# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control in UAV Development  
#### 5.2.1 Early Years  
The early years of UAV development focused on stability and linear control due to the limitations of computing power and control algorithms. Linear control techniques like PID  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & \multicolumn{2}{c}{**Merits**} & \multicolumn{2}{c}{**Demerits**} \\ \hline \multirow{6}{*}{Feedback linearization control} & \(\bullet\) & Flexible design & \(\bullet\) & Sensitivity to modeling errors \\  & \(\bullet\) & Lie derivatives & \(\bullet\) & Bad for complex MIMO systems \\  & \(\bullet\) & No higher order & \(\bullet\) & Requires all system states \\  & \(\bullet\) & Linear control tools & \(\bullet\) & Unable to handle changes \\  & \(\bullet\) & Guarantees stability & \(\bullet\) & Requires precise model \\  & \(\bullet\) & Reduce the complexity & \(\bullet\) & Computationally intensive \\ \hline \multirow{6}{*}{First-order sliding-mode control} & \(\bullet\) & Insensitive to disturbances & & \\  & \(\bullet\) & Robust to & & \\  & \(\bullet\) & handle nonlinearities & & \\  & \(\bullet\) & Lyapunov function & \(\bullet\) & Chattering effect \\  & \(\bullet\) & Simple and easily tunable & \(\bullet\) & High-frequency noise produces \\  & \(\bullet\) & Helps in removing steady state error if integrated with integral action & \(\bullet\) & \(\bullet\) \\  & \(\bullet\) & Unable to handle & & \\  & \(\bullet\) & parametric changes & & \\  & \(\bullet\) & Requires precise model & & \\ \hline \multirow{6}{*}{Backstepping Control} & \(\bullet\) & Recursive structure & & \begin{tabular}{c} Complex design for highly nonlinear \\ MIMO systems \\ Sensitivity to modeling errors \\ Implementation challenges \\ in practice \\ Require significant tuning \\ and optimization \\ Calculation explosion \\ Produces larger magnitude of \\ control signal \\ Existence of steady-state error \\ \end{tabular} \\ \hline \multirow{6}{*}{Model Predictive Control (MPC)} & \(\bullet\) & Accurate tracking & & \begin{tabular}{c} Requires solving optimization problems at each time step \\ Computationally intensive \\ Requires tuning of several parameters \\ (prediction horizon, control horizon, and weighting factors) \\ \end{tabular} \\  & \(\bullet\) & Perform complex maneuvers & & \
\begin{tabular}{c} Requires solving optimization problems at each time step \\ Computationally intensive \\ Requires tuning of several parameters (prediction horizon, control horizon, and weighting factors) \\ \end{tabular} \\  & \(\bullet\) & Robust to disturbances and uncertainties & &
\begin{tabular}{c} Dependent on the accuracy of the \\ mathematical mode \\ Relies on predicting the future state \\ of the system, which can lead to a \\ delayed response to disturbances \\ Complex control method \\ Without learning, cannot handle \\ mass variations \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Merits and Demerits of Nonlinear Control Strategies.  
(proportional-integral-derivative) control were widely used, as they provided satisfactory performance for simpler UAV models [95].