Data-Driven Adaptive Tracking Control for Nonlinear New Quality Productive Forces Systems with Input Constraints

Area of Science:

• Economics
• Control Theory
• Data Science

Background:

• New quality productive forces systems face challenges like nonlinearity and policy constraints.
• Existing models often lack the adaptability for dynamic socioeconomic environments.

Purpose of the Study:

• To develop a data-driven adaptive tracking control framework for complex economic systems.
• To address nonlinearity, model uncertainty, and policy constraints in dynamic systems.

Main Methods:

• Constructed a closed-loop 'theory-data-control' system using provincial panel data.
• Developed a discrete-time model with linear inertia, policy effects, and nonlinear compensation.
• Employed dual machine learning for parameter identification and an adaptive tracking controller with projection.

Main Results:

• Achieved ultimate convergence and boundedness of tracking error based on Lyapunov stability theory.
• Reduced mean absolute error by approximately 30.8% compared to baseline methods.
• Demonstrated enhanced tracking performance and smoother control signals through simulations.

Conclusions:

• The proposed framework offers a rigorous and feasible pathway for precise regulation of complex socioeconomic systems.
• Parameter adaptation and nonlinear compensation are vital for improving control effectiveness.
• Provides an interdisciplinary methodological reference for data-driven closed-loop management.