Event-Based Adaptive Consensus Control of Multiple Flexible Manipulator Systems With Input-Output Constraints

Area of Science:

• Robotics and Control Systems
• Networked Systems Engineering
• Applied Mathematics

Background:

• Flexible manipulators present challenges in vibration suppression and achieving consensus in networked environments.
• System uncertainties, backlash, input quantization, and time-varying disturbances complicate control design.
• Existing barrier Lyapunov function methods can impose conservative constraints on system states.

Purpose of the Study:

• To develop a robust control strategy for vibration suppression and bipartite consensus in networked flexible manipulators.
• To address input-output constraints and system uncertainties using advanced control techniques.
• To relax state constraint conservatism compared to traditional methods.

Main Methods:

• Utilizing neural networks (NNs) to approximate system uncertainties.
• Employing an integral barrier Lyapunov function (IBLF) for time-varying state constraints.
• Implementing a hysteresis quantizer and adaptive techniques for disturbance estimation and handling.
• Designing an event-triggered mechanism with a relative threshold strategy for controller updates.

Main Results:

• Successfully achieved vibration suppression and bipartite consensus for networked flexible manipulators.
• Demonstrated effective handling of system uncertainties and disturbances.
• The IBLF approach relaxed conservative state constraints.
• The event-triggered mechanism reduced controller update frequency, saving network resources.

Conclusions:

• The proposed control strategy effectively addresses vibration suppression and bipartite consensus under input-output constraints.
• The integration of NNs, IBLF, and an event-triggered mechanism offers a robust and efficient solution.
• This work advances control methodologies for complex networked robotic systems.