Brushless DC motor ( BLDCM) speed servo system is multivariable,nonlinear and strong coupling. The parameter variation, the cogging torque and the load disturbance easily influence its performance. Therefore,it is dif...Brushless DC motor ( BLDCM) speed servo system is multivariable,nonlinear and strong coupling. The parameter variation, the cogging torque and the load disturbance easily influence its performance. Therefore,it is difficult to achieve superior performance by using the conventional PID controller. To solve the deficiency,the paper represents the algorithm of active-disturbance rejection control ( ADRC) based on back-propagation ( BP) neural network. The ADRC is independent on accurate system and its extended-state observer can estimate the disturbance of the system accurately. However,the parameters of Nonlinear Feedback ( NF) in ADRC are difficult to obtain. So in this paper,these parameters are self-turned by the BP neural network. The simulation and experiment results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity,control accuracy,adaptability and robustness.展开更多
The position synchronization control(PSC) problem is studied for networked multi-axis servo systems(NMASSs) with time-varying delay that is smaller than one sampling period. To improve the control performance of the s...The position synchronization control(PSC) problem is studied for networked multi-axis servo systems(NMASSs) with time-varying delay that is smaller than one sampling period. To improve the control performance of the system, time-varying delays, modeling uncertainties, and external disturbances are first modeled as a lumped disturbance. Then, a linear extended state observer(LESO) is devised to estimate the system state and the lumped disturbance, and a linear feedback controller with disturbance compensation is designed to perform individual-axis tracking control. After that, a cross-coupled control approach is used to further improve synchronization performance. The bounded-input-bounded-output(BIBO) stability of the closedloop control system is analyzed. Finally, both simulation and experiment are carried out to demonstrate the effectiveness of the proposed method.展开更多
Keeping pressure gradient is an excellent approach to prevent the reveal of </span><span style="font-family:"white-space:normal;">airflow direction and cross infection in manufacturing ...Keeping pressure gradient is an excellent approach to prevent the reveal of </span><span style="font-family:"white-space:normal;">airflow direction and cross infection in manufacturing circumstances of ph</span><span style="font-family:"white-space:normal;">armaceutical cleanrooms, thus how to keep cleanroom’s pressure is critical. In </span><span style="font-family:"white-space:normal;">the paper, we study a positive pressure pharmaceutical cleanroom system wh</span><span style="font-family:"white-space:normal;">ich is composed by a cleanroom and an airlock. We divide the system’s disturbances into step disturbance, ramp disturbance and sine wave disturbance. </span><span style="font-family:"white-space:normal;">We design its pressure gradient control strategies, including CAV control, PI</span><span style="font-family:"white-space:normal;">D control and active-disturbance-rejection-control. We build the system’s mod</span><span style="font-family:"white-space:normal;">el and make simulations based on Matlab/Simulink software platform. Re</span><span style="font-family:"white-space:normal;">sults show that active-disturbance-rejection-control algorithm has good capabilities for shorter responding time and lower overshot of the pressure gradient. The results reveal that active-disturbance-rejection-control method has good control performances in responding time, accuracy and disturbance rejection.展开更多
文摘Brushless DC motor ( BLDCM) speed servo system is multivariable,nonlinear and strong coupling. The parameter variation, the cogging torque and the load disturbance easily influence its performance. Therefore,it is difficult to achieve superior performance by using the conventional PID controller. To solve the deficiency,the paper represents the algorithm of active-disturbance rejection control ( ADRC) based on back-propagation ( BP) neural network. The ADRC is independent on accurate system and its extended-state observer can estimate the disturbance of the system accurately. However,the parameters of Nonlinear Feedback ( NF) in ADRC are difficult to obtain. So in this paper,these parameters are self-turned by the BP neural network. The simulation and experiment results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity,control accuracy,adaptability and robustness.
基金supported by the National Natural Science Foundation of China(NSFC)(61822311)the NSFC-Zhejiang Joint Fund for the Intergration of Industrialization and Informatization(U1709213)。
文摘The position synchronization control(PSC) problem is studied for networked multi-axis servo systems(NMASSs) with time-varying delay that is smaller than one sampling period. To improve the control performance of the system, time-varying delays, modeling uncertainties, and external disturbances are first modeled as a lumped disturbance. Then, a linear extended state observer(LESO) is devised to estimate the system state and the lumped disturbance, and a linear feedback controller with disturbance compensation is designed to perform individual-axis tracking control. After that, a cross-coupled control approach is used to further improve synchronization performance. The bounded-input-bounded-output(BIBO) stability of the closedloop control system is analyzed. Finally, both simulation and experiment are carried out to demonstrate the effectiveness of the proposed method.
文摘Keeping pressure gradient is an excellent approach to prevent the reveal of </span><span style="font-family:"white-space:normal;">airflow direction and cross infection in manufacturing circumstances of ph</span><span style="font-family:"white-space:normal;">armaceutical cleanrooms, thus how to keep cleanroom’s pressure is critical. In </span><span style="font-family:"white-space:normal;">the paper, we study a positive pressure pharmaceutical cleanroom system wh</span><span style="font-family:"white-space:normal;">ich is composed by a cleanroom and an airlock. We divide the system’s disturbances into step disturbance, ramp disturbance and sine wave disturbance. </span><span style="font-family:"white-space:normal;">We design its pressure gradient control strategies, including CAV control, PI</span><span style="font-family:"white-space:normal;">D control and active-disturbance-rejection-control. We build the system’s mod</span><span style="font-family:"white-space:normal;">el and make simulations based on Matlab/Simulink software platform. Re</span><span style="font-family:"white-space:normal;">sults show that active-disturbance-rejection-control algorithm has good capabilities for shorter responding time and lower overshot of the pressure gradient. The results reveal that active-disturbance-rejection-control method has good control performances in responding time, accuracy and disturbance rejection.
