A sliding mode control design for a miniature unmanned helicopter is presented. The control objective is to let the helicopter track some predefined velocity and yaw trajectories. A new sliding mode control design met...A sliding mode control design for a miniature unmanned helicopter is presented. The control objective is to let the helicopter track some predefined velocity and yaw trajectories. A new sliding mode control design method is developed based on a linearized dynamic model. In order to facilitate the control design, the helicopter＇s dynamic model is divided into two subsystems,such as the longitudinal-lateral and the heading-heave subsystem. The proposed controller employs sliding mode control technique to compensate for the immeasurable flapping angles＇ dynamic effects and external disturbances. The global asymptotic stability（GAS） of the closed-loop system is proved by the Lyapunov based stability analysis. Numerical simulations demonstrate that the proposed controller can achieve superior tracking performance compared with the proportionalintegral-derivative（PID） and linear-quadratic regulator（LQR） cascaded controller in the presence of wind gust disturbances.展开更多
To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and ...To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.展开更多
基金supported by the Natural Science Foundation of Tianjin(No.14JCZDJC31900)
文摘A sliding mode control design for a miniature unmanned helicopter is presented. The control objective is to let the helicopter track some predefined velocity and yaw trajectories. A new sliding mode control design method is developed based on a linearized dynamic model. In order to facilitate the control design, the helicopter＇s dynamic model is divided into two subsystems,such as the longitudinal-lateral and the heading-heave subsystem. The proposed controller employs sliding mode control technique to compensate for the immeasurable flapping angles＇ dynamic effects and external disturbances. The global asymptotic stability（GAS） of the closed-loop system is proved by the Lyapunov based stability analysis. Numerical simulations demonstrate that the proposed controller can achieve superior tracking performance compared with the proportionalintegral-derivative（PID） and linear-quadratic regulator（LQR） cascaded controller in the presence of wind gust disturbances.
基金supported by National Natural Science Foundation of China(No.52266012)Gansu Province College Industry Support Plan Project(No.2022CYZC-34)+1 种基金Gansu Province Major Science and Technology Special Project(Nos.20ZD7GF011,22ZD6GA063)Jiuquan City Science and Technology Programme Project(No.2023CA3058).
文摘To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.
