Cheng-Hsiung Chiang

About

Cheng-Hsiung Chiang is a leading researcher in intelligent control systems, with a career dedicated to advancing autonomous robotics and adaptive control architectures. His work centers on developing self-exploring and self-adapting systems that can learn and optimize their behavior without human intervention. Chiang’s most influential contribution is the Self-Exploring-Based Intelligent Control System (SEICS), which integrates fuzzy neural networks (FNNs) with performance evaluators and adaptors to create controllers capable of dynamic learning. This foundational work, published in 2003, has garnered 32 citations and established a framework for multi-objective self-exploration processes. He has further extended these concepts to practical applications, such as vision-based coverage navigation for robotic trash collection tasks, demonstrating real-world utility. Chiang’s research also explores the synergy between genetic algorithms, quantum particle swarm optimization, and symbolic rule generation to enhance control system efficiency. With over 80 total citations across his most-cited papers, his impact is evident in the continued relevance of his adaptive control architectures. Notably, his 2002 paper on adaptive control using FNNs and genetic algorithms remains a key reference for researchers designing human-like adaptive behaviors in machines.

Research Focus

Key Achievements

Top Papers

1. 1
   New approach to intelligent control systems with self-exploring process

   32 citations · 2003
2. 2
   Vision-based coverage navigation for robot trash collection task

   12 citations · 2015
3. 3
   An intelligent control system with a multi-objective self-exploration process

   12 citations · 2003
4. 4
   New approach to adaptive control architecture based on fuzzy neural network and genetic algorithm

   7 citations · 2002
5. 5
   A genetic programming based rule generation approach for intelligent control systems

   6 citations · 2010
6. 6
   A symbolic controller based intelligent control system with quantum particle swarm optimization based hybrid genetic algorithm

   5 citations · 2008
7. 7
   An intelligent control system based on multiobjective genetic algorithms and fuzzy neural network

   5 citations · 2003
8. 8
   A symbol-based intelligent control system with self-exploration process

   3 citations · 2007
9. 9
   An improved Quantum-behaved particle swarm optimization algorithm for training fuzzy neural networks

   2 citations · 2013

Key Collaborators

Contact & Links