Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques
✍ Scribed by Tomás Martínez-Marín; Pedro J. Zufiria
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 128 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0890-6327
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✦ Synopsis
In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward arti"cial neural networks (ANNs), which have proved to be universal and e$cient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions "ts very well with ANNs characteristics. Here, we propose several control architectures which combine, in a di!erent manner, supervised neural networks and CM control algorithms. On the one hand, di!erent CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and uni"ed manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.