1 code implementation • 25 Apr 2024 • Yihan Zhou, Yiwen Lu, Zishuo Li, Jiaqi Yan, Yilin Mo
However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application due to high computational costs.
1 code implementation • 8 Dec 2023 • Yiwen Lu, Zishuo Li, Yihan Zhou, Na Li, Yilin Mo
In this paper, we introduce a new class of parameterized controllers, drawing inspiration from Model Predictive Control (MPC).
no code implementations • 18 Oct 2023 • Shuai Sun, Jiayun Li, Yilin Mo
This paper is concerned with the finite time identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs.
1 code implementation • 29 Sep 2023 • Jiayun Li, Yuxiao Cheng, Yiwen Lu, Zhuofan Xia, Yilin Mo, Gao Huang
Activation functions are essential to introduce nonlinearity into neural networks, with the Rectified Linear Unit (ReLU) often favored for its simplicity and effectiveness.
no code implementations • 26 Jun 2023 • Huiwen Yang, Lingying Huang, Chao Yang, Yilin Mo, Ling Shi
By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution.
no code implementations • 30 Mar 2023 • Zishuo Li, Anh Tung Nguyen, André Teixeira, Yilin Mo, Karl H. Johansson
To deal with such attacks, we propose the design of local estimators based on observability space decomposition, where each local estimator updates the local state and sends it to the fusion center after sampling a measurement.
no code implementations • 13 Jan 2023 • Yiwen Lu, Yilin Mo
The Linear-Quadratic Regulation (LQR) problem with unknown system parameters has been widely studied, but it has remained unclear whether $\tilde{ \mathcal{O}}(\sqrt{T})$ regret, which is the best known dependence on time, can be achieved almost surely.
no code implementations • 8 Dec 2022 • Yiwen Lu, Yilin Mo
Switching control strategies that unite a potentially high-performance but uncertified controller and a stabilizing albeit conservative controller are shown to be able to balance safety with efficiency, but have been less studied under partial observation of state.
no code implementations • 10 Nov 2022 • Zishuo Li, Muhammad Umar B. Niazi, Changxin Liu, Yilin Mo, Karl H. Johansson
At each time step, the local estimates of sensors are fused by solving an optimization problem to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers.
no code implementations • 26 Oct 2022 • Yiwen Lu, Yilin Mo
We show that the switching strategy is both safe and efficient, in the sense that: 1) the linear-quadratic cost of the system is always bounded even if original uncertified controller is destabilizing; 2) in case the uncertified controller is stabilizing, the performance loss caused by switching converges super-exponentially to $0$ for Gaussian noise, while the converging polynomially for general heavy-tailed noise.
no code implementations • 18 May 2022 • Yiwen Lu, Yilin Mo
Sustained research efforts have been devoted to learning optimal controllers for linear stochastic dynamical systems with unknown parameters, but due to the corruption of noise, learned controllers are usually uncertified in the sense that they may destabilize the system.
no code implementations • 13 May 2022 • Jiaqi Yan, Yilin Mo, Hideaki Ishii
We propose an event-based control protocol for achieving the synchronization among agents in the mean square sense and theoretically analyze the performance of it by using a stochastic Lyapunov function, where the stability of $c$-martingales is particularly developed to handle the challenges brought by the general model of noises and the event-triggering mechanism.
no code implementations • 7 Apr 2022 • Jiaqi Yan, Yilin Mo, Hideaki Ishii
This paper considers the problem of distributed estimation in a sensor network, where multiple sensors are deployed to infer the state of a linear time-invariant (LTI) Gaussian system.
no code implementations • 6 Jun 2021 • Zishuo Li, Yilin Mo
We consider the problem of estimating the state of a time-invariant linear Gaussian system in the presence of integrity attacks.
no code implementations • 13 May 2021 • Zishuo Li, Yilin Mo
We consider the problem of estimating the state of a linear Gaussian system in the presence of integrity attacks.
no code implementations • 6 May 2021 • Shuai Sun, Yilin Mo
An upper bound of false alarm rate in the absence of malicious attackers and the necessary and sufficient condition for there is no undetectable input by the attack detector in the system are given.
no code implementations • 24 Mar 2021 • Yiwen Lu, Yilin Mo
This paper considers the linear-quadratic dual control problem where the system parameters need to be identified and the control objective needs to be optimized in the meantime.
no code implementations • 26 Jan 2021 • Jiaqi Yan, Xu Yang, Yilin Mo, Keyou You
This paper studies the distributed state estimation in sensor network, where $m$ sensors are deployed to infer the $n$-dimensional state of a linear time-invariant (LTI) Gaussian system.
no code implementations • 3 Jan 2020 • Jiaqi Yan, Xiuxian Li, Yilin Mo, Changyun Wen
To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an "auxiliary point" based on the received values and moves its state toward this point.