A Novel Exploration-Exploitation-Based Adaptive Law for Intelligent Model-Free Control Approaches

Area of Science:

• Robotics
• Control Theory
• Machine Learning

Background:

• Model-free control requires advanced exploration-exploitation for complex tasks like bipedal robot locomotion.
• Existing methods struggle with unstructured environments and advanced robotic dynamics.

Purpose of the Study:

• To develop a comprehensive exploration-exploitation policy for model-free algorithms.
• To create a model-free adaptive control law addressing saturation and time delays.
• To validate the algorithm's performance on a challenging underactuated manipulator.

Main Methods:

• Constructed a comprehensive exploration-exploitation policy incorporating long-term prediction and control knowledge.
• Derived a model-free adaptive law using model-based solution analogy, accounting for control signal saturation and input delay.
• Implemented the adaptive algorithm in real-time on a fourth-order, coupled, underactuated manipulator.

Main Results:

• The adaptive algorithm explored larger state-action spaces, effectively mitigating the vanishing gradient problem in learning and control.
• Real-time implementation demonstrated optimized learning and control properties.
• Lyapunov stability analysis confirmed the convergence of the adaptive law.

Conclusions:

• The proposed model-free adaptive algorithm enhances exploration-exploitation for robotic control.
• The method successfully addresses practical challenges including input saturation and time delays.
• This approach offers a robust solution for intelligent control in complex robotic systems.