# Evaluation of PID-Based Algorithms for UGVs
## 2 Related Work
### Comparative Approaches to the Use of PID Controller Algorithms in UGVs  
Several studies and reviews are available in the literature addressing UGV navigation methods, whether in structured or unstructured environments. The work conducted by [PERSON] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ON–OFF** & **PID** & **Fuzzy Logic** & **Predictive Control** \\ \hline Simplicity & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ Accuracy & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Computational Cost & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ Stability & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Speed & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Tuning & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and Disadvantages of each type of controller. [PERSON] et al. demonstrates the application of a UGV in unstructured environments, where the A-STAR algorithm was used, enhanced with improvements in the heuristic function to achieve shorter paths [17]. In [18], the authors proposed a method involving a hierarchical adaptive control strategy for UGVs, using a PID to estimate the yaw moment and PSO optimization to handle structural uncertainties, distributing the yaw moment among the motors. [PERSON] et al. present a trajectory-following PID control design for a three-wheeled UGV, addressing lateral directional control, vector-field-based orientation, and sensor fusion using a Kalman filter [19]. In [20], the authors introduced a method based on vector differences and self-adaptive mechanisms for PID tuning, aimed at minimizing human intervention in the adjustment of PID controllers across a diverse and extensive set of problems/tasks. To achieve this, an innovative approach based on the Differential Evolution algorithm with Success-Based Particle Adaptations (DEPA) was employed. This algorithm utilizes a memory mechanism to autonomously and efficiently adapt control parameters. Moreover, modern control techniques also stand out in complex systems such as Markov jump systems. For instance, [PERSON] et al. [21] proposed an approach based on a Proportional-Integral Observer (PIO) and disturbance rejection techniques with IEID, as well as a Smith predictor for systems with input delays and state-dependent nonlinearities. This method employs linear matrix inequalities (LMI) and Lyapunov stability theory to ensure robust output tracking with disturbance rejection. Similarly, [PERSON] and [PERSON] [22] presented a tracking control approach based on observers for Markov jump systems with partially known transition rates, utilizing LMI and stochastic stability theory. This method focuses on reference control and ensuring stability for systems with undefined transitions. Although this work targets specific transition rate conditions, it highlights the importance of observers in improving accuracy and stability in uncertain systems. However, [PERSON] et al. introduced an adaptive trajectory planning framework where a UGV equipped with LIDAR and a camera was used to monitor agricultural terrains. For trajectory planning, they proposed the HSAStar hybrid algorithm (Hybrid SLAM and A Star), which combines SLAM and A* to allow the UGV to continuously monitor the path while mapping [23]. [PERSON] et al. developed a PID controller designed to improve the capability of autonomous cars to perform maneuvers on pre-calculated tracks. They proposed three approaches to design and tune the controller's hyperparameters, using the CTE as input and generating steering commands as output. Simulations on complex tracks, including sharp curves, were used to evaluate the controller's performance at different speeds [24]. In [25], the authors proposed a PID controller to address the trajectory tracking problem in mobile robots with differential kinematics. The developed method consists of a trial-and-error technique with six tuning parameters to allow the robot to follow a desired trajectory.