# 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
The simulation results and pros and cons of each technique were presented in detail, with nonlinear model predictive control (NMPC) being the most appropriate technique for highway driving. The article concluded that geometric control techniques (i.e., PPC and Stanley) are not suitable for highway driving due to their low performance at higher speeds, while robust control techniques (i.e., SMC) have lower-quality performance for external disturbances and may cause discomfort to passengers due to chattering. The optimization-based control techniques, such as NMPC and LQR, have been shown to achieve minimum lateral and orientation errors with disturbance compared to the other control techniques. In uncertain environments, the stability and safety of the autonomous vehicles are important prospects. The operating range of the vehicles can be unsafe due to the nonlinear tire-terrain dynamics and saturation. The states of the system should be restricted within certain bounds to solve this problem. A multi-input multi-output (MIMO) model reference adaptive control (MRAC) approach was designed in [26]. The purpose was to improve vehicle stability by acting as an advanced driver-assistance system (ADAS). This approach enhances the yaw and handling stability of the vehicle's lateral dynamics. A nonlinear integrated adaptive control strategy was proposed, using a constraint optimization algorithm. The efficiency of the proposed control technique was compared with a linear time-varying MRAC and a nonlinear integrated adaptive controller. Simulation results for the double-lane-change (DLC) and  
Figure 2: Block diagram of lateral control. J-turn maneuvers at low tire-road friction coefficients and high speeds demonstrated the superiority of the proposed controller over the conventional MRAC in terms of the handling of sideslip limitation and yaw rate tracking. Despite the existence of several studies that have utilized the barrier function approach in autonomous vehicle research [27; 28], there is currently no significant body of research that addresses the control of lateral dynamics in autonomous vehicles using the barrier function approach in combination with sliding mode control (SMC) in the presence of curvature angle, nonlinear tire-forces, and parametric uncertainties. In addition to addressing the challenges posed by road curvature angle, unknown lateral tire forces, and parametric uncertainties, the sliding mode control (SMC) approach with barrier Lyapunov function implemented in [29] was also designed to keep the system outputs of the autonomous vehicle's lateral dynamics within realistic bounds. This further enhances the effectiveness and practicality of the control approach. The paper [30] introduced a new approach called Type-II zeroing control barrier function (ZCBF), which was designed to ensure both the robustness and forward invariance of a constraint set. Unlike the original ZCBF formulation, Type-II ZCBF is more general and allows for the accommodation of multiple Type-II ZCBFs, which have non-intersecting boundaries for the constraint set. This property ensures that input constraints are respected. The proposed method was applied to a classical unicycle system. Furthermore, this approach can be extended to handle non-intersecting constraint set boundaries. The authors in [31] present a system that utilized a finite state machine (FSM) to automatically switch between different states based on input from the traffic environment and driver. The system's optimal inputs were calculated using a quadratic program (QP)-based optimization problem. The QP was solved by employing rule-based control strategies that leverage control barrier functions and control Lyapunov functions (CBF-CLF). The system's safety was ensured by using a convex quadratic program to perform high-frequency updates, ensuring that the system operates in a safe and collision-free manner, especially during lane-change maneuvers.