# 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
Overall, the study demonstrated the usefulness of combining SM-VSC with fuzzy control techniques to control chaotic behavior and enhance the stability of lateral vehicle motion under challenging operating conditions. A novel and efficient control strategy for the path following of autonomous vehicles was proposed in [23]. The control strategy was designed to handle uncertainties and mismatched disturbances in the system. The sliding mode control (SMC) was used in combination with a radial basis function neural network (RBFNN), gain scheduling with fuzzy system, and disturbance observer (DOB). The RBFNN estimates uncertainties, the fuzzy system compensates for changes in system parameters, and DOB estimates mismatched disturbances. The stability of the closed-loop system was guaranteed using the Lyapunov stability theorem. The proposed control was tested for path-following tasks of autonomous vehicles under different road adhesion conditions and at high speeds in hard driving conditions. Simulation results demonstrated that the proposed control is more efficient than other robust strategies such as \(\mathbf{H}_{\infty}\) and the principle of immersion and invariance. The proposed control strategy has potential for use in real-world applications of autonomous vehicle control. A block diagram of the lateral control scheme is depicted in Figure 2.  
A technical survey on the latest research on lateral control techniques applied for autonomous vehicles and the path-tracking task was proposed in [24]. Many concepts of control formulation are demonstrated and discussed, and the challenges and strengths of each control technique are mentioned and compared. Model predictive control (MPC) is one of the best control techniques for the task of path tracking because of its capability to ensure input and state constraints and its challenge is the stability analysis. SMC, \(\mathbf{H}_{\infty}\), and back-stepping control are great control approaches to deal with disturbances, parameter uncertainties, and nonlinearitieson account of complex design techniques and theoretical derivations. LQR, geometry-based, and PID controllers are appropriate for the applications of low-speed and simple models with negligible uncertainties and disturbances. Most model-based controllers except MPC cannot impose constraints on system states, inputs, and outputs. This is a crucial quality for comfort and safety insurance in automated guidance for imposing actuator limits and maintaining stability conditions. Otherwise, model-free controllers are a crucial solution when modeling becomes a challenging task. These techniques are useful in the case of available sufficient data and can guarantee multitasking without being affected by the system nonlinearities. AI-based techniques can be utilized to improve model-based controllers by learning control algorithms and complex models. The drawbacks of these techniques are the difficulty of interpretability and the nature of black box, which cannot ensure some physical properties and stability. Generally, in spite of the great progress in the field of autonomous driving, many problems such as steering systems, unmeasurable parameters, and discontinuous data still continue and need great efforts to be solved. A detailed overview and comparison of different control methods that can be utilized for the purpose of path tracking in autonomous ground vehicles (AGVs), with a focus on car-like autonomous vehicles, was presented in [25]. It covers several control techniques, including feedback linearization (FL), \(\mathbf{H}_{\infty}\) controller, sliding mode controller (SMC), Lyapunov's direct method (LDM), Stanley controller, pure pursuit controller (PPC), neural network (NN) controller, model predictive control (MPC), and linear quadratic regulator (LQR). The performance of each technique was evaluated through a simulation study in urban path tracking.