Multilevel adaptive control of nonlinear interconnected systems

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Robotics

Background:

• Controlling nonlinear interconnected systems with unknown parameters presents significant challenges.
• Ignoring interaction terms in strongly coupled systems can lead to performance degradation and instability.
• Existing control methods may not adequately address the complexities of unknown parameters and strong interconnections.

Purpose of the Study:

• To present a novel adaptive backstepping-based multilevel control approach for nonlinear interconnected systems.
• To effectively manage interaction terms and tune controller gains using a genetic algorithm.
• To demonstrate the method's applicability to systems with strong couplings and unknown parameters.

Main Methods:

• A two-level control architecture: a nonlinear controller for interaction neutralization and adaptive controllers for system stabilization.
• Optimal tuning of adaptive controller gains via a genetic algorithm.
• Validation through simulation of uncertain double and triple coupled inverted pendulums.

Main Results:

• The proposed adaptive backstepping multilevel approach effectively controls nonlinear interconnected systems.
• The method demonstrates successful regulation and tracking capabilities even with unknown parameters and strong couplings.
• Simulation results confirm the robustness and efficacy of the control scheme.

Conclusions:

• The adaptive backstepping-based multilevel control is a viable solution for complex nonlinear systems.
• The integration of genetic algorithms for gain tuning enhances control performance.
• This approach offers a promising strategy for systems where interaction terms are critical.