Composite RISE control for vehicle-mounted servo system with unknown modeling uncertainties and unknown time-varying disturbances
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Summary
This summary is machine-generated.This study introduces a new robust control method for vehicle servo systems facing model uncertainty and disturbances. The approach ensures accurate trajectory tracking despite complex system challenges.
Area Of Science
- Control Systems Engineering
- Robotics
- Automotive Engineering
Background
- Vehicle servo systems often face challenges with uncertain models and external disturbances, impacting trajectory tracking accuracy.
- Existing control methods may struggle to robustly handle both system uncertainties and time-varying external factors simultaneously.
Purpose Of The Study
- To propose an effective trajectory tracking control method for vehicle servo systems that addresses system model uncertainty and external time-varying disturbances.
- To enhance the robustness and performance of vehicle servo systems in dynamic and uncertain environments.
Main Methods
- A novel composite robust integral of the sign of the error (RISE) control method is developed.
- Multi-layer neural networks are used to approximate system model uncertainties.
- An extended state observer is employed to estimate system errors and external disturbances for feedforward compensation.
- The RISE controller is implemented as a robust feedback controller, with stability analyzed using Lyapunov theory.
Main Results
- The proposed method effectively approximates system uncertainties using neural networks.
- The extended state observer successfully estimates and compensates for near-errors and external disturbances.
- Experimental results demonstrate excellent performance and robustness of the composite RISE controller.
- The approach successfully addresses uncertainties and disturbances in vehicle servo system trajectory tracking.
Conclusions
- The novel composite RISE control method offers a robust solution for trajectory tracking in vehicle servo systems with model uncertainty and external disturbances.
- The integration of neural networks and extended state observers provides effective feedforward and feedback control.
- The proposed approach significantly improves the reliability and accuracy of vehicle servo systems in challenging operational conditions.
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