Finite-Time Fuzzy Adaptive PPC for Nonstrict-Feedback Nonlinear MIMO Systems.

Area of Science:

• Control Theory
• Nonlinear Systems
• Fuzzy Logic

Background:

• Designing controllers for non-strict feedback Multiple-Input Multiple-Output (MIMO) nonlinear systems presents significant challenges, particularly in achieving prescribed performance within finite time.
• Handling unknown nonlinear functions in these systems often requires sophisticated approximation techniques.

Purpose of the Study:

• To develop a finite-time fuzzy adaptive prescribed performance control (PPC) strategy for non-strict feedback MIMO nonlinear systems.
• To address the issue of unknown nonlinearities and improve control performance while avoiding computational complexity.

Main Methods:

• Utilized fuzzy-logic systems (FLSs) to approximate unknown nonlinear functions.
• Developed a novel dynamic surface control (DSC) method by integrating adaptive backstepping and nonlinear filters.
• Constructed a new Lyapunov function incorporating transform error constraints to ensure prescribed performance.

Main Results:

• The proposed DSC method mitigates computational complexity compared to traditional DSC approaches.
• Demonstrated that the control scheme achieves semiglobal practical finite-time stability (SGPFS) for all system signals.
• Simulation results validated the effectiveness and feasibility of the proposed control strategy.

Conclusions:

• The presented fuzzy adaptive PPC approach offers a robust solution for controlling complex nonlinear MIMO systems in finite time.
• The novel DSC method enhances control performance and stability guarantees.
• The study confirms the practical applicability of the developed control scheme through simulations.