CIIE Dept presents Seminar on "Self-Tuning Control of Combustion Engine Speed and Pressure Based on Adaptive Particle Swarm Optimization" by Mr Ahtisham Urooj, PhD Student in CIE Dept, on Wednesday, 30-Oct, in Bld. 22 room 134

Abstract:

This paper presents a self-tuning adaptive particle swarm optimization (APSO) proportional integral derivative (PID) controller for the speed control of gasoline engine. The parameters exhibit strong uncertainties in combustion engine speed control; in particular, mass equivalent coefficient and efficiency . Additionally, heat release Q from a unit air mass of gas is greatly influenced by these external conditions even if the air-fuel ratio is controlled to be constant and the ignition time is also well regulated. Strong uncertainty of parameters is the motivation of this research to develop an adaptive-based self-tuning control design scheme. In contrast to the model's structure, the considerable variability in parameters serves as the driving force behind this research endeavor, leading to the development of a control design scheme based on adaptive optimization of self-tuning controller gains. Based on feedback from the combustion engine, an optimal solution can be attained through the optimization mechanism and self-learning abilities of Adaptive Particle Swarm Optimization (APSO). To enhance the efficiency of obtaining superior optimization solutions, we introduce the aggregation degree and evolution speed into APSO. These elements dynamically modify the inertia weight during the practical optimization process. The APSO system adapts PID gains to achieve smooth control of both speed and pressure with minimum cost of 1950 as compared to PSO (3.05x10Exp6) and ACO (1.2x10Exp7).

About the Speaker:

Mr. Ahtisham Urooj is an Engineering Researcher with expertise in control systems, robotics, and biomedical technologies. He is currently pursuing his Ph.D. in Systems and Control Engineering in the Control and Instrumentation Engineering Department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, where he also obtained his M.Sc. in Systems and Control Engineering. Mr. Ahtisham finished his Bachelor’s degree in Mechatronics and Control Engineering at the University of Engineering and Technology, Lahore, Pakistan.

His research interests include intelligent control systems, neural networks, biosensors, and IoT-based robotic control. Mr. Ahtisham’s contributions include developing adaptive control algorithms and self-tuning systems for hybrid electric vehicles, with his work published in notable scientific journals. He is particularly focused on applying his expertise to biomedical engineering, with a strong interest in cell segmentation, robotic surgical systems, and medical automation.