Browsing by Author "Panda, Deepak Kumar"

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    Action robust reinforcement learning for air mobility deconfliction against conflict induced spoofing
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-12) Panda, Deepak Kumar; Guo, Weisi
    Increased dynamic drone usage has increased complexity in aerial navigation and often demands distributed local deconfliction. Due to the high velocities and few landmarks, robust deconfliction relies on precise positioning and synchronization. However, intentional spoofing attacks aimed at inducing navigation conflicts threaten the reliability of conventional techniques. Here, we address these concerns by establishing a baseline on the impact of novel conflict-inducing spoofing attacks on existing geometric navigation methods. Based on the impact of the attacks on the navigation, reinforcement learning (RL) strategy is used to counter the effects of spoofing attacks. In order to counter the effect of spoofing in randomized dynamic airspace conditions, a zero-sum action-robust (ZSAR) RL based on mixed Nash equilibrium objective is used. The proposed methodology yields an improved number of conflict-free paths while reducing average conflicts compared to existing state of the art RL strategies, thus making it suitable for deploying autonomous aircrafts.
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    Fragility impact of RL based advanced air mobility under gradient attacks and packet drop constraints
    (IEEE, 2023-12-11) Panda, Deepak Kumar; Guo, Weisi
    The increasing utilization of unmanned aerial vehicles (UAVs) in advanced air mobility (AAM) necessitates highly automated conflict resolution and collision avoidance strategies. Consequently, reinforcement learning (RL) algorithms have gained popularity in addressing conflict resolution strategies among UAVs. However, increasing digitization introduces challenges related to packet drop constraints and various adversarial cyber threats, rendering AAM fragile. Adversaries can introduce perturbations into the system states, reducing the efficacy of learning algorithms. Therefore, it is crucial to systematically investigate the impact of increased digitization, including adversarial cyber-threats and packet drop constraints to study the fragile characteristics of AAM infrastructure. This study examines the performance of artificial intelligence(AI) based path planning and conflict resolution strategies under different adversarial and stochastic packet drop constraints in UAV systems. The fragility analysis focuses on the number of conflicts, collisions and fuel consumption of the UAVs with respect to its mission, considering various adversarial attacks and packet drop constraint scenarios. The safe deep q-networks (DQN) architecture is utilized to navigate the UAVs, mitigating the adversarial threats and is benchmarked with vanilla DQN using the necessary metrics. The findings are a foundation for investigating the necessary modification of learning paradigms to develop antifragile strategies against emerging adversarial threats.
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    Observer based decentralized load frequency control with false data injection attack for specified network quality and delay
    (Elsevier, 2024-08-01) Panda, Deepak Kumar; Halder, Kaushik; Das, Saptarshi; Townley, Stuart
    Load frequency control (LFC) aims to stabilize grid frequency fluctuations by countering load disturbances with generation-side controllers. In smart grids, demand response (DR) and electric vehicles (EV) offer alternatives to traditional frequency control, reducing reliance on costly generation-side controllers. These decentralized controls, interconnected through a shared communication medium, form a cyber-physical system, vulnerable to challenges like packet drops and false data injection (FDI) attacks. Additionally, consumer participation in DR introduces significant time delays. This paper derives stability conditions for LFC using a state feedback controller, estimating unobservable states with an observer while accounting for bounded disturbances and noise. This cyber-physical system, involving an observer, controller, and network, is modelled as an observer-based networked control system (NCS) using an asynchronous dynamical system (ADS) approach. The resulting switched system model is used to establish linear matrix inequality (LMI) criteria that ensure stability and determine observer and controller gains under specified packet drop rates, disturbances, and noise. The methodology is tested on various configurations, demonstrating that decentralized EV with LFC and DR improves system response, minimizes frequency fluctuations, and optimizes networked control bandwidth under given conditions.