## 3 Methodology
### Genetic Algorithm
This is the exact reason that a PSO-equipped controller will outperform manual tuned controller, as demonstrated in Figure 13.

When analyzing roll displacement, it is apparent that the controller tuned by GA has the shortest settling time and the least %OS, which is corrected quickly. The PSO controller greatly overshoots its target value and overshoots on its return to zero, making it an unsuitable controller type. The GA-Hybrid controller has a slight overshoot but a long settling time. Similarly, the controller tuned manually has no overshoot but like GA-Hybrid, the settling time is too long to be considered as an effective control strategy. Analysis of yaw displacement and yaw rate shows the controller tuned by the GA method as the standout controller with no overshoot and a very small settling time, the final yaw displacement settling at 21 degrees. There is a slight amount of disturbance present in the yaw rate plot, this is due to the trade off between speed of response and rejection of noise discussed in Section 3.3.3, however, this level of disturbance is an acceptable trade-off for the speed of response. The other tuning methods also settle quickly but overshoot the point of stability slightly and do not stabilize quickly, as they maintain a slight negative velocity meaning these controllers are in a constant negative displacement. ### Comparisons of Best Performing Tuning Method from Each Control Type

The plot in Figure 14 shows the comparison of each of the best tuning methods for each of the different controller types. The benchmark controller \"PID SOF\" (PID with static output feedback) was included to compare the optimal controllers against the basic conventional method. Having discovered in Sections 5.1 and 5.2 that GA performed best at correcting vehicle offset from wind than any other tuning method, this section aims to compare between controller architectures to find the best architecture for RUAV control. Firstly, while comparing between controller architectures, it is clear that those controllers that utilize a filter or observer have superior noise attenuation than those without. This is of particular importance in aerial vehicle applications where rotor dynamics can cause large actuator noise, preventing effective correction of the vehicle. Both Full State Feedback tuned with GA (LQR-GA) and PID with Static Output Feedback (PID SOF) are very noisy and produce a response that would be unusable for real applications. However, the controllers that do make use of an observer or filter have better noise attenuation enabling a clean signal to be preserved, enabling it to be used in real applications. Figure 15 can be used to easily evaluate the performance of each controller type in percentage overshoot and settling time. When evaluating controllers on settling time, the GA tuned LQG (LQG-GA) controller outperforms every other controller tested in both roll displacement and yaw rate. Similarly, when analyzing percentage overshoot performance, LQG-GA is clearly the best controller of those tested as it has the least overshoot for parameters of pitch displacement, roll displacement and yaw rate. This ensures that the vehicle is moved from a place of state offset back to a constant hover state with no overshoot of its target providing good vehicle response for correction under harsh conditions. PIDSOF does have a better settling time than LQG for pitch displacement, but due to its poor attenuation of noise, this controller is not a viable option to consider as the best controller. It can be concluded from this section the LQG is the best controller of those tested for RUAV state correction in cases of high external disturbance due to wind and sensor noise. ## 6 Future Work

As discussed in the previous section, Linear Quadratic Gaussian controllers tuned using machine learning methods such as Genetic Algorithm are capable of outperforming those same controllers tuned by manually.