Search Results for author:
Found 24 papers, 4 papers with code
Intention-Aware Control Based on Belief-Space Specifications and Stochastic Expansion
This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions.
A Comparative Study of Artificial Potential Fields and Safety Filters
In this paper, we have demonstrated that the controllers designed by a classical motion planning tool, namely artificial potential fields (APFs), can be derived from a recently prevalent approach: control barrier function quadratic program (CBF-QP) safety filters.
A Tunable Universal Formula for Safety-Critical Control
Finally, we demonstrate the efficacy of our method through a collision avoidance example, investigating the essential properties including safety, robustness, and smoothness under various tunable scaling terms.
Unifying Controller Design for Stabilizing Nonlinear Systems with Norm-Bounded Control Inputs
This paper revisits a classical challenge in the design of stabilizing controllers for nonlinear systems with a norm-bounded input constraint.
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks
Furthermore, miscellaneous resilient coordination problems are discussed in this survey.
Data-driven Dynamic Event-triggered Control
This paper revisits the event-triggered control problem from a data-driven perspective, where unknown continuous-time linear systems subject to disturbances are taken into account.
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning
In our previous research, we developed an analytical control strategy, namely the universal formula, that incorporates CLF and CBF conditions for safe stabilization.
A Novel Dynamic Event-triggered Mechanism for Dynamic Average Consensus
This paper studies a challenging issue introduced in a recent survey, namely designing a distributed event-based scheme to solve the dynamic average consensus (DAC) problem.
A Behavioral Perspective on Models of Linear Dynamical Networks with Manifest Variables
This work makes an initial step towards addressing the above issues by taking a behavioral perspective, where input and output channels are not pre-determined.
CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention
Inspired by the recent anchor free CNN-based circular object detection method (CircleNet) for ball-shape glomeruli detection in renal pathology, in this paper, we present CircleFormer, a Transformer-based circular medical object detection with dynamic anchor circles.
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment.
Decentralised adaptive-gain control for eliminating epidemic spreading on networks
We first identify a necessary and sufficient condition for the existence of a subset of nodes, which if controlled would result in the elimination of the disease.
A port-Hamiltonian framework for displacement-based and rigid formation tracking
Regarding the external feedback, a general framework is proposed for all kinds of formations by means of the advantage that the pH model is energy-based and coordinate-free.
Moving Obstacle Collision Avoidance via Chance-Constrained MPC with CBF
Model predictive control (MPC) with control barrier functions (CBF) is a promising solution to address the moving obstacle collision avoidance (MOCA) problem.
Automated Formation Control Synthesis from Temporal Logic Specifications
In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks.
Characterizing bearing equivalence in directed graphs
These conditions involve the spectrum and null space of the associated bearing Laplacian matrix for a directed bearing formation.
Modelling and Kron reduction of power flow networks in directed graphs
Electrical grids are large-sized complex systems that require strong computing power for monitoring and analysis.
A gallery of diagonal stability conditions with structured matrices (and review papers)
This note presents a summary and review of various conditions and characterizations for matrix stability (in particular diagonal matrix stability) and matrix stabilizability.
Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards
To solve these tasks efficiently, we propose a novel self-guided continual RL framework, RelayHER (RHER).
Cooperative constrained motion coordination of networked heterogeneous vehicles
We develop a general framework involving differential-algebraic equations and viability theory to determine coordination feasibility for a coordinated motion control under heterogeneous vehicle dynamics and different types of coordination task constraints.
Quadrotor Control on $SU(2)\times R^3$ with SLAM Integration
We present a trajectory tracking controller for a quadrotor unmanned aerial vehicle (UAV) configured on $SU(2)\times R^3$, and relate this result to a family of geometric tracking controllers on $SO(3)\times R^3$.
Distributed adaptive stabilization
In this paper we consider distributed adaptive stabilization for uncertain multivariable linear systems with a time-varying diagonal matrix gain.
Optimal control of nonlinear systems with unsymmetrical input constraints and its applications to the UAV circumnavigation problem
In this paper, a novel design scheme is introduced to solve the optimal control problem for nonlinear systems with unsymmetrical and state-dependent input constraints.
Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty
The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, and it also has a superior tolerance of system uncertainty.
