| Summary: | Remote sensing technology is becoming a standard tool for Arctic studies to understand and preserve vulnerable ecosystems. A Ground Penetrating Radar (GPR) is a common tool used to study the change in ice properties, which can help reduce climate change effects. However, due to the GPR's large size and the Arctic's remote terrain, it is difficult to navigate GPRs in the Arctic. Flying the GPR using a drone provides a solution, but it requires the GPR to be suspended from the drone which can affect the drone's stability through the non-linear sway of the GPR. The non-linear effects may not be well captured by linear controllers and therefore require the implementation of a non-linear controller on the drone. In this thesis, I use a Light Detection and Ranging (LiDAR) sensor and an Extended Kalman filter to measure the position of the payload relative to the drone with an accuracy of 2 cm. Additionally, I design a feedback control system for the drone carrying a payload to compare the performance of a feedback integer-order Proportional-Integral-Derivative (IOPID) controller and fractional-order PID (FOPID) controller to minimize the sway of the payload. Results from simulation and experimental tests indicate that if optimally tuned, both the IOPID controller and FOPID controller (using the Oustaloup recursive filter) had a comparable performance for a non-linear drone-based cable-suspended system. Neither feedback controller provided the optimal performance to control the sway of the payload and a model-based control technique may be a better solution. May 2023
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