Time-varying gain extended state observer-based adaptive optimal control for disturbed unmanned helicopter

Area of Science:

• Robotics and Control Systems
• Aerospace Engineering
• Artificial Intelligence

Background:

• Unmanned helicopters face challenges with unknown disturbances affecting tracking control.
• Traditional methods like linear Extended State Observers (ESO) can suffer from initial peaking.
• Existing control strategies may not be optimal in terms of energy consumption.

Purpose of the Study:

• To develop a robust adaptive optimal tracking control strategy for disturbed unmanned helicopters.
• To design a novel Time-Varying Gain Extended State Observer (TVGESO) to estimate and compensate for unknown disturbances.
• To improve tracking accuracy and energy efficiency compared to conventional methods.

Main Methods:

• Development of a novel TVGESO to address unknown disturbances and avoid initial peaking.
• Transformation of the optimal tracking control problem into an optimization stabilization problem.
• Application of Adaptive Dynamic Programming (ADP) and neural network techniques for controller design.

Main Results:

• The proposed TVGESO offers a simpler and more rigorous stability analysis than nonlinear ESO.
• The developed anti-disturbance optimal controller achieves tracking tasks with reduced energy consumption compared to backstepping methods.
• Simulation results validate the effectiveness of the proposed control scheme.

Conclusions:

• The novel TVGESO effectively handles unknown disturbances in unmanned helicopter control.
• The integration of TVGESO and ADP provides a robust and energy-efficient optimal tracking control solution.
• This control scheme demonstrates significant improvements in unmanned helicopter performance and disturbance rejection.