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首页> 外文期刊>Transactions of the Institute of Measurement and Control >Mixed-kernel least square support vector machine predictive control based on improved free search algorithm for nonlinear systems
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Mixed-kernel least square support vector machine predictive control based on improved free search algorithm for nonlinear systems

机译：基于改进的非线性系统的自由搜索算法，混合内核最小二乘支持向量机预测控制

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摘要

Many controlled objects in the actual industrial process are nonlinear systems, and the traditional control theory cannot achieve very good control effect. Based on swarm intelligence optimization algorithm, the nonlinear prediction and predictive control algorithm, this paper put forwards a nonlinear systems predictive control method based on the mixed-kernel least square support vector machine (LSSVM) model and improved free search (IFS) algorithm. The mixed-kernel LSSVM combines the advantages of radial basis function (RBF) and the Polynomial function, which can achieve a better prediction and modeling accuracy. The optimal parameters of the mixed-kernel LSSVM are obtained by IFS algorithm. The proposed predictive control method utilizes mixed-kernel LSSVM to estimate the nonlinear systems model and forecast the output of the system. The output error is reduced through output feedback and error correction. The rolling optimization of control variables are obtained by IFS algorithm. This predictive control method can be used to design effective controllers for nonlinear systems with unknown mathematical models. The stability analysis shows that the control method is asymptotically stable. The simulation experiment of single input and single output, multiple input multiple output and continuous stirred tank reactor nonlinear systems are performed. The validity of the proposed control method is also verified by an actual electric heating furnace system. The simulation and practical experiment results show that the proposed predictive control method has good tracking signal and anti-interference ability.
机译：实际工业过程中的许多受控物体是非线性系统，传统的控制理论无法达到非常良好的控制效果。基于群智能优化算法，非线性预测和预测控制算法，本文提出了基于混合核最小二乘支持向量机（LSSVM）模型的非线性系统预测控制方法，并改进了自由搜索（IFS）算法。混合核LSSVM结合了径向基函数（RBF）和多项式功能的优点，可以实现更好的预测和建模精度。通过IFS算法获得混合核LSSVM的最佳参数。所提出的预测控制方法利用混合核LSSVM来估计非线性系统模型并预测系统的输出。输出误差通过输出反馈和纠错减少。控制变量的滚动优化是通过IFS算法获得的。该预测控制方法可用于为具有未知数学模型的非线性系统设计有效控制器。稳定性分析表明，控制方法是渐近稳定的。单输入和单输出的仿真实验，进行多个输入多输出和连续搅拌罐式反应器非线性系统。所提出的控制方法的有效性也通过实际的电加热炉系统验证。模拟和实验结果表明，所提出的预测控制方法具有良好的跟踪信号和抗干扰能力。

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