Adaptive quantized finite-time fault-tolerant control for uncertain multi-input multi-output systems and its...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Fuzzy Logic Applications

Background:

• Multiple-input multiple-output (MIMO) nonlinear systems face challenges from actuator faults and input quantization.
• Existing control methods often require prior knowledge of control gains or struggle with fault adaptation.

Purpose of the Study:

• To develop a novel state-feedback control strategy for MIMO nonlinear systems with actuator faults and input quantization.
• To improve system response speed and robustness against uncertainties and failures.
• To avoid assumptions about unknown control gains in adaptive control schemes.

Main Methods:

• Utilizing fuzzy logic systems (FLSs) to approximate uncertain virtual control laws.
• Implementing finite-time control for enhanced system response.
• Integrating an adaptive control scheme for partial loss fault gain with input quantization.
• Employing Lyapunov stability analysis for theoretical validation.

Main Results:

• The proposed control scheme ensures tracking error convergence and bounded closed-loop signals in finite time, even with system faults.
• The adaptive strategy successfully estimates unknown fault gain without prior assumptions.
• Simulation results for a quadrotor unmanned aerial vehicle (UAV) attitude system demonstrate the effectiveness of the proposed method.

Conclusions:

• The novel state-feedback control method effectively addresses actuator faults and input quantization in MIMO nonlinear systems.
• The integration of fuzzy logic and finite-time control offers a robust and efficient solution.
• The approach provides a significant advancement in fault-tolerant control for complex dynamic systems like UAVs.