A variable-structure gradient RBF network with its application to predictive ship motion control

作者：Yin, Jian-Chuan^*; Wang, Li-Dong; Wang, Ni-Ni

来源：Asian Journal of Control, 2012, 14(3): 716-725.

DOI：10.1002/asjc.343

• Radial basis function network
• sequential learning
• sliding window
• predictive control
• ship motion control

The prediction of dynamic behavior of the nonlinear time-varying process plays an important role in predictive control applications. Although neural network algorithms have been intensively researched in modeling and controlling nonlinear systems in recent years, most of them mainly focused on the static dynamics. In this paper, a variable-structure gradient radial basis RBF) network is implemented for nonlinear real-time model predictive control, which is achieved by the proposed gradient orthogonal model selection (GOMS) algorithm. By learning the gradient message of real-time updated data in a sling window, the structure and the connecting parameters of the network can be adaptively adjusted to adapt to the time-varying dynamics. The proposed algorithm is evaluated with Mackey-Glass chaotic time series prediction. Moreover, the variable structure network achieved by GOMS algorithm is applied as a multi-step predictor in a ship course-tracking control study, results demonstrate the applicability and effectiveness of the proposed GOMS algorithm and the variable-RBF-network based predictive control strategy.

• 出版日期2012-5
• 单位大连海事大学