Distributed Mode-Dependent Event-Triggered Passive Filtering for Flexible Manipulator with Semi-Markov Parameters.

Area of Science:

• Robotics
• Control Systems
• Networked Systems

Background:

• Flexible robotic manipulators require accurate state estimation for effective control.
• Existing filtering methods may struggle with varying loads and unstructured environments.
• Distributed filtering over sensor networks presents communication challenges.

Purpose of the Study:

• To investigate the passive filtering problem for flexible robotic manipulators in a distributed sensor network setting.
• To develop a novel mode-dependent event-triggering mechanism for efficient communication.
• To ensure prescribed passive performance under external disturbances.

Main Methods:

• Utilizing a semi-Markov model to represent manipulators with varying loads.
• Developing a mode-dependent event-triggering mechanism for distributed filter communications.
• Establishing sufficient conditions for passive performance via model transformation and employing convex optimization for filter design.

Main Results:

• Sufficient conditions were established to guarantee prescribed passive performance.
• Mode-dependent filters were successfully designed using convex optimization.
• Simulations demonstrated the effectiveness of the developed filtering algorithm for a single-link manipulator.

Conclusions:

• The proposed distributed filtering approach effectively addresses the passive filtering problem for flexible robotic manipulators.
• The developed mode-dependent event-triggering mechanism improves communication efficiency in networked systems.
• The filtering algorithm shows promise for practical applications involving manipulators with uncertain dynamics.