Browsing by Author "Sun, Hao"

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    Crowdsourced GNSS Satellite SNR in Degraded Environments for Dependability Improvement
    (2021-09-21) Sun, Hao; Lu, Debiao; Cai, Baigen; Xiao, Yu; Beijing Jiaotong University; Department of Communications and Networking
    Global Navigation Satellite Systems (GNSS) localization uses time of arrival (TOA) to measure the distance between satellite and rover antenna. The key for TOA technique is the signal propagation should be straight geometrically. However, when the train runs in harsh environments, the GNSS signal quality can be degraded due to obstacles. GNSS signals are usually blocked by buildings and other obstacles, resulting in reflection and reflection, which leads to multipath positioning errors of LOS and NLOS. In the case of LOS multipath error, the GNSS receiver tracks the direct and reflected / diffracted composite signal, and the reflected / diffracted signal affects the correlation function tracking loop of the direct signal in GNSS. In the case of NLOS multipath error, GNSS receiver directly tracks reflected or diffracted signal because there is no direct satellite signal. In both cases, the multipath signal will have a large noise error range which is reflected in the change of SNR. In the GNSS solution, various GNSS receivers are used to obtain the SNR of each satellite to achieve the fully coverage of the satellite signal reception quality. Thus with limited GNSS receiver observation results, of the crowdsourcing of NLOS signal inspection will deliver the observation results in a more convenient way. In the open environment scenario, the relationship between elevation angle and SNR can be fitted by a linear function. However, in GNSS signal reception degraded environments (urban canyon etc.), The SNR at the elevation angle of the occlusion boundary will be drastically reduced. This will give us the boundary of the occlusion range. Based on this principle, we can map the environmental characteristics of different sections of the railway. The railway environment can generally be divided into open scenario, road cutting scenario, half sky scenario, urban canyon scenario and tunnel scenario. 5 typical scenario all have distinct environmental characteristics. By matching the segment with the typical environmental characteristics, the scenario situation of the segment is determined, and then the accuracy of satellite positioning is given.
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    Dynamic modelling of parafoil system based on aerodynamic coefficients identification
    (2023) Zhao, Ligan; Tao, Jin; Sun, Hao; Sun, Qinglin; Nankai University; Department of Electrical Engineering and Automation
    This study focuses on the identification of aerodynamic coefficients through airdrop data based on a known six-degree-of-freedom (6-DOF) model. Since the precise modelling of the parafoil system is a prerequisite for design of guidance, navigation and control systems, the accurate calculation of aerodynamic coefficients is vital. This paper first presents a rigid 6-DOF nonlinear model and simplifies it by linearizing the equations around the equilibrium point, which facilitates the acquisition of aerodynamic parameters next. Then, the recursive weighted least square method is applied to identify roll and yaw coefficients from airdrop test data. At last, simulations in the wind environment are implemented to analyse the dynamics of gliding and turning. The airdrop experiment also verifies the effectiveness of the dynamic model and the accuracy of identification.
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    An intelligent course keeping active disturbance rejection controller based on double deep Q-network for towing system of unpowered cylindrical drilling platform
    (John Wiley and Sons Ltd, 2021-11-25) Zheng, Yuemin; Tao, Jin; Sun, Qinglin; Sun, Hao; Sun, Mingwei; Chen, Zengqiang; Nankai University; Robotic Instruments; Department of Electrical Engineering and Automation
    Towing is a widely used mode of transportation in offshore engineering, and towing of unpowered platforms is of particular significance. However, the addition of unpowered facilities has increased the difficulty of ship maneuvering. Moreover, the marine environment is complex and changeable, and sudden winds or waves can have unpredictable effects on the towing process. Therefore, it is of great significance to overcome the influence of the harsh marine environment while navigating the towing system following a planned course to a target sea area. To tackle the time-varying disturbances, a course control method for a towing system of unpowered cylindrical drilling platform is designed based on double deep Q-network (DQN) optimized linear active disturbance rejection control (LADRC). To be specific, to tackle the difficulty of LADRC tuning, double DQN is applied to select the best parameters of the LADRC at any time according to the states of the system, without relying on the specific information of the modeland the controller. The course control performance of the towing system is evaluated in a simulation environment under various disturbances. Moreover, the Monte Carlo experiment is used to test the robustness of the controller when the ship's mass changes and the robustness of the proposed method is verified by testing with various heading angles. The results show that the LADRC with adaptive parameters optimized by double DQN performs well under external interference and inherent uncertainty, and compared with the traditional LADRC, the proposed method has better course control effects.
