Currently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system by using the genetic algorithm (GA) to support the vehicle in following the predefined path accurately, minimizing the sideslip, and stabilizing the vehicle’s yaw rate. The dynamic system model of the vehicle is represented based on yaw rate angle, lateral speed, and vehicle sideslip angle as the variables of the state space model, with the steering angle as an input parameter. Using the GA to optimize the LQR control by tuning the weighting of the Q and R matrices led to enhancing the system response and minimizing deviation errors via a proposed cost function of GA. The simulation results were obtained using MATLAB/Simulink 2024a, with a representation of a predefined path as a Gaussian path. Under external and internal disturbances, such as road conditions, lateral wind, and actuator delay, the model demonstrates improved tracking performance and reduced sideslip angle and lateral acceleration by adjusting the longitudinal vehicle speed. This work highlights the effectiveness of robust control in addressing path planning, driving stability, and safety in autonomous vehicle systems.

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero.

The fuzzy logic controller (FLC) is considered one of the most familiar control techniques for dealing with uncertain and nonlinear systems. However, the steady-state error is still a challenge in some applications that need to be handled. To diminish this issue, an integral part has been incorporated with the fuzzy controller. This paper examines three kinds of FLC systems to improve the accuracy of the antenna's azimuth position. The three proposed controllers: basic fuzzy PD logic control system (FPD), fuzzy logic control system with an I controller (FPDI), and fuzzy logic control system plus an I controller (FPD+I) are implemented and compared in case of tracking desired antenna angle, disturbances and uncertainties using MATLAB simulation software. It has been observed that the FPD+I controller part proved effective in reducing steady-state error while delivering the best tracking performance response of the antenna's azimuth position and robustness to disturbances and uncertainties.