A Novel Approach to Collision Control Technique using Fuzzy Logic Controller for Autonomous Two-Wheeler Application

This work suggests and investigates a two-wheeled self-balancing vehicle with fuzzy proportional-derivative control for a Two-Wheeler Application. A fuzzy PD (Proportional-Derivative) controller is then developed utilizing the vehicle's location and speed as inputs, and a PD controller is used to manage the vehicle's angle as well as its angle rate. Finally, the vehicle's position and speed are provided. It built an ultimate control platform for numerous self-balancing vehicles. This platform handles automobiles effectively and was created because of multiple rounds of parameter debugging. Two goals are explored in this study. The first of these was to propose a fuzzy logic-based controller. This controller will be used to control the vehicle's movement. It will generate the output after receiving information from the fuzzy logic controller about the distance to an item, odometer, theta, ultrasonic sensor, and active sensor. The controller receives input from theta and OBSI. Proposed implementation two guided vehicles using the ANFIS (Adaptive Network-Based Fuzzy Inference System) controller. This variant of ANFIS gives the neural network two inputs, Theta and OBSI, and creates one output, which might be V1 or V2. Finally, after comparing the two controllers, it was discovered that the ANFIS controller surpasses the fuzzy logic controller in terms of displaying the shortest route between the source and the destination of the vehicle.