11 Papers
4 Citations
Yu Li is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Nonlinear system. The author has an hindex of 1, co-authored 1 publications.
Chat about Author
Papers
A Gaussian-multivariate Laplacian mixture distribution based robust cubature Kalman filter
TL;DR: In this article , a new Gaussian-multivariate Laplacian mixture (GMLM) distribution is proposed to model the non-Gaussian noises caused by the randomly occurring measurement outliers and a robust cubature Kalman filter is derived.
10
A cascaded nonlinear fault-tolerant control for fixed-wing aircraft with wing asymmetric damage.
TL;DR: In this article , a robust cascaded nonlinear fault-tolerant control framework that integrates the incremental nonlinear dynamic inversion control with improved piecewise-constant-based nonlinear L1 adaptive control for the stability control was proposed to enhance the stability and tracking performance of the damaged aircraft.
7
Morphing aircraft acceleration and deceleration task morphing strategy using a reinforcement learning method
Ruichen Ming,Xiaoxiong Liu,Yu Li,Yi Yin,Weiguo Zhang +4 more
- 26 Aug 2023
TL;DR: A novel morphing aircraft is designed, and its nonlinear dynamic equations are established based on the calculated aerodynamic data, and a soft actor critic (SAC) approach is utilized to design the scheme, whose structure consists of the environment, the agent, and the reward function.
4
Gust alleviation controller for elastic aircraft based on L1 adaptive control
Liu Xiao-xiong,Yu Li,Ma Qing Yuan,Shen Jian +3 more
- 01 Oct 2017
TL;DR: A designed method of gust alleviation controller based on L1 adaptive output feedback that can reduce the influence of gust in the longitudinal of the aircraft is presented.
4
DRNAS: Differentiable RBF neural architecture search method considering computation load in adaptive control
Ruichen Ming,Xiaoxiong Liu,Yu Li,Wei Huang,Weiguo Zhang +4 more
TL;DR: This paper proposes DRNAS, a differentiable RBF neural architecture search method that balances control performance and computation load in adaptive control systems, using a hypernetwork and backstepping method to optimize neural network architecture and parameters.
3