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    LADRC-based Path Following Control for Cylindrical Drilling Platform Towing System
    (2020-11-06) Tao, Jin; Du, Lei; Sun, Hao; Sun, Qinglin; Xie, Guangming; Zhou, Quan; Robotic Instruments; Marine Technology; Nankai University; Peking University; Department of Electrical Engineering and Automation; Department of Mechanical Engineering
    The towing process is the precondition to put a cylindrical drilling platform into use, which is saturated with risk due to the complexity of the towing environment, towing maneuvering, and the sudden and severity of accidents. Therefore, to control the cylindrical drilling platform towing system safely following a predefined course to reach the target see area becomes increasingly important. For environmental disturbances caused by wind and currents changing with time, a linear adaptive-disturbance-rejection-control (LADRC) based path following control method for cylindrical drilling platform towing system is proposed. Firstly, on the basis of both the mathematical modeling group model and the catenary model, three degrees of freedom nonlinear model of the cylindrical drilling platform towing system is built to obtain its real-time motion state. Then, a LADRC controller based on a two-dimensional trajectory tracking guidance law is designed for real-time path following control. Finally, simulation experiments of the path following control for the cylindrical drilling platform towing system is conducted. The results illustrate that the LADRC can effectively resist influences of the environmental disturbances and has a better path following performances than the traditional proportional-integral-derivative (PID) controller.
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    Load frequency active disturbance rejection control for multi-source power system based on soft actor-critic
    (MDPI AG, 2021-08-06) Zheng, Yuemin; Tao, Jin; Sun, Hao; Sun, Qinglin; Chen, Zengqiang; Dehmer, Matthias; Zhou, Quan; Nankai University; Robotic Instruments; Swiss Distance University of Applied Sciences; Department of Electrical Engineering and Automation
    To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft ActorCritic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.
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    Power system load frequency active disturbance rejection control via reinforcement learning-based memetic particle swarm optimization
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-08) Zheng, Yuemin; Huang, Zhaoyang; Tao, Jin; Sun, Hao; Sun, Qinglin; Dehmer, Matthias; Sun, Mingwei; Chen, Zengqiang; Department of Electrical Engineering and Automation; Robotic Instruments; Nankai University; Swiss Distance University of Applied Sciences
    Load frequency control (LFC) is necessary to guarantee the safe operation of power systems. Aiming at the frequency and power stability problems caused by load disturbances in interconnected power systems, active disturbance rejection control (ADRC) was designed. There are eight parameters that need to be adjusted for an ADRC, which are challenging to adjust manually, thus limiting the development of this approach in industrial applications. Regardless of the theory or application, there is still no unified and efficient parameter optimization method. The traditional particle swarm optimization (PSO) algorithm suffers from premature convergence and a high computational cost. Therefore, in this paper, we utilize an improved PSO algorithm, a reinforcement-learning-based memetic particle swarm optimization (RLMPSO), for the parameter tuning of ADRC to obtain better control performance for the controlled system. Finally, to highlight the advantages of the proposed RLMPSO-ADRC method and to prove its superiority, the results were compared with other control algorithms in both a traditional non-reheat two-area thermal power system and a non-linear power system with a governor dead band (GDB) and a generation rate constraint (GRC). Moreover, the robustness of the proposed method was tested by simulations with parameter perturbations and different working conditions. The simulation results showed that the proposed method can meet the demand for the frequency deviation to stabilize to 0 in LFC with higher performance, and it is worthy of popularization and application.