Research Directions

Our core research areas and theoretical frameworks
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Reinforcement Learning/Adaptive Optimization

Many adaptive and learning control problems can be formulated as optimization problems aimed at minimizing objective functions related to system performance.
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Self-Organizing Systems

Nature provides us with several examples of collective systems or multi-agent systems that can achieve desired goals through appropriate strategies that promote self-organization.
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Adaptive Networked Control

With the advancement of wireless networks and Internet of Things (IoT), many control systems can be implemented through networked control.
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Reconfigurable Systems

Reconfigurable systems are intelligent systems that embody logic-based adaptive mechanisms.

Machine Learning

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn and gradually improve accuracy.

Data-Driven Control/Unstructured Adaptation

Data-driven control includes several control theories and methods where controllers are designed directly using online or offline input/output (I/O) data of the controlled system or knowledge obtained from data processing.

Research Showcase

Visual demonstrations of our research applications
Fixed-wing UAV Systems

Advanced fixed-wing UAV platforms and control technology research

UAV Guidance Laws

High-precision UAV guidance and trajectory planning algorithms

Robotics System Control

Intelligent robotics system modeling and cooperative control