A novel electro-hydrostatic actuator （EHA） for active vibration isolation has been designed, modelled and tested. The EHA consists of a brushless DC motor running in oil and integrated with a bidirectional gear pu...A novel electro-hydrostatic actuator （EHA） for active vibration isolation has been designed, modelled and tested. The EHA consists of a brushless DC motor running in oil and integrated with a bidirectional gear pump, driving a hydraulic cylinder. The actuator is designed to be integrated into a flexible strut connecting a helicopter rotor hub and fuselage, to provide isolation at the dominant rotor vibration frequency of around 20 Hz. The resonant frequency of the EHA is tuned to provide some passive vibration isolation. Active control increases the isolation performance by compensating for damping losses, and provides isolation over a broader range of frequencies. Tests on a prototype demonstrated a four-fold reduction of the root-mean-square transmitted force and a near elimination at the fundamental frequency. The advantages of the resonant EHA are a wider range of operating frequencies than a purely passive system, and lower power consumption than a purely active system.展开更多
A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar f...A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar friction. A steady-state Kalman filter is built based on the model of pipeline dynamics. Pressure signals at both ends of a target section of a pipe are input to the model of pipeline dynamics, and as an output of the model an estimated pressure signal at a mid-point of the pipe is obtained. Difference between measured and estimated pressure signals at the mid-point is fed back to the model of pipeline dynamics to modify state variables of the model. According to the Kalman filter principle, the state variables of the model are adjusted so that they converge to real values. It is demonstrated that real-time implementation of the Kalman filter is possible with the sampling time of 0.1 ms.展开更多
文摘A novel electro-hydrostatic actuator （EHA） for active vibration isolation has been designed, modelled and tested. The EHA consists of a brushless DC motor running in oil and integrated with a bidirectional gear pump, driving a hydraulic cylinder. The actuator is designed to be integrated into a flexible strut connecting a helicopter rotor hub and fuselage, to provide isolation at the dominant rotor vibration frequency of around 20 Hz. The resonant frequency of the EHA is tuned to provide some passive vibration isolation. Active control increases the isolation performance by compensating for damping losses, and provides isolation over a broader range of frequencies. Tests on a prototype demonstrated a four-fold reduction of the root-mean-square transmitted force and a near elimination at the fundamental frequency. The advantages of the resonant EHA are a wider range of operating frequencies than a purely passive system, and lower power consumption than a purely active system.
文摘A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar friction. A steady-state Kalman filter is built based on the model of pipeline dynamics. Pressure signals at both ends of a target section of a pipe are input to the model of pipeline dynamics, and as an output of the model an estimated pressure signal at a mid-point of the pipe is obtained. Difference between measured and estimated pressure signals at the mid-point is fed back to the model of pipeline dynamics to modify state variables of the model. According to the Kalman filter principle, the state variables of the model are adjusted so that they converge to real values. It is demonstrated that real-time implementation of the Kalman filter is possible with the sampling time of 0.1 ms.
