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Found 15 papers, 0 papers with code
Application of Log-Linear Dynamic Inversion Control to a Multi-rotor
This paper presents an approach that employs log-linearization in Lie group theory and the Newton-Euler equations to derive exact linear error dynamics for a multi-rotor model, and applies this model with a novel log-linear dynamic inversion controller to simplify the nonlinear distortion and enhance the robustness of the log-linearized system.
Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications
By pre-training the MA-BERT on a large dataset from a major airport and then fine-tuning it to other airports and specific air traffic applications, a large amount of the total training time can be saved.
Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems
The conventional solutions for fault-detection, identification, and reconstruction (FDIR) require centralized decision-making mechanisms which are typically combinatorial in their nature, necessitating the design of an efficient distributed FDIR mechanism that is suitable for multi-agent applications.
Distributed Gaussian Mixture PHD Filtering under Communication Constraints
In this paper, we develop a distributed GM-PHD filtering recursion that uses a probabilistic communication rule to limit the communication bandwidth of the algorithm, while ensuring asymptotic optimality of the algorithm.
Exploiting Sparsity for Localization of Large-Scale Wireless Sensor Networks
Wireless Sensor Network (WSN) localization refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information.
Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements
A practical challenge which arises in the operation of sensor networks is the presence of sensor faults, biases, or adversarial attacks, which can lead to significant errors incurring in the localization of the agents, thereby undermining the security and performance of the network.
On Attack Detection and Identification for the Cyber-Physical System using Lifted System Model
Motivated by the safety and security issues related to cyber-physical systems with potentially multi-rate, delayed, and nonuniformly sampled measurements, we investigate the attack detection and identification using the lifted system model in this paper.
Data-driven Cyberattack Synthesis against Network Control Systems
We utilize the equivalent model to synthesize hybrid cyberattacks -- a combination of FDI and DoS attacks against the NCS.
Log-linear Dynamic Inversion Control with Provable Safety Guarantees in Lie Groups
In this paper, we use the derivative of the exponential map to derive the exact evolution of the logarithm of the tracking error for mixed-invariant systems, a class of systems capable of describing rigid body tracking problems in Lie groups.
Approximating Reachable Sets for Neural Network based Models in Real-Time via Optimal Control
We utilize a running example of a quadrotor model that is learned using trajectory data via NNs.
Provably Stabilizing Model-Free Q-Learning for Unknown Bilinear Systems
In this paper, we present a provably convergent Model-Free ${Q}$-Learning algorithm that learns a stabilizing control policy for an unknown Bilinear System from a single online run.
State Prediction of Human-in-the-Loop Multi-rotor System with Stochastic Human Behavior Model
To reduce the conservativeness of the reachability analysis, we present a state prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM).
Safety Verification of Model Based Reinforcement Learning Controllers
The proposed frame-work can efficiently handle models and controllers which are represented using neural networks.
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine
The proposed algorithm is developed for the discrete state and action space and utilizes a multi-class support vector machine (SVM) to represent the policy.
SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders
Our intuition is that, the more similarity exists between the unknown data samples and the part of known data that an autoencoder was trained with, the better chances there could be that this autoencoder makes use of its trained knowledge, reconstructing output samples closer to the originals.
