Open-loop control design for contraction in affine nonlinear systems

2025-03-28 · Mohamed Yassine Arkhis, Denis Efimov

In this paper, first, it is shown that if a nonlinear time-varying system is contractive, then it is incrementally exponentially stable. Second, leveraging this result, under mild restrictions, an approach is proposed to design feedforward inputs for affine in control systems providing contraction/incremental exponential stability. Unlike standard stability notions, which have well-established control design techniques, this note can be considered among the first ones to provide such a tool for a kind of incremental stability. The theoretical findings are illustrated by examples.

📄 PDF Abstract BibTeX arXiv:2503.22210

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Neural Exponential Stabilization of Control-affine Nonlinear Systems

2024-03-26 · Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contra…

AutoLALA: Automatic Loop Algebraic Locality Analysis for AI and HPC Kernels

2026-04-06 · Yifan Zhu, Yekai Pan, Yanghui Wu, Chen Ding arxiv

Data movement is the primary bottleneck in modern computing systems. For loop-based programs common in high-performance computing (HPC) and AI workloads, including matrix multiplication, tensor contraction, stencil compu…

Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory

2022-06-14 · Glen Chou, Necmiye Ozay, Dmitry Berenson

We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e.g., RGB-D images) …

Motion Planning State Estimation valid

Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning

2025-12-01 · Alexander Davydov arxiv

We address the problem of verifying closed-loop contraction in nonlinear control systems whose controller and contraction metric are both parameterized by neural networks. By leveraging interval analysis and interval bou…

Online Algorithms and Policies Using Adaptive and Machine Learning Approaches

2021-05-13 · Anuradha M. Annaswamy, Anubhav Guha, Yingnan Cui, Sunbochen Tang 외

This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. We propose a combination of a Reinforcement Learning (RL) based policy in the outer loop suitab…

BIG-bench Machine Learning Reinforcement Learning (RL)