arXiv subjects

Yuhu Wu

Publications and source records attributed to Yuhu Wu.

10 recordsLinked to original sources

arXiv2026

The distance-based formation controller design for multi-agent systems in port-Hamiltonian form

Based on the practical scenario where collisions in formation control may lead to agent damage, this paper investigates the integrated problem of distance-based formation control and collision avoidance for multi-agent systems governed by port-Hamiltonian dynamics. A foundational step involves constructing a signed incidence matrix, which, by design, corresponds to a directed acyclic graph and possesses the full column rank property. To overcome the prevalent issue of local minima in traditional artificial potential fields, a novel design utilizing attraction-only potentials is introduced, with collision avoidance rigorously enforced by safety barriers. This framework leads to a unified controller that concurrently manages velocity tracking, target formation acquisition, and inter-agent safety. The stability of the resulting closed-loop system is guaranteed through LaSalle's invariance principle. Numerical simulations demonstrate the validity and effectiveness of the proposed control strategy.

math.OC↗

arXiv2026

Boundary Stabilization for the Rayleigh Beam System under Event-triggered Controls

In this paper, we propose two event-triggered control laws incorporating an eventtriggering mechanism to tackle the boundary stabilization for the Rayleigh beam system. Under this event-triggered controls, a sufficient condition for parameter determination is constructed to guarantee the exponential stability of the closed-loop system by using the integral multiplier technique and energy perturbation method, wherein the desired exponential decay rate can be precisely determined. Numerical examples are presented to demonstrate the efficacy of the event-triggered control methodology.

math.OC↗

arXiv2026

Privacy-Preserving Formation Control for Networked Underactuated USVs: A Passivity-Based Approach

This paper studies coordinated trajectory planning and tracking control for multiple unmanned surface vessels (USVs) under strict privacy requirements. To avoid the privacy risks associated with direct position sharing in conventional cooperative methods, the proposed approach adopts an estimated fleet centroid as the only shared variable, preventing individual trajectory disclosure while enabling coordination. Based on this interaction mechanism, a formation-oriented trajectory is generated for the fleet. The collective dynamics are modeled using Port-Hamiltonian systems, and a passivity-based tracking controller is designed for each USV to accurately follow the planned trajectories. The stability of the closed-loop system is rigorously proven, and experiments on a real USV platform confirm effective formation tracking and privacy preservation. The proposed result extends and validates through experimental results the approach in [26] that was limited to idealized pointmass models and lacked a feedback control.

math.OC↗

arXiv2026

Seeking Nash Equilibrium in Non-cooperative Quadratic Games Under Delayed Information Exchange

In this paper, we investigate the seeking of Nash equilibrium (NE) in a non-cooperative quadratic game where all agents exchange their delayed strategy information with their neighbors. To extend best-response algorithms to the delayed information setting, an estimation mechanism for each agent to estimate the current strategy profile is designed. Based on the best-response strategy to the estimations, the strategy profile dynamics of all agents is established, which is revealed to converge asymptotically to the NE when agents exchange multi-step-delay information via the Lyapunov-Krasovskii functional approach. In the scenario where agents exchange one-step-delay information, the exponential convergence of the strategy profile dynamics to the NE can be guaranteed by restricting the learning rate to less than an upper bound. Moreover, a lower bound on the learning rate for instability of the NE is proposed. Numerical simulations are provided for verifying the developed results.

eess.SY↗

arXiv2026

Modeling and Control for UAV with Off-center Slung Load

Unmanned aerial vehicle (UAV) with slung load system is a classic air transportation system. In practical applications, the suspension point of the slung load does not always align with the center of mass (CoM) of the UAV due to mission requirements or mechanical interference. This offset creates coupling in the system's nonlinear dynamics which leads to a complicated motion control problem. In existing research, modeling of the system are performed about the UAV's CoM. In this work we use the point of suspension instead. Based on the new model, a cascade control strategy is developed. In the middle-loop controller, the acceleration of the suspension point is used to regulate the swing angle of the slung load without the need for considering the coupling between the slung load and the UAV. An inner-loop controller is designed to track the UAV's attitude without the need of simplification on the coupling effects. We prove local exponential stability of the closed-loop using Lyapunov approach. Finally, simulations and experiments are conducted to validate the proposed control system.

