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- All Subjects: PID controllers
- Creators: Liu, Yiqiu
- Creators: Serrano Rodriguez, Victoria Melissa
Description
Vertical taking off and landing (VTOL) drones started to emerge at the beginning of this century, and finds applications in the vast areas of mapping, rescuing, logistics, etc. Usually a VTOL drone control system design starts from a first principles model. Most of the VTOL drones are in the shape of a quad-rotor which is convenient for dynamic analysis.
In this project, a VTOL drone with shape similar to a Convair XFY-1 is studied and the primary focus is developing and examining an alternative method to identify a system model from the input and output data, with which it is possible to estimate system parameters and compute model uncertainties on discontinuous data sets. We verify the models by designing controllers that stabilize the yaw, pitch, and roll angles for the VTOL drone in the hovering state.
This project comprises of three stages: an open-loop identification to identify the yaw and pitch dynamics, an intermediate closed-loop identification to identify the roll action dynamic and a closed-loop identification to refine the identification of yaw and pitch action. In open and closed loop identifications, the reference signals sent to the servos were recorded as inputs to the system and the angles and angular velocities in yaw and pitch directions read by inertial measurement unit were recorded as outputs of the system. In the intermediate closed loop identification, the difference between the reference signals sent to the motors on the contra-rotators was recorded as input and the roll angular velocity is recorded as output. Next, regressors were formed by using a coprime factor structure and then parameters of the system were estimated using the least square method. Multiplicative and divisive uncertainties were calculated from the data set and were used to guide PID loop-shaping controller design.
In this project, a VTOL drone with shape similar to a Convair XFY-1 is studied and the primary focus is developing and examining an alternative method to identify a system model from the input and output data, with which it is possible to estimate system parameters and compute model uncertainties on discontinuous data sets. We verify the models by designing controllers that stabilize the yaw, pitch, and roll angles for the VTOL drone in the hovering state.
This project comprises of three stages: an open-loop identification to identify the yaw and pitch dynamics, an intermediate closed-loop identification to identify the roll action dynamic and a closed-loop identification to refine the identification of yaw and pitch action. In open and closed loop identifications, the reference signals sent to the servos were recorded as inputs to the system and the angles and angular velocities in yaw and pitch directions read by inertial measurement unit were recorded as outputs of the system. In the intermediate closed loop identification, the difference between the reference signals sent to the motors on the contra-rotators was recorded as input and the roll angular velocity is recorded as output. Next, regressors were formed by using a coprime factor structure and then parameters of the system were estimated using the least square method. Multiplicative and divisive uncertainties were calculated from the data set and were used to guide PID loop-shaping controller design.
ContributorsLiu, Yiqiu (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Thesis advisor) / Rivera, Daniel (Committee member) / Arizona State University (Publisher)
Created2015
Description
Buck converters are electronic devices that changes a voltage from one level to a lower one and are present in many everyday applications. However, due to factors like aging, degradation or failures, these devices require a system identification process to track and diagnose their parameters. The system identification process should be performed on-line to not affect the normal operation of the device. Identifying the parameters of the system is essential to design and tune an adaptive proportional-integral-derivative (PID) controller.
Three techniques were used to design the PID controller. Phase and gain margin still prevails as one of the easiest methods to design controllers. Pole-zero cancellation is another technique which is based on pole-placement. However, although these controllers can be easily designed, they did not provide the best response compared to the Frequency Loop Shaping (FLS) technique. Therefore, since FLS showed to have a better frequency and time responses compared to the other two controllers, it was selected to perform the adaptation of the system.
An on-line system identification process was performed for the buck converter using indirect adaptation and the least square algorithm. The estimation error and the parameter error were computed to determine the rate of convergence of the system. The indirect adaptation required about 2000 points to converge to the true parameters prior designing the controller. These results were compared to the adaptation executed using robust stability condition (RSC) and a switching controller. Two different scenarios were studied consisting of five plants that defined the percentage of deterioration of the capacitor and inductor within the buck converter. The switching logic did not always select the optimal controller for the first scenario because the frequency response of the different plants was not significantly different. However, the second scenario consisted of plants with more noticeable different frequency responses and the switching logic selected the optimal controller all the time in about 500 points. Additionally, a disturbance was introduced at the plant input to observe its effect in the switching controller. However, for reasonable low disturbances no change was detected in the proper selection of controllers.
Three techniques were used to design the PID controller. Phase and gain margin still prevails as one of the easiest methods to design controllers. Pole-zero cancellation is another technique which is based on pole-placement. However, although these controllers can be easily designed, they did not provide the best response compared to the Frequency Loop Shaping (FLS) technique. Therefore, since FLS showed to have a better frequency and time responses compared to the other two controllers, it was selected to perform the adaptation of the system.
An on-line system identification process was performed for the buck converter using indirect adaptation and the least square algorithm. The estimation error and the parameter error were computed to determine the rate of convergence of the system. The indirect adaptation required about 2000 points to converge to the true parameters prior designing the controller. These results were compared to the adaptation executed using robust stability condition (RSC) and a switching controller. Two different scenarios were studied consisting of five plants that defined the percentage of deterioration of the capacitor and inductor within the buck converter. The switching logic did not always select the optimal controller for the first scenario because the frequency response of the different plants was not significantly different. However, the second scenario consisted of plants with more noticeable different frequency responses and the switching logic selected the optimal controller all the time in about 500 points. Additionally, a disturbance was introduced at the plant input to observe its effect in the switching controller. However, for reasonable low disturbances no change was detected in the proper selection of controllers.
ContributorsSerrano Rodriguez, Victoria Melissa (Author) / Tsakalis, Konstantinos (Thesis advisor) / Bakkaloglu, Bertan (Thesis advisor) / Rodriguez, Armando (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2016