Yazar "Karimi, Hamid Reza" seçeneğine göre listele

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    Öğe
    A distributed state and fault estimation scheme for state-saturated systems with measurements over sensor networks
    (Elsevier, 2024) Huang, Cong; Coskun, Serdar; Karimi, Hamid Reza; Ding, Weiping
    This article explores a new framework of distributed state and fault estimation (DSFE) for the state -saturated systems over sensor networks. To this aim, the upper bound on estimation error covariance (EEC) is ensured and the explicit expression of the corresponding estimator gains is given with both quantization effects and state saturations. Further, a feasible upper bound is located on EEC and minimized by parameterizing the estimator gain. The matrix simplification technique is adopted to deal with the sensor network topology's sparseness problem. Additionally, the estimation performance is first analyzed and then ensured by conducting a sufficient condition. At last, experiments are carried out to verify the feasibility of the developed DSFE method.
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    Öğe
    A multi-objective hierarchical deep reinforcement learning algorithm for connected and automated HEVs energy management
    (Pergamon-Elsevier Science Ltd, 2024) Coskun, Serdar; Yazar, Ozan; Zhang, Fengqi; Li, Lin; Huang, Cong; Karimi, Hamid Reza
    Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.