arXiv:2501.15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to guarantee both safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performance with a safety guarantee by solving reinforcement learning (RL)-based optimal control problems for nonlinear systems subject to high-relative-degree state constraints and unknown time-varying disturbance/actuator faults. A new type of contr
The increasing complexity and safety-critical nature of AI systems necessitate advancements that guarantee both performance and safety, a core challenge in current AI development.
This research addresses a critical limitation in AI-driven control systems, enabling their deployment in sensitive applications where errors are unacceptable and performance degradation is undesirable.
The ability to formally guarantee safety alongside optimal performance in nonlinear systems under uncertainty fundamentally alters the risk calculus for deploying advanced AI in real-world control scenarios.
- · Autonomous systems developers
- · Robotics industry
- · Industrial control systems
- · AI safety researchers
- · Companies relying on ad-hoc safety mitigation strategies
- · Systems with high performance but unquantified safety risks
More robust and reliable AI-driven systems can be developed and deployed across various industries.
Reduced regulatory hurdles and increased public trust in autonomous systems will accelerate their adoption.
The definition of 'autonomous' will expand to include guaranteed safety, leading to a new class of AI applications with far-reaching societal and economic impacts.
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