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Found 17 papers, 2 papers with code
A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance
We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies.
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System
Specifically, the first-order average consensus algorithm is utilized to expand the observations of the DESS state in a fully-decentralized way, and the initial actions (i. e., output power) are decided by the agents (i. e., energy storage units) according to these observations.
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties
This paper is concerned with the robust tracking control of linear uncertain systems, whose unknown system parameters and disturbances are bounded within ellipsoidal sets.
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer.
Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation
To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem.
Resilient Output Containment Control of Heterogeneous Multiagent Systems Against Composite Attacks: A Digital Twin Approach
Third, according to the reconstructed leader dynamics, we design decentralized solvers that calculate the output regulator equations on CPL.
Resilient Output Consensus Control of Heterogeneous Multi-agent Systems against Byzantine Attacks: A Twin Layer Approach
Inspired by the concept of Digital Twin, a new hierarchical protocol equipped with a virtual twin layer (TL) is proposed, which decouples the above problems into the defense scheme against Byzantine edge attacks on the TL and the defense scheme against Byzantine node attacks on the cyber-physical layer (CPL).
Optimal Resource Allocation between Two Nonfully Cooperative Wireless Networks under Malicious Attacks: A Gestalt Game Perspective
In this paper, the problem of seeking optimal distributed resource allocation (DRA) policies on cellular networks in the presence of an unknown malicious adding-edge attacker is investigated.
Data-Driven Leader-following Consensus for Nonlinear Multi-Agent Systems against Composite Attacks: A Twins Layer Approach
Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL and the other is resilient decentralized tracking control against actuation attacks on the CPL.
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery
The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently.
Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization
This paper investigates the control barrier function (CBF) based safety-critical control for continuous nonlinear control affine systems using the more efficient online algorithms through time-varying optimization.
Novel Nussbaum-Type Function based Safe Adaptive Distributed Consensus Control with Arbitrary Unknown Control Direction
This paper develops a novel saturated Nussbaum function to relax such limitations and proposes a Nussbaum function based control scheme for the consensus problem of multi-agent systems with arbitrary non-identical unknown control directions and safe control progress.
Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation
On the one hand, it is shown that the proposed control scheme can always guarantee the safety in the identification process with noised signal injection excitation, which was not considered in the previous study.
Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation
This paper introduces this method to tracking problem for heterogeneous linear systems.
AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars
But the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy.
Resilient Path Planning of UAVs against Covert Attacks on UWB Sensors
The UAV is equipped with two sensors, a GPS sensor, which is vulnerable to the spoofing attacker, and a well-functioning Ultra-Wideband (UWB) sensor, which is possible to be fooled.
Robotics
Off-policy reinforcement learning for $ H_\infty $ control design
The $H_\infty$ control design problem is considered for nonlinear systems with unknown internal system model.
