Finite-time optimal control for MMCPS via a novel preassigned-time performance approach

Area of Science:

• Robotics and Control Systems
• Mechatronics Engineering
• Applied Physics

Background:

• Macro-micro composite positioning stages (MMCPS) are essential for precision engineering.
• Existing control schemes lack guaranteed finite-time convergence for coupled dynamics.
• Optimizing cooperative control between macro and micro components remains a challenge.

Purpose of the Study:

• To address the finite-time optimal stabilization problem in MMCPS.
• To develop a control strategy with guaranteed convergence time for positioning errors and coupling effects.
• To enhance controller performance for voice coil motor (VCM) propulsion and piezoelectric actuator vibration reduction.

Main Methods:

• Establishing the dynamic model of MMCPS using Newton's second law.
• Implementing a reinforcement learning strategy with actor-critic neural networks.
• Designing a novel preassigned-time performance function for displacement control.

Main Results:

• Achieved finite-time convergence for system errors and coupling effects within a specified range.
• Optimized controller performance ensuring propulsion and vibration reduction forces.
• Guaranteed that displacements are limited to a preassigned area in a preassigned time, reducing vibration amplitude.

Conclusions:

• The developed control algorithm ensures semi-global practical finite-time stability for all MMCPS signals.
• Simulation results validate the feasibility and effectiveness of the proposed control strategy.
• The approach enhances cooperative work between macro and micro components in positioning stages.