1. A Central Pattern Generator-Based Control Strategy of a Nature-Inspired Unmanned Aerial Vehicle
- Author
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Shane Kyi Hla Win, Gim Song Soh, Shaohui Foong, Ying Hong Pheh, Luke Thura Soe Win, and Danial Sufiyan
- Subjects
0301 basic medicine ,03 medical and health sciences ,030104 developmental biology ,Computer science ,Control theory ,Lift (data mining) ,Plane (geometry) ,030106 microbiology ,Path (graph theory) ,Trajectory ,Central pattern generator ,Actuator ,Tracking (particle physics) - Abstract
This work introduces a Central Pattern Generator (CPG)-based control formulation for a dual-winged nature-inspired Unmanned Aerial Vehicle (UAV). Unlike majority of the current popular configurations of UAVs such as multirotors and fixed-wing aircraft, this particular nature-inspired UAV generates lift by spinning its entire body about a central axis. This inherent oscillatory nature makes this particular type of UAV suitable for the implementation of a CPG-based controller, which also possess oscillatory characteristics. The CPG handles the low-level actuator commands given a simple higher-level input. Policy Gradients with Parameter-based Exploration (PGPE) was used to optimize the CPG parameters to obtain the desired UAV motion from the CPG inputs. A Tip Path Plane (TPP) angle controller was added on top of the CPG to form a TPP-CPG controller, in which closed-loop position control was implemented above this. Hovering and trajectory tracking tests were successfully conducted and the performance of the control strategy was verified.
- Published
- 2020