Reference
K. He, S. Shi, T. van den Boom, and B. De Schutter, "Efficient and safe
learning-based control of piecewise affine systems using optimization-free
safety filters,"
Proceedings of the 63rd IEEE Conference on
Decision and Control, Milan, Italy, pp. 5046-5053, Dec. 2024.
Abstract
Control of piecewise affine (PWA) systems under complex constraints faces
challenges in guaranteeing both safety and online computational efficiency.
Learning-based methods can rapidly generate control signals with good
performance, but rarely provide safety guarantees. A safety filter is a modular
method to improve safety for any controller. When applied to PWA systems, a
traditional safety filter usually need to solve a mixed-integer convex program,
which reduces the computational benefit of learning-based controllers. We
propose a novel optimization-free safety filter designed to handle state
constraints that involve a combination of polyhedra and ellipsoids. The
proposed safety filter only utilizes algebraic and min-max operations to
determine safe control inputs. This offers a notable advantage compared with
traditional safety filters by allowing for significantly more efficient
computation of control signals. The proposed safety filter can be integrated
into various function approximators, such as neural networks, enabling safe
learning throughout the learning process. Simulation results on a bicycle model
with PWA approximation validate the proposed method regarding constraint
satisfaction, CPU time, and the preservation of sub-optimality.
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BibTeX
@inproceedings{HeShi:24-023,
author = {He, Kanghui and Shi, Shengling and van den Boom, Ton and De
Schutter, Bart},
title = {Efficient and Safe Learning-Based Control of Piecewise Affine
Systems Using Optimization-Free Safety Filters},
booktitle = {Proceedings of the 63rd IEEE Conference on Decision and
Control},
address = {Milan, Italy},
pages = {5046--5053},
month = dec,
year = {2024}
}