Data-Driven Point-to-Point Finite-Iteration Learning Control for a Class of Nonlinear Systems With Output Saturation.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Machine Learning

Background:

• Controlling unknown nonlinear discrete-time systems with output saturation presents significant challenges.
• Existing control methods often require accurate system models, which are unavailable in many real-world applications.

Purpose of the Study:

• To develop a data-driven control algorithm for precise point-to-point (PTP) tracking in unknown nonlinear discrete-time systems with output saturation.
• To achieve bounded tracking errors within a finite number of iterations without prior system knowledge.

Main Methods:

• A novel data-driven Finite Iteration Learning Control (FILC) algorithm is proposed.
• The algorithm utilizes recursive evolution in the time domain to derive system input-output relationships.
• Iterative domain dynamic linearization techniques establish a dynamic data-driven model.
• A finite-iteration learning strategy based on fractional error power ensures convergence.

Main Results:

• The proposed FILC algorithm effectively addresses PTP tracking control for unknown nonlinear discrete-time systems.
• Bounded tracking errors are achieved within a limited number of iterations.
• Theoretical proof confirms the finite-iteration convergence of the algorithm.
• Simulation results validate the practical effectiveness of the proposed method.

Conclusions:

• The data-driven FILC approach offers a robust solution for complex control problems where system models are unknown.
• This method provides an efficient and theoretically sound way to achieve precise tracking control under output saturation.
• The study demonstrates the potential of data-driven techniques in advancing control engineering for nonlinear systems.