## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Author** & **Type** & **Category** & **Advantages** & **Disadvantages** \\ \hline
1 & [PERSON] et al. [3] & PID Control & Linar & Unit step provides a fast response with small overshoot & Slow and inefficient to retune each time the input changes. No sensor noise attenuation \\ \hline
2 & [PERSON] et al. [4] & PID+SOF & Linear & Fast settling time and minimum overshoot when compared to PID & No noise attenuation. \\ \hline
3 & [PERSON] et al. [5] & LQR-TAE & Linear & Better Steady-State Error, transient response and reduction in control effort than PID & No test against external disturbance. Difficult to tune complex systems optimally using the Trial and Error (TAE) method. \\ \hline
4 & [PERSON] et al. [6] & LQR-TAE & Linear & Controller responds well to disturbance introduced as a sine wave & Sine wave is an unrealistic disturbance. Difficult to optimally tune for a complex system by TAE \\ \hline
5 & [PERSON] et al. [7] & Integral & Linear & Eliminates all Steady-State error when applied to nonlinear system because of the integrator & Disturbances are constant offsets that do not accurately represent RUAV disturbances \\ \hline
6 & [PERSON] et al. [8] & LQG-TAE & Linear & Gaussian noise in sensor output rejected by Kalman Filter & Complex to tune by TAE for MIMO systems \\ \hline
7 & [PERSON] et al. [9] & LQG-PSO & Linear & Tuned with a small number of iterations & No improvement to the transient response when tuned with Particle Swarm Optimisation (PSO) \\ \hline
8 & [PERSON] s et al. [10] & LQG-PSO & Linear & Lower Control Effort required & No improvement in transient response by tuning with PSO \\ \hline
9 & [PERSON] et al. [11] & LQG-PSO & Linear & Better transient response observed for PSO-LQG when compared with PID & Better response is more likely down to comparison of PID with LQG as opposed to LQG and PSO-LQG \\ \hline
10 & [PERSON] et al. [12] & H-Infinity & Linear & Improved Anti-Interference when compared to LQR & Slower settling time when compared to LQR \\ \hline
11 & [PERSON] et al. [13] & H-Infinity & Linear & Improved stability in wind disturbance than PID control & With no disturbance H-inf was slower than PID \\ \hline
12 & [PERSON] et al. [14] & H-Infinity & Linear & H-Infinity outperforms LQG in high wind disturbances, maintaining low percentage overshoot & Benefits of H-Infinity only become apparent at high winds as LQG was much faster under small disturbances \\ \hline
13 & [PERSON] et al. [15] & Sliding Mode Control & Nonlinear & Good rejection of Gaussian noise when used with a sliding mode observer & Control chattering due to high frequency switching \\ \hline
14 & [PERSON] et al. [16] & Backstepping & Nonlinear & If designed in compliance with the Lyapunov stability theorem, stability is guaranteed & Explosion in complexity seen when used on systems with many state. No tests for noise rejection \\ \hline
15 & [PERSON] et al. [17] & Reinforcement Learning & AI & Performs well in real applications, highly stable in harsh conditions (RUAV thorownin the air) & High computational cost \\ \hline
16 & [PERSON] et al. [18] & Reinforcement Learning & AI & Model-based method using linear regression instead of Neural Networks, thus, fewer computations & Limited success for a simple step and hover motion \\ \hline
17 & [PERSON] et al. [19] & Fuzzy Logic control & Hybrid & Impressive Reference tracking as learning rate is updated online & Controller was not tested with any sensor or actuator noise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: State of the art Control strategies for stability of RUAVs. The main limitation associated with using an optimal controller for real applications is the uncertainty associated with using the trial-and-error method (TAE) for tuning the controller. TAE is a knowledge-based approach used by [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] et al. [7] and [PERSON] et al. [8] to select controller parameters, a solid understanding of the system dynamics is required for good performance here.