基于在线支持向量机和遗传算法的预测控制

针对非线性系统模型预测控制中预测模型容易失配且目标函数难以求解的问题，本文提出一种基于在线支持向量机建模和遗传算法滚动优化的模型预测控制方法。该方法利用在线支持向量机建立被控对象的非线性模型，在线支持向量机是一种迭代学习的支持向量机训练算法，可以进行在线训练，从而实现模型在线自校正；并且通过遗传算法求解目标函数的最优控制量，完成滚动优化。对非线性系统的仿真研究结果表明，该方法有效且具有良好的自适性。

Online support vector machine and genetic algorithm based predictive control

For predictive model mismatch and difficulty in solving nonlinear optimization function of nonlinear system model predictive control, an online support vector machine (OSVM)
modeling and genetic algorithm (GA) rolling optimization based model predictive control is proposed. This algorithm modeled nonlinear object by OSVM, which is an iterative support vector regression learning algorithm and can be used for online training, hence predictive model parameters could be adjusted online through online learning; furthermore, nonlinear optimization function is solved by GA optimization, rolling optimization is realized in system. The nonlinear case simulation results showed system adaptability was improved by the present algorithm.