OSAROBO IGHODARO

This research presents the design and implementation of an integrated fuzzy logic–based decision-making system for autonomous vehicle navigation, focusing on intelligent speed regulation, steering control, and lane keeping. A three-degree-of-freedom (3-DoF) dual-track vehicle dynamics model was developed in MATLAB/Simulink to capture longitudinal, lateral, and yaw behaviors. The control architecture uses a Takagi–Sugeno fuzzy inference system to process speed error, distance error, and yaw deviation, generating throttle and steering actions that emulate human driving intuition. A simulation-based framework was developed for both ego and target vehicles, enabling the evaluation of inter-vehicle distance, trajectory following, and lane stability across straight and curved road sections. Results show that the fuzzy controller reduced longitudinal speed error to below 0.25 m/s, maintained lateral deviation within ±0.12 m on curved paths, and improved yaw rate tracking with a settling time of 1.8 s compared to 3.1 s without fuzzy control. The controller also limited throttle oscillations to less than 5% and sustained a safe inter-vehicle distance with less than 7% deviation from the desired headway. Overall, the research establishes a computationally efficient fuzzy-logic framework suitable for autonomous-vehicle applications, and the findings confirm the controller's robustness and adaptability in a virtual test environment.

This study investigates the impact of Nigeria’s tropical environment on the performance of marine diesel engines, focusing on how climatic factors such as air temperature, humidity, atmospheric pressure, and seawater temperature influence engine efficiency Nigeria’s coastal regions are characterized by consistently high temperatures, intense humidity, and seasonal rainfall variations all of which can affect combustion efficiency, cooling capacity, and fuel consumption in marine engines.

This research presents the design and implementation of an integrated fuzzy logic–based decision-making system for autonomous vehicle navigation, focusing on intelligent speed regulation, steering control, and lane keeping. A three-degree-of-freedom (3-DoF) dual-track vehicle dynamics model was developed in MATLAB/Simulink to capture longitudinal, lateral, and yaw behaviors. The control architecture uses a Takagi–Sugeno fuzzy inference system to process speed error, distance error, and yaw deviation, generating throttle and steering actions that emulate human driving intuition. A simulation-based framework was developed for both ego and target vehicles, enabling the evaluation of inter-vehicle distance, trajectory following, and lane stability across straight and curved road sections. Results show that the fuzzy controller reduced longitudinal speed error to below 0.25 m/s, maintained lateral deviation within ±0.12 m on curved paths, and improved yaw rate tracking with a settling time of 1.8 s compared to 3.1 s without fuzzy control. The controller also limited throttle oscillations to less than 5% and sustained a safe inter-vehicle distance with less than 7% deviation from the desired headway. Overall, the research establishes a computationally efficient fuzzy-logic framework suitable for autonomous-vehicle applications, and the findings confirm the controller's robustness and adaptability in a virtual test environment.