# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Lateral Control of Autonomous Vehicles  
#### 2.1.1 Lateral Model-Based Control Techniques  
In the environment for a lane-change maneuver, similar to vehicle dynamics, a vehicle's presence in the current and adjacent lane is evaluated using the strategic level. Minimizing overall braking induced by lane changes (MOBIL) is a model of strategic level that minimizes the overall lane changes induced by braking. This model can deduct the rules of lane changing for mandatory and optional lane changes in different following models of a car [9]. The challenges of lane keeping, lane changing, safety, and achieving the desired lateral position are dealt with in lateral control [10; 11]. The complicated lateral dynamics make the lateral control of autonomous vehicles a challenging task. There are several studies in the literature that designed different control methodologies for the lateral dynamics of autonomous vehicles, involving proportional integral derivative (PID) [12], backstepping [13; 14], LQR [15], and feedback linearization [16]; sliding mode control (SMC) and backstepping control were designed in [17], a higher sliding mode control technique was developed with  
Figure 1: Key technologies for an autonomous driving. experiment validation [18], gain scheduling was proposed and compared with LQR and MPC approaches [19], and fuzzy logic was also tested [20; 21]. To improve the transient performance and ensure the normal path-tracking maneuver, a novel approach based on a fuzzy-observer-based composite nonlinear feedback (CNF) controller was proposed [21]. This controller was designed to effectively deal with system disturbances and uncertainties that may affect the vehicle's performance. By including a fuzzy observer, the CNF controller is able to estimate the vehicle's lateral states accurately. The proposed technique was formulated using linear matrix inequalities (LMIs) to implement an observer-based controller aimed at enhancing the tracking performance under challenging conditions such as input saturation and disturbances in GPS-denied environments. To validate the effectiveness of the approach, high-fidelity simulations were conducted using CarSim-Matlab/Simulink, and the results demonstrate the validity of the proposed methodology. A novel Lyapunov-based robust control for lateral control of an autonomous vehicle utilizing meta-heuristic optimization algorithm is presented in [17]. First, a double-lane-change path was developed utilizing a fifth-degree polynomial (quantic) function and dynamic constraints. The double-lane-change maneuver was designed using a lane-changing path-planning strategy. Then, a two-degree-of-freedom vehicle bicycle model was used to extract the position and orientation errors. Integration of backstepping control and sliding mode control (SMC) was applied for steering control. The overall stability of the proposed control was analytically demonstrated using the Lyapunov stability theorem. Additionally, a particle swarm optimization algorithm was proposed to determine the optimal parameters of the combined controller. Simulation studies were carried out using CarSim software and Matlab/Simulink, and the proposed controller showed superior performance compared to the backstepping controller in low road friction scenarios. A 3 (DOF) nonlinear vehicle model was developed in [22], which accounts for yaw, lateral, and roll motions, and chaotic behavior was then simulated using the Lyapunov exponent method. To control this chaotic behavior, a sliding mode variable structure control (SM-VSC) was designed. To further improve lateral vehicle stability and reduce chattering under maximum operating conditions, a fuzzy control technique was used to realize an adaptive power-reaching law. The SM-VSC system performance was simulated using Matlab/Simulink. The results of the simulation showed that the adaptive-reaching SM-VSC control approach was effective in quelling the chaotic phase of the vehicle's lateral motion.