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1. Dynamic Intermittent Suboptimal Control: Performance Quantification and Comparisons

   https://doi.org/10.23919/ChiCC.2018.8483352  

   Yang, Yongliang; Vamvoudakis, Kyriakos G.; Modares, Hamidreza; Ding, Dawei; Yin, Yixin; Wunsch, Donald C. (July 2018, 2018 37th Chinese Control Conference (CCC))

   This paper presents a novel intermittent suboptimal event-triggered controller design for continuous-time nonlinear systems. The stability of the equilibrium point of the closed-loop system, and the performances are analyzed and quantified theoretically. It is proven that the static and the dynamic event-triggered suboptimal controllers have a known degree of suboptimality compared to the conventional optimal control policy. In order to generate dynamic event-triggering framework, we introduce an internal dynamical system. Moreover, the Zeno behavior is excluded. Finally, a simulation example is conducted to show the effectiveness of the proposed intermittent mechanisms. 

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2. Demystify Epidemic Containment in Directed Networks: Theory and Algorithms

   https://doi.org/10.1145/3701551.3703575  

   He, Yinhan; Chen, Chen; Wang, Song; Min, Guanghui; Li, Jundong (March 2025, ACM)

   Epidemic containment has long been a crucial task in many high-stake application domains, ranging from public health to misinformation dissemination. Existing studies for epidemic containment are primarily focused on undirected networks, assuming that the infection rate is constant throughout the contact network regardless of the strength and direction of contact. However, such an assumption can be unrealistic given the asymmetric nature of the real-world infection process. To tackle the epidemic containment problem in directed networks, simply grafting the methods designed for undirected network can be problematic, as most of the existing methods rely on the orthogonality and Lipschitz continuity in the eigensystem of the underlying contact network, which do not hold for directed networks. In this work, we derive a theoretical analysis on the general epidemic threshold condition for directed networks and show that such threshold condition can be used as an optimization objective to control the spread of the disease. Based on the epidemic threshold, we propose an asymptotically greedy algorithm DINO (DIrected NetwOrk epidemic containment) to identify the most critical nodes for epidemic containment. The proposed algorithm is evaluated on real-world directed networks, and the results validate its effectiveness and efficiency. 

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3. Model-Free Event-Triggered Containment Control of Multi-Agent Systems

   https://doi.org/10.23919/ACC.2018.8430818  

   Yang, Yongliang; Modares, Hamidreza; Vamvoudakis, Kyriakos G.; Yin, Yixin; Wunsch, Donald C. (June 2018, 2018 Annual American Control Conference (ACC))

   This paper presents a model-free distributed event-triggered containment control scheme for linear multiagent systems. The proposed event-triggered scheme guarantees asymptotic stability of the equilibrium point of the containment error as well the avoidance of the Zeno behavior. To relax the requirement of complete knowledge of the dynamics, we combine an off-policy reinforcement learning algorithm in an actor critic structure with the event-trigger control mechanism to obtain the feedback gain of the distributed containment control protocol. A simulation experiment is conducted to verify the effectiveness of the approach. 

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4. Optimal Communication Topology and Static Output Feedback of Networked Collocated Actuator/Sensor Pairs in Distributed Parameter Systems

   https://doi.org/10.23919/ACC45564.2020.9147660  

   Demetriou, Michael A.; Hadjicostis, Christoforos N. (July 2020, 2020 American Control Conference (ACC))

   This paper is motivated by economic aspects of fixed initial and operating costs for control of spatially distributed systems. In particular, the paper investigates the possibility of a large number of inexpensive actuating and sensing devices, as an alternative to (a reduced number of) expensive high capacity devices. While such an alternative reduces the fixed initial costs associated with actuators and sensors, it may also lead to increased operating costs resulting from communication requirements between the now-networked actuator-sensor-control units. To simplify the controller architecture, a proportional controller is assumed that amounts to a static output feedback controller. In a network of n actuator-sensor pairs, an all-to-all communication topology results in a fully populated static output feedback matrix with as much as n(n-1) communication links. In addition to a traditional performance index used to obtain the static output feedback gain matrix, this paper proposes a mixed index wherein both the traditional performance index and the number of communication links (representing operating costs associated with information exchange links), are taken into account. As an example, the proposed scheme is applied to a parabolic partial differential equation having four actuator-sensor pairs. The resulting optimization produces a sparse static gain matrix with a communication topology that has half the graph edges of the fully connected case and with essentially the same performance. 

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5. Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting

   https://doi.org/10.1145/3634913  

   Wang, Tianyi; Chen, Shu-Ching (September 2024, ACM Transactions on Spatial Algorithms and Systems)

   Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic spatio-temporal relations in the traffic data, which are crucial in capturing the traffic dynamic. In this work, we propose AJSTGL, a novel adaptive joint spatio-temporal graph learning network for traffic data forecasting. The proposed model utilizes static and adaptive graph learning modules to capture the static and dynamic spatial traffic patterns and optimize the graph learning process. A sequence-to-sequence fusion model is proposed to learn the temporal correlation and combine the output of multiple parallelized encoders. We also develop a spatio-temporal graph transformer module to complement the sequence-to-sequence fusion module by dynamically capturing the time-evolving node relations in long-term intervals. Experiments on three large-scale traffic flow datasets demonstrate that our model could outperform other state-of-the-art baseline methods. 

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