Abstract
A new single neuron adaptive Proportional-Integral-Derivative (PID) controller based on Levenberg-Marquardt (LM) algorithm is presented in this paper. This new controller overcomes some drawbacks of the conventional single neuron adaptive PID controllers. There are two kinds of problems in traditional algorithms. Firstly, gradient descent algorithm is a one-order optimization method. Secondly, Newton iterative method costs much computing resource. For the improved controller, LM algorithm is applied, which combines steepest gradient descent and Gauss-Newton method. As a consequence, the convergence speed is increased and the control performance is greatly improved. The simulation results show that the control effect of this novel controller has strong robustness and good self-adaptation.
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References
Xu, B.-C., Wu, J.-Z., Chen, Y.-K.: An Improved Single Neuron Adaptive PID Control Algorithm. In: Fifth International Conference on Natural Computation, pp. 558–562. IEEE Press (2009)
David, W.T., John, J.H.: Simple ‘neural’ optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. J. IEEE Transaction on Circuits System CAS-33, 53–541 (1986)
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. J. Proceedings of the Institution of Electrical Engineers, Control & Science 121(12), 1585–1588 (1974)
Wang, X.-W., Tan, D.-J., Guo, Y.-N., Hao, R.: Expert-PID Control Based on Genetic Algorithm. J. Control and Instruments in Chemical Industry 29(4), 35–37 (2002)
Liu, T.-K., Juang, J.-G.: A Single Neuron PID Control for Twin Rotor MIMO System. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 186–191. IEEE Press, Singapore (2009)
Fang, H., Yu, X.-H.: Design and Simulation of Neuron PID Controller. In: International Conference of Information Technology, Computer Engineering and Management Sciences, pp. 80–82. IEEE Press (2011)
Zuo, J.-M., Pan, C., Shen, C.-J.: The Single Neuron PID Control of PMLSM Based on Quadratic Index. J. Manufacture Information Engineering of China 36(19), 20–23 (2007) (in Chinese)
Tang, X.-J., Huang, T.-L., Liu, X.-Y., Wang, J.: Application of PID with Single-Neuron Adaptive Control in Liquid Level Control. In: Third International Conference on Genetic and Evolutionary Computing, pp. 533–536. IEEE Press (2009)
Wang, X., Shi, H.-R.: Study on the single neuron adaptive PID control with self-adaptive gain. J. Automation and Instruments 5, 51–52 (2005) (in Chinese)
Fu, J., He, H.-B., Zhou, X.-M.: Adaptive Learning and Control for MIMO System Based on Adaptive Dynamic Programming. J. IEEE Transactions on Neural Networks 22(7), 1133–1148 (2011) (in Chinese)
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Hu, TT., Zhuang, YF., Yu, J. (2012). An Improved Single Neuron Adaptive PID Controller Based on Levenberg-Marquardt Algorithm. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_32
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DOI: https://doi.org/10.1007/978-3-642-31561-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31560-2
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