Advanced Control Theory

Adaptive Neural Network Control

Our research center focuses on developing intelligent control algorithms for highly uncertain and nonlinear dynamic systems. We specialize in Adaptive Neural Network Control, which utilizes the powerful function approximation capabilities of neural networks to online identify and compensate for unknown system dynamics, external disturbances, and time-varying environmental changes.

By mathematically guaranteeing the stability and safety of the entire closed-loop system (e.g., via Lyapunov theory), we aim to bridge the gap between advanced theoretical machine learning and robust real-world robotic applications.

Advanced Control for Electric Motors (PMSM)

Our lab extensively researches advanced control algorithms for high-performance electric motor drives, with a particular focus on Permanent Magnet Synchronous Motors (PMSM). We design and optimize sophisticated Field-Oriented Control (FOC) frameworks to achieve highly precise torque and speed regulation under complex and dynamic operating conditions.

To ensure exceptional robustness and system efficiency, we integrate cutting-edge techniques such as Disturbance Observers (DOB) and Event-Triggered Control mechanisms. These approaches accurately estimate and reject external load variations while significantly reducing the computational burden and communication bandwidth, enabling highly reliable and efficient motor drives for advanced robotics and industrial applications.

Intelligent Gain Tuning Techniques

Optimal performance in electric motor drives heavily depends on accurate control gain settings. We develop advanced Intelligent Gain Tuning techniques, focusing on Adaptive Learning Gain (ALG) algorithms for cascaded control systems.

Moving beyond conventional fixed gains, our adaptive update laws dynamically auto-tune control gains in real-time. This capability enables the system to automatically achieve the desired tracking performance across various reference trajectories, while maintaining exceptional robustness against unknown load disturbances and mechanical parameter variations.