A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS₂ memtransistor

Area of Science:

• Materials Science
• Electrical Engineering
• Robotics

Background:

• Traditional proportional-integral-derivative (PID) controllers in drones offer simplicity but lack robustness and optimal gain adjustment capabilities.
• Disturbances in drone operation necessitate adaptive control strategies for improved stability and performance.

Purpose of the Study:

• To develop a self-tuning and energy-efficient PID controller for drone applications.
• To leverage analog-digital hybrid computing with memtransistors for enhanced PID control.

Main Methods:

• Implementation of a custom analog circuit using double-gate SnS2 memtransistors to execute the PID control algorithm.
• Experimental verification of the energy consumption and performance of the hybrid computing-based PID controller.
• Development of a self-tuning algorithm to automatically optimize PID control parameters.

Main Results:

• The proposed hybrid computing-based PID controller demonstrated a 37% reduction in energy consumption compared to traditional controllers.
• The memtransistor's tunable analog conductance states enabled effective reconfiguration of PID controller performance.
• The self-tuning algorithm successfully identified optimal PID control parameters for improved drone operation.

Conclusions:

• The analog-digital hybrid computing platform based on SnS2 memtransistors offers a viable solution for energy-efficient and robust drone control.
• Memtransistor technology enables precise, low-power implementation of adaptive control algorithms.
• This approach paves the way for more autonomous and efficient drone systems.