Abstract
The application of sinusoidal periodic search signals into the general extremum seeking algorithm(ESA) results in the “chatter” problem of the output and the switching of the control law and incapability of escaping from the local minima. A novel chaotic annealing recurrent neural network (CARNN) is proposed for ESA to solve those problems in the general ESA and improve the capability of global searching. The paper converts ESA into seeking the global extreme point where the slope of Cost Function is zero, and applies a CARNN to finding the global point and stabilizing the plant at that point. ESA combined with CARNN doesn’t make use of search signals such as sinusoidal periodic signals, which solves those problems in previous ESA and improves the dynamic performance of the ESA system greatly. During the process of optimization, chaotic annealing is realized by decaying the amplitude of the chaos noise and the probability of accepting continuously. The process of optimization was divided into two phases: the coarse search based on chaos and the elaborate search based on RNN. At last, CARNN will stabilize the system to the global extreme point. At the same time, it can be simplified by the proposed method to analyze the stability of ESA. The simulation results of a simplified UAV tight formation flight model and a typical testing function proved the advantages mentioned above.
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References
Blackman, B.F.: Extremum-seeking Regulators. An Exposition of Adaptive Control, pp. 36–50. Macmillan, New York (1962)
Drakunov, S., Ozguner, U., Dix, P., Ashrafi, B.: ABS Control Using Optimum Search via Sliding Mode. IEEE Transactions on Control Systems Technology 3(1), 79–85 (1995)
Krstic, M.: Toward Faster Adaptation in Extremum Seeking Control. In: Proc. of the 1999 IEEE Conference on Decision and Control, Phoenix, AZ, pp. 4766–4771 (1999)
Tan, Y., Wang, B.Y., He, Z.Y.: Neural Networks with Transient Chaos and Time-variant gain and Its Application to Optimization Computations. Acta Electronica Sinica 26(7), 123–127 (1998)
Wang, L., Zheng, D.Z.: A Kind of Chaotic Neural Network Optimization Algorithm Based on Annealing Strategy. Control Theory and Applications 17(1), 139–142 (2000)
Tang, W.S., Wang, J.: A Recurrent Neural Network for Minimum Infinity-Norm Kinematic Control of Redundant Manipulators with an Improved Problem Formulation and Reduced Architecture Complexity. IEEE Transactions on systems, Man and Cybernetics 31(1), 98–105 (2001)
Zuo, B., Hu, Y.A.: Optimizing UAV Close Formation Flight via Extremum Seeking. In: WCICA 2004, vol. 4, pp. 3302–3305 (2004)
Pan, Y., Ozguner, U., Acarman, T.: Stability and Performance Improvement of Extremum Seeking Control with Sliding Mode. Control 76, 968–985 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hu, Ya., Zuo, B., Li, J. (2006). A Novel Chaotic Annealing Recurrent Neural Network for Multi-parameters Extremum Seeking Algorithm. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_112
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DOI: https://doi.org/10.1007/11893257_112
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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