Minimum-Entropy Optimal Control of Electromechanical Linkages for Energy Harvesting

Area of Science:

• Thermodynamics
• Control Systems Engineering
• Robotics

Background:

• Optimal mechanical-electrical power conversion is crucial for energy harvesting systems.
• Existing methods often struggle with variable or unknown system dynamics and external forces.
• Thermodynamic principles offer a framework for improving energy conversion efficiency.

Purpose of the Study:

• To develop a novel control strategy for optimal mechanical-electrical power conversion in rigid linkages.
• To formulate an optimal control problem using a generalized entropy generation cost function.
• To design a controller that is robust to unknown excitation characteristics and mechanical dynamics.

Main Methods:

• Adoption of a novel cost function based on generalized Second Law of Thermodynamics principles.
• Formulation of an optimal control problem leading to a decoupled velocity feedback controller.
• Derivation of suboptimal gains independent of excitation and mechanical dynamics, ensuring closed-loop stability.
• Validation through Monte Carlo simulations comparing the proposed controller against a maximum-efficiency controller.

Main Results:

• The proposed controller achieves closed-loop stability.
• Suboptimal gains are independent of excitation characteristics and mechanical subsystem dynamics.
• Monte Carlo simulations demonstrate the controller's effectiveness under random, periodic forcing.
• The novel controller shows a higher statistical expectation for average harvested power compared to maximum-efficiency control.

Conclusions:

• The developed controller provides a simple yet effective method for optimal mechanical-electrical power conversion.
• The approach offers improved energy harvesting performance in systems with uncertain dynamics and external disturbances.
• Generalized thermodynamic principles can be successfully applied to design advanced control systems for energy efficiency.