Comparison of PID, Fuzzy Logic, ANFIS and Model Predictive Controllers for Cruise Control System

Cruise control systems are of great importance in modern cars, not only to save energy but also to enhance driving comfort, avoid road accidents and achieve safety. The speed control system is a multivariable system with high nonlinearity, which makes the use of conventional controllers, such as PID, infeasible under all conditions. This research deals with the design and analysis of an ANFIS-based controller and its comparison with other controllers such as PID, fuzzy logic, and model predictive controllers of the vehicle speed control system. The MATLAB/SIMULINK environment was used to model and simulate the cruise control system to identify the efficiency of the four controllers and show the comparison between them. This work presents the design and performance analysis of an intelligent controller for a vehicle cruise control system, comparing it with PID, ANFIS and model predictive control techniques. Simulation results show that FLC, ANFIS, and MPC controllers have different strengths, with trade-offs between fast response, overshoot, and integral error. The results underscore the importance of selecting the appropriate controller based on specific application requirements, with MPC showing superiority in managing constraints.