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Neural dynamic optimization for control systems.III. Applications.

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 a novel approach to optimal feedback control for complex nonlinear systems. This method utilizes neural networks to simplify computations and storage, overcoming limitations of traditional dynamic programming.

Area of Science:

Control Systems Engineering
Artificial Intelligence
Robotics

Background:

Classical optimal control methods like dynamic programming (DP) face significant computational and storage challenges for nonlinear multi-input-multi-output (MIMO) systems.
The need for more efficient and scalable solutions in advanced control applications is critical.

Purpose of the Study:

To introduce Neural Dynamic Optimization (NDO) as a novel method for optimal feedback control.
To demonstrate the efficacy of NDO in addressing the complexities of nonlinear MIMO systems.
To reduce the computational and storage burdens associated with traditional dynamic programming techniques.

Main Methods:

Neural Dynamic Optimization (NDO) leverages neural networks to approximate optimal feedback control solutions.

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The method builds upon the theoretical underpinnings of dynamic programming (DP) while mitigating its practical limitations.

NDO's implementation is validated through simulations and practical applications.

Main Results:

NDO successfully approximates optimal feedback solutions for nonlinear MIMO systems.
The method significantly reduces computational complexity and storage requirements compared to classical DP.
Demonstrated successful application in controlling autonomous vehicles and robot arms.

Conclusions:

Neural Dynamic Optimization (NDO) presents a viable and efficient alternative for optimal feedback control of nonlinear MIMO systems.
NDO offers a promising direction for advancing control engineering by integrating neural networks.
The presented method has broad applicability in areas requiring sophisticated control strategies.