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Related Experiment Videos

Revisiting approximate dynamic programming and its convergence.

IEEE Transactions on Cybernetics

|May 22, 2014

Summary

This study introduces a novel adaptive dynamic programming (ADP) method for infinite-horizon optimal control. The proposed approach simplifies policy updates and proves convergence for continuous systems.

Area of Science:

Control Theory
Machine Learning
Optimization

Background:

Infinite-horizon optimal control problems with continuous state and action spaces are challenging.
Approximate/adaptive dynamic programming (ADP) offers a potential solution but requires efficient iteration methods.
Existing ADP methods often necessitate complex numerical solutions for policy updates.

Purpose of the Study:

To investigate a value iteration-based ADP approach for deterministic infinite-horizon optimal control.
To develop a simplified and convergent ADP method for continuous systems.
To enable practical implementation for neurocontroller training and look-up table creation.

Main Methods:

Decomposition of learning iterations into outer and inner loops.

Related Experiment Videos

Novel proof for outer-loop convergence using an analogy to fixed-final-time problems.

Inner loop designed to bypass numerical solution of nonlinear equations for policy updates.

Main Results:

A simple proof for the convergence of outer-loop iterations to the optimal solution.
Sufficient conditions established for the uniqueness and convergence of inner-loop policy updates.
The method is formulated as a learning algorithm for neurocontrollers and look-up tables.

Conclusions:

The proposed ADP method provides a convergent and computationally efficient approach for optimal control.
The novel inner-loop strategy simplifies policy updates in ADP.
The developed algorithm is suitable for real-world applications in nonlinear system control.