever possible.
Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates. Safety filters separate them at action execution, but classical designs require an analytic safety function and dynamics model, and standard minimal-intervention filters are...
Song-Yuan Zhang, Baljeet Singh, Sarthak R. Kaingade et al.· 0 citations
Learning-based microgrid energy management has matured rapidly, yet existing safe-reinforcement-learning (RL) and stochastic-MPC hybrids typically treat the AC network as a power-balance constraint and the battery as a scalar state-of-charge integrator. Both abstractions discard the physics that determines whether a co...
Ali Sayghe, Ahmad Bakht Sultan, M. Mousa· IEEE Access· 0 citations
This paper proposes a reinforcement learning (RL) framework for safe gain scheduling based on forwardinvariance-induced action-space design. Under stated nominalmodel and inversion-domain assumptions, rather than enforcing safety through runtime shielding or penalty-based constraints, safety is embedded directly into t...
Chieh-Lin Tsai, M. Zahed, Jin-Zhi Shen et al.· 0 citations
Maintaining stable velocity control for a self-propelled pipeline inspection robot remains challenging under high-dimensional state spaces, time-varying friction, and non-stationary operating conditions. This study proposes a safety-constrained deep reinforcement learning control strategy for robust velocity regulation...
Zhong-Chao Zhang, Guangqi Wang, Yi-Ming Li et al.· ISA transactions· 0 citations
Comparative results indicate that the spectral entropy of the internal state of the internal state (motor current) is significantly lower with a narrower probability distribution under the proposed scheme, validating its enhanced stability and smoothness.
C. Treesatayapun· Neural computing & applicati...· 0 citations
Active vibration control of mechanical structures is unavoidably affected by bounded parametric uncertainty caused by varying payloads, aging, and modeling errors. Such uncertainty can degrade nominal vibration suppression and, more importantly, trigger violations of hard safety limits such as travel, deflection, and s...
Wen-Shuo Hu, Shao-Hua Wu, Yu-Chong Guan et al.· Journal of Vibration and Con...· 0 citations
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