no code implementations • 13 Apr 2024 • Taoran Wu, Yiqing Yu, Bican Xia, Ji Wang, Bai Xue
Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications.
no code implementations • 27 Feb 2024 • Yonghan Li, Chenyu Wu, Taoran Wu, Shijie Wang, Bai Xue
In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances.
no code implementations • 23 Jan 2024 • Zhen Liang, Taoran Wu, Ran Zhao, Bai Xue, Ji Wang, Wenjing Yang, Shaojun Deng, Wanwei Liu
However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question.
no code implementations • 8 Oct 2023 • Taoran Wu, Dejin Ren, Shuyuan Zhang, Lei Wang, Bai Xue
Digital control has become increasingly prevalent in modern systems, making continuous-time plants controlled by discrete-time (digital) controllers ubiquitous and crucial across industries, including aerospace, automotive, and manufacturing.
1 code implementation • 5 May 2023 • Zhen Liang, Taoran Wu, Changyuan Zhao, Wanwei Liu, Bai Xue, Wenjing Yang, Ji Wang
For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors.
no code implementations • 23 Apr 2023 • Jianqiang Ding, Taoran Wu, Yuping Qian, Lijun Zhang, Bai Xue
In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set.
