David Cheng

Papers

Total Citations

H-Index

About

David Cheng’s research centers on intelligent robotic control systems, with a particular emphasis on mobile robot dynamics and neural network optimization. His most-cited work, “Mobile Robot Dynamic Model Controlling using Wavelet Network,” introduces a novel control framework that integrates wavelet neural networks with particle swarm optimization (PSO) algorithms. This approach systematically refines the controller’s architecture, selecting the most effective network structure from multiple tested configurations to enhance robotic motion precision and adaptability. While his citation count remains modest, Cheng’s contributions are foundational in bridging wavelet theory and evolutionary computation for real-time robotic applications. His work demonstrates a rigorous methodology for optimizing neural network controllers, offering a scalable solution for dynamic environments. Cheng’s research is particularly valuable for students and engineers exploring hybrid AI-driven control strategies, as it provides a clear, step-by-step framework for integrating adaptive algorithms into robotic systems. His focus on structural optimization and algorithmic synergy marks him as a thoughtful contributor to the field of intelligent robotics.