Adaptive Model Predictive Control Of A Hybrid Motorboat Using Self-organizing GAP-RBF Neural Network And GA Algorithm

摘要：

The paper presents a novel adaptive neural-network based nonlinear model predictive control (NMPC) methodology for hybrid systems with mixed inputs. For this purpose an online self-organizing growing and pruning redial basis function (GAP-RBF) neural network is employed to identify the hybrid system using the unscented kalman filter (IIKF) learning algorithm. A receding horizon adaptive NMPC is then devised based on the identified GAP-RBF neural network model. The resulting nonlinear optimization problem is solved by a genetic algorithm (GA). The performance of the proposed adaptive model predictive control methodology is illustrated on a motorboat simulation case study.

关键词： Hybrid Systems Motorboat Case Study Model Predictive Control Online Identification GAP-RBF Neural Network GA optimization

作者: Karim Salahshoor Ehsan Safari Mohammad Foad Samadi

作者单位: Department of Automation and Instrumentation Petroleum University of Technology Tehran, Iran

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 588-592

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）