Neuro-Evolution of Continuous-Time Dynamic Process Controllers
Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.
Aamir, M. On replacing pid controller with ann controller for dc motor position control. arXiv preprint arXiv:1312.0148 (2013).
Eiben, A. E., Smith, J. E., et al. Introduction to evolutionary computing, vol. 53. Springer, 2003.
Harp, S. A., Samad, T., and Guha, A. Designing application-specific neural networks using the genetic algorithm. In NIPS (1989), vol. 2, Citeseer, pp. 447–454.
Harp, S. A., Samad, T., and Guha, A. Towards the genetic synthesis of neural network. In Proceedings of the third international conference on Genetic algorithms (1989), pp. 360–369.
Jalali, S. M. J., Kebria, P. M., Khosravi, A., Saleh, K., Nahavandi, D., and Nahavandi, S. Optimal autonomous driving through deep imitation learning and neuroevolution.
In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (2019), IEEE, pp. 1215–1220.
Kumar, R., Srivastava, S., and Gupta, J. Artificial neural network based pid controller for online control of dynamical systems. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (2016), IEEE, pp. 1–6.
MATLAB. version R2017a. The MathWorks Inc., Natick, Massachusetts, 2017.
Matousek, R., Dobrovsky, L., and Kudela, J. How to start a heuristic? utilizing lower bounds for solving the quadratic assignment problem. International Journal of Industrial Engineering Computations 13, 2 (2022), 151–164.
Michalewicz, Z., and Michalewicz, Z. Genetic algorithms+ data structures= evolution programs. Springer Science & Business Media, 1996.
Montana, D. J., Davis, L., et al. Training feedforward neural networks using genetic algorithms. In IJCAI (1989), vol. 89, pp. 762–767.
Panbude, A., and Sharma, M. Implementation of neural network for pid controller. International Journal of Computer Applications 975 (2015), 8887.
Prados, D. L. New learning algorithm for training multilayered neural networks that uses genetic-algorithm techniques. Electronics Letters 28, 16 (1992), 1560–1561.
Sekaj, I. Evolutionary based controller design. In Evolutionary Computation (SMC) (Editor: Wellington Pinheiro dos Santos) (2009), In-Tech.
Sekaj, I. Control algorithm design based on evolutionary algorithms. In Introduction to Modern Robotics. iC. Press, Hong Kong (2011).
Sekaj, I., C´ıfersk´y, L., and Hvozd´ık, M. Neuro-evolution of mobile robot controller. Mendel Journal 25, 1 (2019), 39–42.
Stanley, K. O., Clune, J., Lehman, J., and Miikkulainen, R. Designing neural networks through neuroevolution. Nature Machine Intelligence 1, 1 (2019), 24–35.
Stanley, K. O., and Miikkulainen, R. Evolving neural networks through augmenting topologies. Evolutionary computation 10, 2 (2002), 99–127.
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