Robust Control for the Segway with Unknown Control Coefficient and Model Uncertainties.

Area of Science:

• Robotics
• Control Systems Engineering
• Nonlinear System Dynamics

Background:

• Segway vehicles represent complex nonlinear systems with inherent uncertainties.
• Accurate control design must account for unknown time-varying control coefficients and model uncertainties.

Purpose of the Study:

• To develop a robust control scheme for Segway balancing.
• To address challenges posed by unknown control coefficients and model uncertainties.
• To design a controller that avoids adaptive, neural network, or fuzzy logic compensation.

Main Methods:

• Utilized the Nussbaum gain technique to manage time-varying unknown control coefficients.
• Introduced an auxiliary variable to overcome the underactuated problem.
• Employed prescribed performance control to handle uncertainties without adaptive methods.
• Applied Lyapunov stability theory to guarantee closed-loop system boundedness.

Main Results:

• The proposed controller effectively manages Segway dynamics despite unknown parameters.
• The controller demonstrates robustness against model uncertainties.
• Simulation results validate the effectiveness of the developed control scheme.

Conclusions:

• The robust control strategy ensures stability for Segway systems with uncertainties.
• The controller's simplicity is achieved by avoiding complex compensation techniques.
• The approach offers a viable solution for controlling uncertain nonlinear systems like the Segway.