The virtual synchronous generator(VSG)technology has been proposed to address the problem of system frequency and active power oscillation caused by grid-connected new energy power sources.However,the traditional volt...The virtual synchronous generator(VSG)technology has been proposed to address the problem of system frequency and active power oscillation caused by grid-connected new energy power sources.However,the traditional voltage-current double-closed-loop control used in VSG has the disadvantages of poor disturbance immunity and insufficient dynamic response.In light of the issues above,a virtual synchronous generator voltage outer-loop control strategy based on improved linear autonomous disturbance rejection control(ILADRC)is put forth for consideration.Firstly,an improved first-order linear self-immunity control structure is established for the characteristics of the voltage outer loop;then,the effects of two key control parameters-observer bandwidthω_(0)and controller bandwidthω_(c)on the control system are analyzed,and the key parameters of ILADRC are optimally tuned online using improved gray wolf optimizer-radial basis function(IGWO-RBF)neural network.A simulationmodel is developed using MATLAB to simulate,analyze,and compare the method introduced in this paper.Simulations are performed with the traditional control strategy for comparison,and the results demonstrate that the proposed control method offers superior anti-interference performance.It effectively addresses power and frequency oscillation issues and enhances the stability of the VSG during grid-connected operation.展开更多
In this paper, a fuzzy sliding mode active disturbance rejection control(FSMADRC) scheme is proposed for an autonomous underwater vehicle-manipulator system(AUVMS) with a two-link and three-joint manipulator. First, t...In this paper, a fuzzy sliding mode active disturbance rejection control(FSMADRC) scheme is proposed for an autonomous underwater vehicle-manipulator system(AUVMS) with a two-link and three-joint manipulator. First, the AUVMS is separated into nine subsystems, and the combined effects of dynamic uncertainties, hydrodynamic force, unknown disturbances, and nonlinear coupling terms on each subsystem are lumped into a single total disturbance. Next, a linear extended state observer(LESO) is presented to estimate the total disturbance. Then, a sliding mode active disturbance rejection control(SMADRC) scheme is proposed to enhance the robustness of the control system. The stability of the SMADRC and the estimation errors of the LESO are analyzed. Because it is difficult to simultaneously adjust several parameters for a LESO-based SMADRC scheme, a fuzzy logic control(FLC) scheme is used to formulate the FSMADRC to determine the appropriate parameters adaptively for practical applications. Finally, two AUVMS tasks are illustrated to test the trajectory tracking performance of the closed-loop system and its ability to reject and attenuate the total disturbance. The simulation results show that the proposed FSMADRC scheme achieves better performance and consume less energy than conventional PID and FLC techniques.展开更多
The Tilt Quad Rotor(TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve ...The Tilt Quad Rotor(TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve the problem of control in conversion mode of TQR, this paper carries out the design of the controller based on improved Active Disturbance Rejection Control(ADRC). According to the characteristics of flight in conversion mode, Tracking Differentiator(TD) with explicit model is used to solve the problem of multiple integrals when the system is high-order system. Extended State Observer(ESO) with Radial Basis Function(RBF) neural network is used to estimate and compensate for internal and external uncertainties, and the adaptive sliding mode control in Nonlinear State Error Feedback(NLSEF) is used to improve the response speed of the controller and reduce the parameters which should be tuned. Through the flight control simulation of the TQR, the validity and rationality of the control system are verified.展开更多
基金supported by the Lanzhou Jiaotong University-Southwest Jiaotong University Joint Innovation Fund(LH2024027).
文摘The virtual synchronous generator(VSG)technology has been proposed to address the problem of system frequency and active power oscillation caused by grid-connected new energy power sources.However,the traditional voltage-current double-closed-loop control used in VSG has the disadvantages of poor disturbance immunity and insufficient dynamic response.In light of the issues above,a virtual synchronous generator voltage outer-loop control strategy based on improved linear autonomous disturbance rejection control(ILADRC)is put forth for consideration.Firstly,an improved first-order linear self-immunity control structure is established for the characteristics of the voltage outer loop;then,the effects of two key control parameters-observer bandwidthω_(0)and controller bandwidthω_(c)on the control system are analyzed,and the key parameters of ILADRC are optimally tuned online using improved gray wolf optimizer-radial basis function(IGWO-RBF)neural network.A simulationmodel is developed using MATLAB to simulate,analyze,and compare the method introduced in this paper.Simulations are performed with the traditional control strategy for comparison,and the results demonstrate that the proposed control method offers superior anti-interference performance.It effectively addresses power and frequency oscillation issues and enhances the stability of the VSG during grid-connected operation.
基金supported in part by the Fundamental Research Funds for the Central Universities (No. 201964012)the Open Foundation of Henan Key Laboratory of Underwater Intelligent Equipment (No. KL02A1802)+1 种基金the National Natural Science Foundations of China (Nos. 61603361 and 51979256)the Shandong Provincial Natural Science Foundation (No. ZR2017MEE015)。
文摘In this paper, a fuzzy sliding mode active disturbance rejection control(FSMADRC) scheme is proposed for an autonomous underwater vehicle-manipulator system(AUVMS) with a two-link and three-joint manipulator. First, the AUVMS is separated into nine subsystems, and the combined effects of dynamic uncertainties, hydrodynamic force, unknown disturbances, and nonlinear coupling terms on each subsystem are lumped into a single total disturbance. Next, a linear extended state observer(LESO) is presented to estimate the total disturbance. Then, a sliding mode active disturbance rejection control(SMADRC) scheme is proposed to enhance the robustness of the control system. The stability of the SMADRC and the estimation errors of the LESO are analyzed. Because it is difficult to simultaneously adjust several parameters for a LESO-based SMADRC scheme, a fuzzy logic control(FLC) scheme is used to formulate the FSMADRC to determine the appropriate parameters adaptively for practical applications. Finally, two AUVMS tasks are illustrated to test the trajectory tracking performance of the closed-loop system and its ability to reject and attenuate the total disturbance. The simulation results show that the proposed FSMADRC scheme achieves better performance and consume less energy than conventional PID and FLC techniques.
基金sponsored by China Aerodynamics Research and Development Center Rotor Aerodynamics Key Laboratory opening topic fund。
文摘The Tilt Quad Rotor(TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve the problem of control in conversion mode of TQR, this paper carries out the design of the controller based on improved Active Disturbance Rejection Control(ADRC). According to the characteristics of flight in conversion mode, Tracking Differentiator(TD) with explicit model is used to solve the problem of multiple integrals when the system is high-order system. Extended State Observer(ESO) with Radial Basis Function(RBF) neural network is used to estimate and compensate for internal and external uncertainties, and the adaptive sliding mode control in Nonlinear State Error Feedback(NLSEF) is used to improve the response speed of the controller and reduce the parameters which should be tuned. Through the flight control simulation of the TQR, the validity and rationality of the control system are verified.
