arXiv:2606.09047v1 Announce Type: cross Abstract: A classical universal stabilization formula offers the practitioner no design freedom: it is a single, parameter-free object. We introduce a cost-parametrized family of stabilizing feedback laws, where (1) the user chooses a function that serves as the running cost on control in an inverse-optimal cost functional, and (2) obtains, through a formula, a nonlinear "expander" of a pre-existing universal controller, which solves an infinite-horizon optimal control problem with a meaningful cost on the state. The cost-to-expander formula is a three-s
This paper introduces a novel approach to universal stabilization, providing more flexible and controllable feedback mechanisms for complex systems, building on decades of control theory research.
It allows for the design of systems that are not only universally stable but also optimized for specific control costs, opening new possibilities for robust and efficient AI and robotic applications.
Control systems can now be designed with a family of stabilizing feedback laws, allowing practitioners to fine-tune performance based on economic or resource constraints rather than a single fixed solution.
- · AI agents developers
- · Robotics engineers
- · Autonomous systems manufacturers
- · Industrial automation sector
- · Developers relying on rigid control solutions
- · Systems with high, unoptimized control power consumption
More robust and efficient autonomous systems can be developed due to customizable stabilization.
This could accelerate the adoption of complex AI agents and robotics in industrial and civilian applications due to enhanced reliability and cost efficiency.
The ability to 'cost-parametrize' stability might lead to a new generation of energy-efficient AI hardware and software designed around inverse-optimal control principles.
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