e-journal

Adaptive Dynamic Programming for Optimal Tracking Control of Unknown Nonlinear Systems With Application to Coal Gasification

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In this paper, we establish a new data-based iterative optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach
and apply the developed control scheme to solve a coal gasification optimal tracking control problem. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process, coal quality and reference control, respectively, where the mathematical model of the system is unnecessary.
The approximation errors from neural network construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum optimal control problem. A new iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the performance index function converges
to a finite neighborhood of the optimal performance index function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present
method.
Note to Practitioners—Dynamic programming is a useful technique for solving optimal control problems. However, in many cases, it is computationally difficult to apply it due to the backward-in-time calculation or the “curse of dimensionality.” ADP is an effective tool for solving optimal control problems forward-in-time. For most ADP algorithms, the accurate system model, the accurate iterative control and the accurate iterative performance index function are required to obtain the optimal
control law. These iterative ADP algorithms can be called “accurate iterative ADP algorithms.” For many real-world control systems, such as coal gasification systems, the system model is very difficult to construct. The optimal control and optimal performance index function cannot analytically be obtained. These make the accurate iterative ADP algorithms difficult to apply in real-world industrial systems. In this paper, based on the system data, NNs are used to overcome these difficulties, where the approximation errors and control disturbance are both considered. System transformation is introduced that transforms the tracking control system into a two-person zero-sum control system. Iterative
ADP algorithm with iteration errors is then established to obtain the optimal control scheme, where the convergence proof is developed.
Index Terms—Adaptive dynamic programming, coal gasification, data-based control, finite approximation errors, neural networks, optimal tracking control.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 4, OCTOBER 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 4, OCTOBER 2014

Subjek

TEKNIK

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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