arXiv:2506.01226v3 Announce Type: replace-cross Abstract: We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Youla-Kucera parameterization combined with robust neural networks such as the recurrent equilibrium network (REN). The resulting parameterizations are unconstrained, and hence can be searched over with first-order optimization methods, while always ensuring closed-loop stability by construction. We study the combination of (a) nonlinear dynamics, (b) partial observation, and (c) incremental
The increased sophistication of neural network architectures and demand for reliable AI systems in critical applications drives research into stable-by-design control methods.
Ensuring the inherent stability of AI-driven control systems is crucial for their deployment in complex real-world environments, accelerating their adoption and reducing risks.
This research provides a framework for developing AI controllers that are guaranteed stable by construction, simplifying deployment in safety-critical systems.
- · AI-driven automation companies
- · Robotics manufacturers
- · Critical infrastructure operators
- · Aerospace and defence
- · Companies relying on unstable or unpredictable AI control systems
Increased reliability and broader adoption of AI for complex control tasks across industries.
Reduced development and certification costs for AI-enabled autonomous systems due to inherent stability guarantees.
Accelerated shift towards fully autonomous operations in sectors like manufacturing, logistics, and transportation, altering labor market demands.
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Read at arXiv cs.LG