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Control Systems, Cruise Control, Linear Quadratic Tracking, Model Predictive Control, Neural Networks, Fuzzy Logic Control.

In our world, technology is adapting to the needs that society strives for in advancements, and control systems play an enormous role in improving technology. Different techniques, ranging from classical to intelligent controllers, have been proposed and applied to enhance and optimize general system performance. For example, in the automotive industry, multiple control systems are implemented into the automotive system to create successful and better-performing vehicles. As part of this research, adaptive cruise control in cars as applied by researchers in the automotive industry is investigated, and re-designed systems for better performance are recommended. As part of the research work, a Proportional-Integral-Derivative controller, Fuzzy Logic controller, Model Predictive Controller, Linear Quadratic Tracking, Neural Network, Neuro- Fuzzy, Neuro-Fuzzy-LQT, and LQT-Neural Network controllers are reviewed and discussed regarding the advantages and disadvantages of each technique. These controllers are applied in various cases where the objectives are to keep a desired speed and avoid a collision. The system performances are compared, analyzed, and discussed based on the simulation results. As a result, the Linear Quadratic Tracking combined with a Neuro-Fuzzy controller outperforms the other controllers in the objectives assigned.