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Advanced Optimal Tracking Control With Stability Guarantee via Novel Value Learning Formulation.

Ding Wang, Junlong Wu, Mingming Ha

IEEE Transactions on Neural Networks and Learning Systems

|April 4, 2023

Summary

This summary is machine-generated.

This study introduces a novel value function for discrete-time nonlinear systems to solve the optimal tracking control problem (OTCP). New algorithms ensure control policy stability and improve performance, validated by simulations.

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Area of Science:

Control Systems Engineering
Nonlinear Dynamics
Computational Intelligence

Background:

Traditional value functions pose limitations for optimal tracking control problems (OTCP) in discrete-time (DT) nonlinear systems.
Existing methods may lack robustness or convergence guarantees for complex nonlinear dynamics.

Purpose of the Study:

To develop and analyze novel value iteration algorithms for solving the OTCP in DT nonlinear systems.
To introduce a new value function that overcomes limitations of traditional approaches.
To guarantee the stability and performance of the proposed control strategies.

Main Methods:

General Value Iteration (GVI) scheme with analysis of monotonicity and convergence.
Introduction of a novel value function for enhanced OTCP solutions.
Online Value Iteration (VI) algorithms incorporating attraction domains for improved control policies.
Novel approach for analyzing the admissibility of iterative control.

Main Results:

The proposed GVI scheme demonstrates monotonicity and convergence properties.
A novel value function is introduced, addressing limitations of traditional methods.
Improved control policies are achieved using online VI, considering tracking error and reference points.
The stability of the online VI-based control system is mathematically guaranteed.
Simulation examples confirm the effectiveness of the developed methods.

Conclusions:

The novel value function and GVI/online VI algorithms effectively solve the OTCP for DT nonlinear systems.
The proposed methods ensure system stability and offer improved control performance.
The study provides a robust framework for advanced tracking control in nonlinear systems.