Abstract
This work presents a deep echo state network (DESN) based neuroadaptive control approach for a class of single-input single-output (SISO) uncertain system. In which, a DESN based on multiple reservoirs is applied for approximating the uncertain parts of the control system and the rigorous stability condition under the presented control strategy is analyzed. The availability of the approach is proved by comparison with the control technique using radial basis function neural network (RBFNN) and the control scheme using traditional echo state network (ESN) via numerical simulations, demonstrating that superior tracking performance is achieved by the proposed method.
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References
Li, Y., Tong, S.: Adaptive neural networks decentralized FTC design for nonstrict-feedback nonlinear interconnected large-scale systems against actuator faults. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2541–2554 (2017)
Song, Y., Guo, J., Huang, X.: Smooth neuroadaptive PI tracking control of nonlinear systems with unknown and nonsmooth actuation characteristics. IEEE Trans. Neural Netw. Learn. Syst. 28(9), 2183–2195 (2017)
Yang, C., Wang, X., Cheng, L., Ma, H.: Neural-learning-based telerobot control with guaranteed performance. IEEE Trans. Cybern. 47(10), 3148–3159 (2017)
Esfandiari, K., Abdollahi, F., Talebi, H.: Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks. IEEE Trans. Neural Netw. Learn. Syst. 26(10), 2311–2322 (2015)
Liu, Y., Li, J., Tong, S., Chen, C.: Neural network control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE Trans. Neural Netw. Learn. Syst. 27(7), 1562–1571 (2016)
Song, Y., Zhou, S.: Neuroadaptive control with given performance specifications for MIMO strict-feedback systems under nonsmooth actuation and output constraints. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4414–4425 (2018)
Zhao, K., Song, Y.: Neuroadaptive robotic control under time-varying asymmetric motion constraints: a feasibility-condition-free approach. IEEE Trans. Cybern. 50(1), 15–24 (2020)
Han, H., Zhang, L., Hou, Y., Qiao, J.: Nonlinear model predictive control based on a self-organizing recurrent neural network. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 402–415 (2016)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical report GMD Report 148, German National Research Center for Information Technology (2001)
Han, S., Lee, J.: Precise positioning of nonsmooth dynamic systems using fuzzy wavelet echo state networks and dynamic surface sliding mode control. IEEE Trans. Ind. Electron. 60(11), 5124–5136 (2013)
Han, S., Lee, J.: Fuzzy echo state neural networks and funnel dynamic surface control for prescribed performance of a nonlinear dynamic system. IEEE Trans. Ind. Electron. 61(2), 1099–1112 (2014)
Chen, Q., Shi, L., Na, J., Ren, X., Nan, Y.: Adaptive echo state network control for a class of pure-feedback systems with input and output constraints. Neurocomputing 275, 1370–1382 (2017)
Liu, C., Zhang, H., Luo, Y., Su, H.: Dual heuristic programming for optimal control of continuous-time nonlinear systems using single echo state network. IEEE Trans. Cybern. (2020) https://doi.org/10.1109/TCYB.2020.2984952
Chen, Q., Shi, H., Sun, M.: Echo state network-based backstepping adaptive iterative learning control for strict-feedback systems: an error-tracking approach. IEEE Trans. Cybern. 50(7), 3009–3022 (2020)
Yao, X., Wang, Z., Zhang, H.: Identification method for a class of periodic discrete-time dynamic nonlinear systems based on Sinusoidal ESN. Neurocomputing 275, 1511–1521 (2018)
Wang, Z., Yao, X., Li, T., Zhang, H.: Design of PID controller based on echo state network with time-varying reservoir parameter. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3090812
Hermans, M., Schrauwen, B.: Training and analyzing deep recurrent neural networks. In: Proceedings of the 27th Conference on Neural Information Processing Systems, pp. 190–198 (2013)
Gallicchio, C., Micheli, A.: Deep reservoir computing: a critical analysis. In: Proceedings of the 24th European Symposium on Artificial Neural Networks, pp. 497–502 (2016)
Gallicchio, C., Micheli, A.: Deep echo state network (DeepESN): a brief survey. arXiv preprint arXiv: 1712.04323 (2017)
Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87–99 (2017)
Claudio, G., Alessio, M., Luca, P.: Design of deep echo state networks. Neural Netw. 108, 33–47 (2018)
Kim, T., King, B.R.: Time series prediction using deep echo state networks. Neural Comput. Appl. 32(23), 17769–17787 (2020). https://doi.org/10.1007/s00521-020-04948-x
Long, J., Zhang, S., Li, C.: Evolving deep echo state networks for intelligent fault diagnosis. IEEE Trans. Industr. Inf. 16(7), 4928–4937 (2020)
Song, Z., Wu, K., Shao, J.: Destination prediction using deep echo state network. Neurocomputing 406, 343–353 (2020)
Gallicchio, C., Micheli, A., Pedrelli, L.: Deep echo state networks for diagnosis of Parkinson’s disease. In: Proceedings of the 26th European Symposium on Artificial Neural Networks, pp. 397–402 (2018)
Funahashi, K., Nakamura, Y.: Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 6, 801–806 (1993)
Acknowledgement
This work was supported in part by the Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province (Grant No. TSZW2109) and in part by the Research Foundation of Chongqing University of Science and Technology (Grant No. 182101058).
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Xu, B., Chen, Q. (2022). Deep Echo State Network Based Neuroadaptive Control for Uncertain Systems. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_21
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DOI: https://doi.org/10.1007/978-981-19-6142-7_21
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