基于在线支持向量回归的非线性模型预测控制方法

Online support vector regression-based nonlinear model predictive control

针对非线性模型预测控制中离线模型难以适应非线性对象实时变化的缺点,提出一种基于在线支持向量回归的非线性模型预测控制方法.该方法通过在线支持向量回归离线训练与在线学习相结合的方式,建立具有在线校正特性的预测模型,同时采用最速下降原理滚动优化非线性模型预测控制的目标函数,求得多步控制量.通过对非线性对象的控制结果表明,所提出方法有效且具有良好的自适应性.

For off-line model hardly adapt to the real-time change of nonlinear object in nonlinear model predictive control （NMPC）, an NMPC based on online support vector regression（OSVR） is developed. This algorithm utilizes OSVR off-line training and on-line learning to built nonlinear dynamical model with on-line adjusted capability. Furthermore, the multi-step control rules are obtained by the rolling optimization of gradient decent rule. Simulation results for a nonlinear system show the good adaptability and effectiveness of the proposed algorithm.