Neuroadaptive Control With Given Performance Specifications for MIMO Strict-Feedback Systems Under Nonsmooth...

Area of Science:

• Control Systems Engineering
• Robotics
• Nonlinear Dynamics

Background:

• Multi-input multi-output (MIMO) strict-feedback systems present significant control challenges due to nonsmooth actuator nonlinearities, output constraints, and external disturbances.
• Existing control strategies often struggle with simultaneous handling of these complex system characteristics and achieving prescribed performance.

Purpose of the Study:

• To develop a novel neural adaptive control scheme for MIMO strict-feedback systems.
• To address challenges posed by asymmetric nonsmooth actuator characteristics, output constraints, and external disturbances.
• To achieve prescribed performance tracking within a finite time and at a prespecified convergence mode.

Main Methods:

• A novel speed transformation combined with a barrier Lyapunov function is employed.
• Radial basis function neural networks (RBFNNs) are utilized to approximate unknown virtual control gains.
• A matrix factorization technique is introduced to relax constraints on the control gain matrix.

Main Results:

• The proposed control scheme guarantees prescribed performance tracking with specified precision and convergence time.
• The use of RBFNNs effectively handles uncertainties in virtual control gains.
• A matrix factorization technique simplifies the control design by removing restrictive requirements on the control gain matrix.

Conclusions:

• The developed neural adaptive control strategy effectively manages complex system dynamics, including nonlinearities and constraints.
• The approach offers reduced computational complexity and parameter updates through a virtual parameter for lumped uncertainties.
• The control scheme's efficacy is confirmed through rigorous stability analysis and numerical simulations.