Abstract
A model predictive control (MPC) strategy based on a novel Bayesian-Gaussian neural network (BGNN) model was proposed for the controller design of hydraulic turbine in this paper. The BGNN was used to learn the nonlinear dynamic model of controlled hydraulic turbine on-line as the predictive model for the design of MPC controller. Experiments show that the proposed nonlinear MPC strategy based on BGNN performs much better than the conventional PID controller.
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Liu, Y., Fang, Y. (2008). Predictive Control Strategy of Hydraulic Turbine Turning System Based on BGNN Neural Network. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_37
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DOI: https://doi.org/10.1007/978-3-540-92137-0_37
Publisher Name: Springer, Berlin, Heidelberg
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