Robust control of uncertain feedback linearizable systems based on adaptive disturbance estimation.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Adaptive Control Theory

Background:

• Feedback linearizable systems often face challenges due to external perturbations and unmodeled dynamics.
• Accurate state estimation is crucial for effective control of uncertain systems.
• Existing methods may struggle with limited plant knowledge and output signal information.

Purpose of the Study:

• To develop an adaptive disturbance estimation-based control strategy for uncertain feedback linearizable systems.
• To address the trajectory tracking problem in systems with external perturbations and non-modeled dynamics.
• To design a state estimator capable of handling limited system information.

Main Methods:

• An adaptive scheme was proposed for state estimation of uncertain dynamics.
• A parametric identifier was implemented within an observer to estimate time-varying parameters of uncertain sections.
• An adaptive gain adjusted observer trajectories to ensure boundedness of the estimation error.
• A feedback controller actively compensated for estimated perturbations.

Main Results:

• The proposed method achieved adequate estimation of uncertainties/perturbations, validated by mean square error metrics.
• The state estimator successfully estimated system states and uncertain dynamics despite limited plant knowledge.
• The observer-based control demonstrated effective rejection of system perturbations.
• Numerical examples confirmed the observer-based control performance.

Conclusions:

• The adaptive disturbance estimation-based control effectively handles uncertain systems with external perturbations and non-modeled dynamics.
• The proposed state estimator and adaptive compensation scheme ensure accurate trajectory tracking.
• This approach offers a robust solution for controlling complex, uncertain dynamic systems.