Recent research:
The author’s work spans AI-driven optimization, machine learning-inspired methods, and control engineering for automation. Across publications, the core focus converges on integrating advanced optimization and adaptive strategies with real-world control systems and educational tooling. A recurring thread is the use of multi-objective optimization and fractional/order-aware approaches to model, tune, and enhance performance in AI, neural networks, adaptive filtering, and computer-vision–driven tasks, alongside robust controller design for pneumatic and air-pressure systems. Methodologies include evolutionary algorithms, Pareto-front analysis, fractional calculus in optimization, Hammerstein–Wiener nonlinear identification, MPC, fuzzy logic, PID variants, and LabView/MATLAB-driven prototyping. The work emphasizes practical deployment (PID, MPC, fuzzy controllers), interpretability of trade-offs through Pareto analysis, and didactic platforms for hands-on learning in control theory. Impact centers on delivering structured design frameworks for modeling and controlling nonlinear, high-variance systems, enabling improved convergence, robustness, and educational value, while acknowledging computational overhead and convergence considerations in fractional methods.

Key Topics:

• Control Systems, Automation & Industrial Control (SCADA/PLC)
• Optimization, Operations Research & Decision Support
• Education, Pedagogy, Teacher Training & Inclusion
• Artificial Intelligence, Machine Learning & Data Science
• Computer Vision, Medical Imaging & Signal Processing