系统与控制
We develop a learning-based framework for constructing shrinking disturbance-invariant tubes under state- and input-dependent uncertainty, intended as a building block for tube Model Predictive Control (MPC), and certify safety via a…
The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges,…
Multi-hop collaboration offers new perspectives for enhancing task execution efficiency by increasing available distributed collaborators for resource sharing. Consequently, selecting trustworthy collaborators becomes critical for realizing…
This study investigates the application of modern control theory to improve the precision of spacecraft orbit maneuvers in low Earth orbit under the influence of solar radiation pressure. A full order observer based feedback control…
In many scenarios, it is natural to model a plant's dynamical behavior using a hybrid dynamical system influenced by exogenous continuous-time inputs. While solution concepts and analytical tools for existence and completeness are well…
Thermal energy storage (TES) systems coupled with heat pumps offer significant potential for improving building energy efficiency by shifting electricity demand to off-peak hours. However, conventional operating strategies maintain…
Soft, stretchable organic field-effect transistors (OFETs) can provide powerful on-skin signal conditioning, but current fabrication methods are often material-specific: each new polymer semiconductor (PSC) requires a tailored process. The…
The rapid growth of radio access networks (RANs) is increasing energy consumption and challenging the sustainability of future systems. We consider a dense-urban vertical heterogeneous network (vHetNet) comprising a high-altitude platform…
This paper develops a generalized finite horizon recursive solution to the discrete time stage bound disturbance attenuation regulator (StDAR) for state feedback control. This problem addresses linear dynamical systems subject to stage…
The transition to electric vehicles (EVs) depends heavily on the reliability of charging infrastructure, yet approximately 1 in 5 drivers report being unable to charge during station visits due to inoperable equipment. While regulatory…
We study state estimation for discrete-time linear stochastic systems under distributional ambiguity in the initial state, process noise, and measurement noise. We propose a noise-centric distributionally robust Kalman filter (DRKF) based…
We propose a novel symbolic control framework for enforcing temporal logic specifications in Euler-Lagrange systems that addresses the key limitations of traditional abstraction-based approaches. Unlike existing methods that require exact…
Traditional reinforcement learning lacks the ability to provide stability guarantees. More recent algorithms learn Lyapunov functions alongside the control policies to ensure stable learning. However, the current self-learned Lyapunov…
This paper proposes a novel approach to design analog electronic circuits that implement Model Predictive Control (MPC) policies for dynamical systems described by affine models. Effective approaches to define a reduced-complexity Explicit…
The increasing integration of renewable energy sources has introduced complex dynamic behavior in power systems that challenge the adequacy of traditional continuous-time modeling approaches. These developments call for modeling frameworks…
Optimal sensor placement is essential for state estimation and effective network monitoring. As known in the literature, this problem becomes particularly challenging in large-scale undirected or bidirected cyclic networks with parametric…
Sustainability is paramount in modern cellular networks, which face significant energy consumption challenges from rising mobile traffic and advancements in wireless technology. Cell-switching, well-established in literature as an effective…
This study introduces and addresses the critical challenge of traffic load estimation in cell switching within vertical heterogeneous networks. The effectiveness of cell switching is significantly limited by the lack of accurate traffic…
We develop delay-compensating feedback laws for linear switched systems with time-dependent switching. Because the future values of the switching signal, which are needed for constructing an exact predictor-feedback law, may be unavailable…
Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems…