Soft Computing-Based Design and Control for Vehicle Health Monitoring

The study of Vehicle Health monitoring plays very important role in deciding the possibility of completing journey successfully. Already work has been done on Pre Trip plan assistance with vehicle Health Monitoring with simple and Hybrid Fuzzy controllers. The conventional Fuzzy logic controllers are knowledge-based systems, incorporating human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions. They are limited in application, as its logic rules and membership functions have to be preset with expert knowledge and have a great influence over the performance of the fuzzy logic controller. In the present work, a Genetic Fuzzy system is proposed to promote the learning performance of logic rules. The resultant hybrid system seems to be highly adaptive and trained through a proper performance, hence is much more sophisticated and has a higher degree of adaptive parameters. The work proposes a benefit of Methodology by comparison of calculating safest distance by evaluating Vehicle Health Monitoring using Hybrid Fuzzy, and genetic fuzzy controller.