Adaptive NN controller design for a class of nonlinear MIMO discrete-time systems

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Controlling discrete-time multiple-input multiple-output (MIMO) nonlinear systems with dead-zone inputs presents significant challenges due to system complexity, unknown functions, and external disturbances.
• The noncausal problem inherent in discrete-time systems further complicates control design.

Purpose of the Study:

• To develop an adaptive neural network tracking control strategy for discrete-time MIMO nonlinear systems incorporating dead-zone inputs.
• To address and overcome the control difficulties arising from system complexity, unknown dynamics, and the noncausal nature of discrete-time systems.

Main Methods:

• Coordinate transformations were employed to convert the complex discrete-time systems into a form amenable to backstepping design.
• Radial basis function neural networks (NNs) were utilized to approximate unknown system functions.
• Adaptation laws and controllers were designed based on the transformed system dynamics, employing the Lyapunov stability method.

Main Results:

• The proposed adaptive control algorithm ensures semiglobally uniformly ultimately boundedness of all system signals.
• Tracking errors were demonstrated to converge to a bounded compact set, indicating effective control performance.
• Simulation examples validated the algorithm's effectiveness and showed favorable comparisons with existing control approaches.

Conclusions:

• The developed adaptive neural network control is effective for MIMO nonlinear discrete-time systems with dead-zone inputs.
• The method successfully handles unknown dynamics, external disturbances, and the noncausal problem, ensuring robust stability and accurate tracking.
• This approach offers a viable solution for complex control applications requiring high precision and stability.