Search Results for author:
Found 12 papers, 1 papers with code
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system.
General SIS diffusion process with indirect spreading pathways on a hypergraph
While conventional graphs only characterize pairwise interactions, higher-order networks (hypergraph, simplicial complex) capture multi-body interactions, which is a potentially more suitable modeling framework for a complex real system.
Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR).
A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning
A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied.
Simultaneous Recursive Identification of Parameters and Switching Manifolds Identification of Discrete-Time Switched Linear Systems
Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method.
Discrete-time Layered-network Epidemics Model with Time-varying Transition Rates and Multiple Resources
This paper studies a discrete-time time-varying multi-layer networked SIWS (susceptible-infected-water-susceptible) model with multiple resources under both single-virus and competing multi-virus settings.
Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM
These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero.
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control
The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots.
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy.
Instantaneous Local Control Barrier Function: An Online Learning Approach for Collision Avoidance
This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments.
Model-Free Incremental Adaptive Dynamic Programming Based Approximate Robust Optimal Regulation
This paper presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems.
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control
By introducing pseudo controls and risk-sensitive input and state penalty terms, the constrained robust stabilization problem of the original system is converted into an equivalent optimal control problem of an auxiliary system.
