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Tielong Shen

Publications and source records attributed to Tielong Shen.

6 recordsLinked to original sources

On Dimension-Varying Control Systems: A Universal State Space Approach

This paper develops a unified framework for the analysis and design of dimension-varying control systems by constructing an intrinsic quotient state space, $Ω$. A significant challenge in non-fixed-dimensional systems is the lack of a common metric space that enables comparison of states across dimensions without relying on arbitrary external embeddings. To address this, we propose a cross-dimensional pseudo-metric $d_{\mathcal{V}}$ on $\mathbb{R}^{\infty}$ and derive $Ω$ by identifying zero-distance representatives. We demonstrate that $Ω$ preserves the essential topological and metric geometry of Euclidean space, providing the necessary foundation to extend fundamental control notions to the dimension-varying case. Specifically, we establish conditions for controllability, observability and stabilizability, and we address the complexities of Lipschitz switching and disturbance decoupling within this common space. The framework is further extended to hierarchical dimension-varying networks. The practical utility of the results is illustrated through a generator-removal-and-reconnection scenario in a three-machine power system. This case study demonstrates the use of translated representatives and projection/lift benchmarks, quantifies event-wise $d_{\mathcal{V}}$-gaps, and provides a finite-schedule dwell-time consistency check to validate the system's structural transitions.

math.OC

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

Data-Driven Mean Field Equilibrium Computation in Large-Population LQG Games

This paper presents a novel data-driven approach for approximating the $\varepsilon$-Nash equilibrium in continuous-time linear quadratic Gaussian (LQG) games, where multiple agents interact with each other through their dynamics and infinite horizon discounted costs. The core of our method involves solving two algebraic Riccati equations (AREs) and an ordinary differential equation (ODE) using state and input samples collected from agents, eliminating the need for a priori knowledge of their dynamical models. The standard ARE is addressed through an integral reinforcement learning (IRL) technique, while the nonsymmetric ARE and the ODE are resolved by identifying the drift coefficients of the agents' dynamics under general conditions. Moreover, by imposing specific conditions on models, we extend the IRL-based approach to approximately solve the nonsymmetric ARE. Numerical examples are given to demonstrate the effectiveness of the proposed algorithms.

eess.SY

Mean Field LQG Social Optimization: A Reinforcement Learning Approach

This paper presents a novel model-free method to solve linear quadratic Gaussian mean field social control problems in the presence of multiplicative noise. The objective is to achieve a social optimum by solving two algebraic Riccati equations (AREs) and determining a mean field (MF) state, both without requiring prior knowledge of individual system dynamics for all agents. In the proposed approach, we first employ integral reinforcement learning techniques to develop two model-free iterative equations that converge to solutions for the stochastic ARE and the induced indefinite ARE respectively. Then, the MF state is approximated, either through the Monte Carlo method with the obtained gain matrices or through the system identification with the measured data. Notably, a unified state and input samples collected from a single agent are used in both iterations and identification procedure, making the method more computationally efficient and scalable. Finally, a numerical example is given to demonstrate the effectiveness of the proposed algorithm.

math.OC

Autonomous UAV Landing System Based on Visual Navigation

In this paper, we present an autonomous unmanned aerial vehicle (UAV) landing system based on visual navigation. We design the landmark as a topological pattern in order to enable the UAV to distinguish the landmark from the environment easily. In addition, a dynamic thresholding method is developed for image binarization to improve detection efficiency. The relative distance in the horizontal plane is calculated according to effective image information, and the relative height is obtained using a linear interpolation method. The landing experiments are performed on a static and a moving platform, respectively. The experimental results illustrate that our proposed landing system performs robustly and accurately.

cs.RO

Equivalence-Based Model of Dimension-Varying Linear Systems

Dimension-varying linear systems are investigated. First, a dimension-free state space is proposed. A cross dimensional distance is constructed to glue vectors of different dimensions together to form a cross-dimensional topological space. This distance leads to projections over different dimensional Euclidean spaces and the corresponding linear systems on them, which provide a connection among linear systems with different dimensions. Based on these projections, an equivalence of vectors and an equivalence of matrices over different dimensions are proposed. It follows that the dynamics on quotient space is obtained, which provides a proper model for cross-dimensional systems. Finally, using lifts of dynamic systems on quotient space to Euclidean spaces of different dimensions, a cross-dimensional model is proposed to deal with the dynamics of dimension-varying process of linear systems. On the cross-dimensional model a control is designed to realize the transfer between models on Euclidean spaces of different dimensions.

math.DS