Reduced-order improved generalized proportional integral observer based tracking control for full-state constrained...

Area of Science:

• Control Systems Engineering
• Robotics and Automation
• Electrical Engineering

Background:

• Proportional-Integral-Derivative (PID) controllers are fundamental but struggle with high-order systems and state constraints.
• Existing adaptive neural network (ANN) methods for state-constrained control lack engineering practicality.
• Active disturbance rejection is key for robust control in complex systems.

Purpose of the Study:

• To develop a novel state-constrained control strategy for systems facing multiple uncertainties.
• To improve upon PID-based disturbance rejection principles for enhanced system performance.
• To ensure system stability and prevent state constraint violations in practical applications.

Main Methods:

• Derivation of active disturbance rejection PID using singular perturbation theory and bandwidth parameterization.
• Proposal of a reduced-order improved generalized proportional integral observer (IGPIO) for state-constrained control.
• Simulation-based verification using a permanent magnet synchronous motor (PMSM) driven electric vehicle (EV) model.

Main Results:

• The proposed IGPIO-based method effectively rejects multiple uncertainties in real-time.
• The controller successfully prevents system state constraint violations.
• Demonstrated robustness and effectiveness in a simulated PMSM-driven EV system.

Conclusions:

• The developed state-constrained control method offers a practical and effective solution for systems with multiple uncertainties.
• The IGPIO observer provides a viable alternative to complex ANN methods for engineering applications.
• The approach enhances the reliability and safety of electric vehicle control systems.