Neural-Network-Based Adaptive Backstepping Control With Application to Spacecraft Attitude Regulation.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Real-world nonlinear systems often exhibit model uncertainties, actuator faults, and external disturbances.
• Existing control methods may require restrictive assumptions, such as norm-bounded uncertainties, limiting their applicability.
• Accurate modeling and control of such complex systems are crucial for reliable operation.

Purpose of the Study:

• To develop a robust neural-network-based adaptive control strategy for continuous-time nonlinear systems.
• To address challenges posed by unknown nonlinearities, actuator faults, and external disturbances without requiring norm-bounded assumptions.
• To ensure the asymptotic stability of the closed-loop system under these adverse conditions.

Main Methods:

• An indirect adaptive backstepping control strategy is employed.
• Adaptive neural networks are utilized to approximate unknown system nonlinearities.
• Online adaptive updating laws are designed to estimate actuator fault effectiveness and disturbance bounds.
• The control law is developed to guarantee asymptotic stability.

Main Results:

• The proposed control strategy effectively handles unknown nonlinear functions, actuator faults, and external disturbances.
• The developed adaptive backstepping control law ensures asymptotic stability of the fault closed-loop system.
• The approach does not require the system uncertainties to be norm-bounded, offering broader applicability.
• Online estimation of fault effects and disturbances is achieved.

Conclusions:

• The neural-network-based adaptive control approach provides a robust solution for stabilizing complex nonlinear systems.
• The method demonstrates significant potential for practical applications, as evidenced by the spacecraft attitude regulation example.
• This work advances the field of adaptive control by accommodating more general system uncertainties and faults.