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Found 17 papers, 1 papers with code
Observation-based Optimal Control Law Learning with LQR Reconstruction
Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention.
Inverse Reinforcement Learning with Unknown Reward Model based on Structural Risk Minimization
A simplistic model is less likely to contain the real reward function, while a model with high complexity leads to substantial computation cost and risks overfitting.
AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs).
Unidentifiability of System Dynamics: Conditions and Controller Design
In this paper, we investigate the problem of dynamic unidentifiability and design the controller to make the system dynamics unidentifiable.
Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design
More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously.
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
We show that, despite such state distribution shift, the policy gradient estimation bias can be reduced in the following three ways: 1) a small learning rate; 2) an adaptive-learning-rate-based optimizer; and 3) KL regularization.
Toward Global Sensing Quality Maximization: A Configuration Optimization Scheme for Camera Networks
Then, we form a single quantity that measures the sensing quality of the targets by the camera network.
Multi-period Optimal Control for Mobile Agents Considering State Unpredictability
This paper aims at the trade-off between the control performance and state unpredictability of mobile agents in long time horizon.
SVR-based Observer Design for Unknown Linear Systems: Complexity and Performance
The variances of the estimation error and the fluctuations in performance are smaller with a properly-designed parameter $\gamma$ compared with the OLS methods.
I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks
Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i. e., how to protect control mechanism secrets from inference attacks, including state information, interaction structure and control laws.
Local Topology Inference of Mobile Robotic Networks under Formation Control
We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots.
Safety-Aware Optimal Control for Motion Planning with Low Computing Complexity
Simulations demonstrates that BRSCA has a higher probability of finding feasible solutions, reduces the computation time by about 17. 4% and the energy cost by about four times compared to other methods in the literature.
Resilient Average Consensus: A Detection and Compensation Approach
We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound.
System Identification with Variance Minimization via Input Design
Next, an input design method is proposed to deal with the uncertainty and obtain stable identification results by minimizing the variance.
Sneak Attack against Mobile Robotic Networks under Formation Control
The security of mobile robotic networks (MRNs) has been an active research topic in recent years.
Topology Inference for Network Systems: Causality Perspective and Non-asymptotic Performance
Along with this line, we analyze the non-asymptotic inference performance of the proposed method by taking the OLS estimator as a reference, covering both asymptotically and marginally stable systems.
Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network
Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances.
