基于SVR的焦炉冷鼓系统预测控制

Predictive control for coke oven blowing cooler system based on support vector regression

针对焦炉冷鼓系统的强非线性,难以建立精确的数学模型等问题,提出了基于支持向量回归机(SVR,support vector regression)的预测控制策略。基于结构风险最小化的SVR可以直接反应非线性模型的特征,采用自适应权值粒子群算法(APSO,adaptive weight particle swarm optimization)对SVR的辨识参数加以优化;预测控制作为控制系统的主体,其滚动式的有限时域优化及反馈校正可有效地克服过程中的不确定性和非线性。在MATLAB仿真平台上,将此控制策略与传统的PID(proportion integration differention)相比较。仿真结果表明该控制策略具有较强的抗干扰性和鲁棒性,可保证冷鼓系统的初冷器前吸力快速、有效地稳定在工艺要求的范围内。

Coke oven blowing cooler system is difficult to establish accurate mathematical model for its strong nonlinearity.To solve the problem,apredictive control strategy based on support vector regression（SVR）is proposed.SVR based on the structural risk minimization can directly reflect model nonlinear characteristics,and the adaptive weight particle swarm optimization（APSO）is utilized to optimize the SVR identification parameters.The rolling of the finite horizon optimization and the feedback correction of can predictive control which is the main body of the control system,overcome the uncertainty and nonlinear process effectively.On the MATLAB simulation platform,this control strategy is compared with the traditional PID（proportion integration differention）.The simulation results show that the control strategy has strong anti-interference and robustness,which ensures the rapid and effective stability of the pre-cooling device in the process.