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您的检索式：作者名="Qiangang ZHENG"
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| 1 | A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information显示文摘The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm. | Qiangang Jia Yiyan Li Zheng Yan Chengke Xu Sijie Chen | 2022 | Journal of Modern Power Systems and Clean Energy2022，10，4： | 1 |
| 2 | Vector deflection stability control of aero-engine based on linear active disturbance rejection显示文摘Aimed at the problem of instability in engine control caused by vector deflection in experiment of turbofan engines with Axisymmetric Vectoring Exhaust Nozzle(AVEN),a vector deflection stability control method of aero-engine based on Linear Active Disturbance Rejection Control(LADRC)is proposed.Firstly,based on CFD numerical simulation,aerodynamic performance model of AVEN is established,and the aerodynamic load change rule of the nozzle throat area actuator during vector deflection is revealed.Subsequently,the integrated model of AVEN/-turbofan engine is established by Simulink/AMESim co-simulation.Finally,the nozzle throat area control loop based on LADRC is designed.The simulation results show that the integrated model can reflect the influence of vector deflection on the stability of the control system.The accuracy comparison between the fan rotor speed and the test data during vector deflection is larger than 1%,indicating a high degree of confidence.Compared with the conventional PID control,the designed LADRC control loop reduces the speed of the low-pressure rotor during vector deflection by 70%,which effectively improves the control stability of the vector deflection.Meanwhile,the fuel flow ratechange during the vector deflection process is smaller and more economical,which provides an important reference for engineering applications. | Changpeng CAI Yerong PENG Qiangang ZHENG Haibo ZHANG | 2022 | Chinese Journal of Aeronautics2022，35，8： | 0 |
| 3 | Maneuver control at high angle of attack based on real-time optimization of integrated aero-propulsion显示文摘To reduce the propulsion system installation thrust loss under high angle of attack maneuvering,a control method based on real-time optimization of the integrated aeropropulsion is proposed.Firstly,based on data fitting and physical principle,an integrated onboard model of propulsion system is established,which can calculate various performance parameters of the propulsion system in real time,and has high accuracy and real-time performance.Secondly,to improve the compatibility of optimization real-time performance and search accuracy,the online optimization control of aero-propulsion system is realized based on an improved trust region algorithm.Finally,by controlling the auxiliary intake valve,a good match between inlet and engine is realized,which solves the problems of intake flow reducing and total pressure recovery coefficient declining,and improves the installation performance of propulsion system.The simulation results indicate that,compared with the conventional independent engine control,the real-time integrated optimization method reduces the installed thrust loss by 3.61%under the design condition,and 4.58%under the off-design condition.Furthermore,the simulation on HIL(Hardware-In-theLoop)platform verifies the real-time performance of integrated optimization method. | Juan FANG Qiangang ZHENG Changpeng CAI Haibo ZHANG | 2022 | Chinese Journal of Aeronautics2022，35，12： | 0 |
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