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Stochastic closed-loop model predictive control of continuous nonlinear chemical processes

机译：连续非线性化学过程的随机闭环模型预测控制

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A new predictive control framework for chemical processes is presented, that has a number of fundamental differences to classical MPC. Both future disturbances and future process measurements are explicitly introduced in the model prediction, while back-off prevents violation of the inequality constraints. A feedforward trajectory, used for constraint pushing, is optimized simultaneously with a linear time-varying feedback controller, used to minimize the back-off. No feedback is generated by the receding horizon implementation itself. Via several transformations, the resulting optimization problem is rendered convex. For nonlinear processes, this applies to the sub-problem in a sequential conic optimization approach. A two stage LQG approach reduces the complexity even further for large scale systems. The method is illustrated on a HDPE reactor example and compared to a LTV-MPC. (c) 2005 Elsevier Ltd. All rights reserved.
机译：提出了一种新的化学过程预测控制框架，该框架与经典MPC有许多基本差异。模型预测中明确引入了未来的干扰和未来的过程测量，而后退则防止了不平等约束的违反。用于约束推动的前馈轨迹与线性时变反馈控制器同时进行了优化，用于最小化补偿。后退的地平线实施本身不会产生任何反馈。通过几次变换，最终的优化问题变得凸了。对于非线性过程，这适用于顺序圆锥优化方法中的子问题。两阶段LQG方法甚至进一步降低了大型系统的复杂性。该方法在HDPE反应器示例中进行了说明，并与LTV-MPC进行了比较。 （c）2005 Elsevier Ltd.保留所有权利。

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