Abstract
A stable self-learning DRNN (Dynamic Recurrent Neural Network) PID (Proportional + Integral + Derivative) control strategy for multi-point mooring system is proposed in this paper. Multi-point mooring system is complex characteristics by water depth, asymmetrical layout of mooring radius, large geometric nonlinearity of the platform, wave and current excitation. The self-learning DRNN-PID is adapted by DRNN to learn mooring system dynamic change and the gradient descent method to minimize squared positioning error, they guarantee convergence and asymptotically positioning the desired trajectory. The proposed mooring control scheme is applied to “Kantan3” mooring simulation system, the self-learning DRNN-PID mooring control strategy has the advantages of strong identification ability and anti-interference ability, which provide stable controlling performance for the multi-point mooring system.
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This work was supported by the NSFC Projects of China under grant NO 51779136.
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Zhang, G., Lu, R., Chen, M. (2022). A Stable Self-Learning DRNN-PID Control of Multi-Point Mooring System. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_394
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DOI: https://doi.org/10.1007/978-981-15-8155-7_394
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