Type-2 Fuzzy Hybrid Controller Network for Robotic Systems.

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Computational Neuroscience

Background:

• Dynamic control systems require accurate models and defined uncertain bounds, posing significant theoretical and practical challenges.
• Existing control methods often struggle with complex nonlinear dynamics inherent in robotic systems.

Purpose of the Study:

• To propose a novel fuzzy neural network integrated with a robust compensator for intelligent dynamic control.
• To address the challenges of accurate system modeling and uncertain system bounds in dynamic control applications.
• To enhance the tracking performance and robustness of robotic systems.

Main Methods:

• Development of a fuzzy neural network by integrating Type-2 fuzzy cerebellar model articulation controller (CMAC) and brain emotional learning controller (BELC) components.
• Utilizing a Type-2 fuzzy inference system (T2FIS) for input processing and sensory/emotional channels for output generation.
• Employing brain emotional learning rules and Lyapunov functions for adaptive dynamic tuning of the network.

Main Results:

• The proposed system successfully estimated nonlinear equations, mimicking an ideal sliding mode controller.
• Robust tracking of controlled system dynamics was achieved for both a robot manipulator and a mobile robot.
• Comparative analysis showed significant performance improvements over alternative control methods.

Conclusions:

• The novel fuzzy neural network and robust compensator offer a powerful solution for intelligent dynamic control.
• The proposed system demonstrates high efficacy and potential for advanced robotic applications.
• This approach significantly improves upon existing methods for intelligent dynamic control of robotic systems.