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Neural dynamic optimization for control systems.II. Theory.

C Y Seong¹, B Widrow

¹Dept. of Electr. Eng., Stanford Univ., CA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

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Neural dynamic optimization (NDO) offers optimal feedback control for complex systems by using neural networks to simplify computations. This method addresses limitations of traditional dynamic programming approaches.

Area of Science:

Control Theory
Artificial Intelligence
Nonlinear Systems

Background:

Classical optimal control methods like dynamic programming (DP) face computational and storage challenges.
Existing methods struggle with the complexity of nonlinear multi-input-multi-output (MIMO) systems.

Purpose of the Study:

Introduce Neural Dynamic Optimization (NDO) as a novel approach to optimal feedback control.
Address the computational and storage limitations of traditional methods for nonlinear MIMO systems.

Main Methods:

Develop NDO, a method leveraging neural networks to approximate optimal feedback solutions.
Utilize the theoretical foundation of dynamic programming (DP) to justify the existence of NDO solutions.

Main Results:

Related Experiment Videos

NDO effectively approximates optimal feedback control solutions for nonlinear MIMO systems.
The NDO method significantly reduces computational and storage complexities compared to classical DP.

Conclusions:

NDO provides a viable and efficient alternative for optimal feedback control in complex systems.
This theoretical framework lays the groundwork for practical applications demonstrated in companion papers.