网络信息模式下大范围工况运行系统的多模型协同预测控制

Multi-model Coordinated Predictive Control for Plant-wide Operation Systems under Networked Information Mode

针对大范围工况运行的非线性系统,提出了网络信息模式下的多模型协同预测控制算法.系统发生大范围工况切换时,应用间隙度量理论将非线性系统操作空间分为不同工况下的局部子空间,在每个子空间内采用局部线性模型代替非线性模型,设计基于局部模型的无穷时域预测控制器.针对状态变量在不同子空间的转移问题,考虑预测状态在子区间的转移对控制性能的影响,优化N步时域的控制序列代替单一的反馈控制律,降低多模型预测控制中忽略区间转移带来的保守性.将该算法施加于化工过程中广泛应用的连续搅拌釜式反应器,仿真结果表明了算法的有效性.

We propose a multi-model coordinated predictive control algorithm under networked information mode for plant-wide operation nonlinear systems. When a large-scale switching occurs,we have adopted the gap metric theory to divide the operation space of the nonlinear system into local subspaces under different operation conditions. In each subspace,we have applied a local linear model to design an infinite-horizon predictive controller. To transfer the problem of state variables in different subspaces,we have assessed the influence of the predicted state transitions in subintervals on the control performance. The optimization of N-step horizon control law sequences（ instead of a single feedback control law） reduces the conservativeness caused by ignoring subspaces transition in multi-model predictive control. We have applied the algorithm to the continuous stirred tank reactor,which is widely used in chemical processes. Our simulation results show the effectiveness of the proposed algorithm.