eess.SY↗

arXiv2025

Robust Mean Field Social Control: A Unified Reinforcement Learning Framework

This paper studies linear quadratic Gaussian robust mean field social control problems in the presence of multiplicative noise. We aim to compute asymptotic decentralized strategies without requiring full prior knowledge of agents' dynamics. The primary challenges lie in solving an indefinite stochastic algebraic Riccati equation for feedback gains, and an indefinite algebraic Riccati equation for feedforward gains. To overcome these challenges, we first propose a unified dual-loop iterative framework that handles both indefinite Riccati-type equations, and provide rigorous convergence proofs for both the outer-loop and inner-loop iterations. Secondly, considering the potential biases arising in the iterative processes due to estimation and modeling errors, we verify the robustness of the proposed algorithm using the small-disturbance input-to-state stability technique. Convergence to a neighborhood of the optimal solution is thus ensured, even in the existence of disturbances. Finally, to relax the limitation of requiring precise knowledge of agents' dynamics, we employ the integral reinforcement learning technique to develop a data-driven method within the dual-loop iterative framework. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.

eess.SY↗

arXiv2025

Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties

Validating and controlling safety-critical systems in uncertain environments necessitates probabilistic reachable sets of future state evolutions. The existing methods of computing probabilistic reachable sets normally assume that stochastic uncertainties are independent of system states, inputs, and other environment variables. However, this assumption falls short in many real-world applications, where the probability distribution governing uncertainties depends on these variables, referred to as contextual uncertainties. This paper addresses the challenge of computing probabilistic reachable sets of stochastic nonlinear states with contextual uncertainties by seeking minimum-volume polynomial sublevel sets with contextual chance constraints. The formulated problem cannot be solved by the existing sample-based approximation method since the existing methods do not consider conditional probability densities. To address this, we propose a consistent sample approximation of the original problem by leveraging conditional density estimation and resampling. The obtained approximate problem is a tractable optimization problem. Additionally, we prove the proposed sample-based approximation's almost uniform convergence, showing that it gives the optimal solution almost consistently with the original ones. Through a numerical example, we evaluate the effectiveness of the proposed method against existing approaches, highlighting its capability to significantly reduce the bias inherent in sample-based approximation without considering a conditional probability density.

eess.SY↗

arXiv2023

Chance-constrained probability measure optimization

Stochastic optimization with chance constraints often relies on deterministic decision-making, where optimal decisions are fixed and may be applied to a system repeatedly. A critical question arises: Can probabilistic decision-making, where probability measures/distributions are considered as decision variables, outperform deterministic decision-making in terms of the expected performance when chance constraints are present? This paper addresses this question by introducing the \textit{Chance-Constrained Probability Measure Optimization} (CCPMO) framework, which formulates the problem of optimizing probabilistic decisions under chance constraints. We first establish the existence of the optimal solution to CCPMO. Crucially, we prove that the optimal probabilistic decisions can always be represented by a probability measure concentrated on only two points, thereby reducing the CCPMO problem to an equivalent, simplified form. To solve this reduced problem, we propose a sample-based smooth approximation method. This approach leverages samples of model uncertainties to construct an approximate problem with uniform convergence and probabilistic feasibility guarantees. The approximate problem can be efficiently solved using standard nonlinear programming techniques. Finally, we validate the proposed framework through a numerical example of a quadrotor control problem under turbulent conditions. The results demonstrate that probabilistic decision-making outperforms deterministic approaches in expected performance while satisfying safety-critical chance constraints.

math.OC↗

arXiv2022

Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks

The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e.g., with or without control inputs; attractor state or a subset of the state space as the target domain). The method is agnostic to the distribution of probabilities for the next state, hence it does not use the probability transition matrix. The time complexity is linear on the time steps, or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore the scalability of the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with 200 nodes.

cs.LG↗

arXiv2021

A Comprehensive Survey on STP Approach to Finite Games

Nowadays the semi-tensor product (STP) approach to finite games has become a promising new direction. This paper provides a comprehensive survey on this prosperous field. After a brief introduction for STP and finite (networked) games, a description for the principle and fundamental technique of STP approach to finite games is presented. Then several problems and recent results about theory and applications of finite games via STP are presented. A brief comment about the potential use of STP to artificial intelligence is also proposed.

cs.GT↗