基于最小二乘支持向量机和混沌优化的非线性预测控制

Nonlinear Predictive Control Based on Least Squares Support Vector Machines and Chaos Optimization

  • 摘要: 针对非线性多入多出(MIMO)系统,提出一种基于最小二乘支持向量机(LSSVM)和混沌优化的预测控制策略.预测模型是预测控制的三要素之一.本文给出了基于混沌优化的Chaos-LSSVM算法,在可行域内反复搜索,从而得到最优的LSSVM算法参数,以及最优的LSSVM模型.在线优化是另一个要素.提出了基于变尺度混沌优化的MSC-MPC(变尺度混沌-模型预测控制)算法,可根据控制误差的大小,决定是否缩小搜索范围,从而迅速收敛到最优解.该算法计算简单,容易实现,避免了同类方法复杂的求导、求逆运算.仿真结果显示:Chaos-LSSVM算法和MSC-MPC算法分别具有良好的建模、控制性能.

     

    Abstract: Aimed at nonlinear multi-input multi-output(MIMO) system,a predictive control strategy based on least squares support vector machines(LSSVMs) and chaos optimization is proposed.Predictive model is one of the three main factors of predictive control.Chaos-LSSVM algorithm based on chaos optimization is presented to obtain optimal LSSVM parameters and the model by iterative search in the feasible region.Online optimization is another essential factor.MSC-MPC(mutative scale chaos-model predictive control) algorithm based on mutative scale chaos optimization is developed,which can decide whether to reduce the search scope according to the size of control error,thus it can converge to the optimal solution rapidly.The algorithm is easy to compute and implement,and avoids the complicated derivation and inversion of other similar methods.The simulation results show that Chaos-LSSVM algorithm and MSC-MPC algorithm have good modeling and control performance,respectively.

     

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