Nonsingular Practical Fixed-Time Adaptive Output Feedback Control of MIMO Nonlinear Systems.

Area of Science:

• Control Theory
• Nonlinear Systems
• Adaptive Control

Background:

• Addressing the challenge of controlling nonlinear systems with unmeasured states.
• The need for robust control strategies that guarantee stability within a finite, predetermined time.
• Limitations of existing methods in handling unknown system dynamics and unmeasured states.

Purpose of the Study:

• To develop a nonsingular fixed-time control algorithm for multiple-input multiple-output (MIMO) nonlinear systems with unmeasured states.
• To design a state observer to estimate unmeasured system states.
• To approximate unknown nonlinear dynamics using neural networks (NNs).

Main Methods:

• Designing a state observer to estimate unmeasured states.
• Employing neural networks (NNs) for approximating unknown nonlinear dynamics.
• Combining adaptive backstepping recursive technology and adding power integration technology for control design.
• Introducing a filter to simplify the derivation of the virtual control function.
• Utilizing fixed-time Lyapunov stability theory to prove system stability.

Main Results:

• A novel nonsingular fixed-time adaptive output feedback control algorithm is proposed.
• The state observer successfully estimates unmeasured system states.
• Neural networks effectively approximate unknown nonlinear dynamics.
• The closed-loop system is proven to be practically fixed-time stable.
• All signals in the closed-loop system remain bounded within a fixed time.

Conclusions:

• The proposed control algorithm effectively addresses the nonsingular fixed-time control problem for MIMO nonlinear systems with unmeasured states.
• The integration of state observers, neural networks, and adaptive control techniques provides a robust solution.
• The fixed-time stability of the closed-loop system is rigorously proven, demonstrating practical applicability.