Model Predictive Control of Large-scale (Plant-wide) Systems

美国的许多城市都面临着联合下水道溢流(CSO)的问题。联合下水道输送城市污水和暴雨径流的混合物。在暴风雨期间，这类下水道(和/或相关的处理厂)的处理能力可能会超出，合并后的污水必须未经处理而排放到环境中。实时控制联合污水输送系统是解决这一问题的一种潜在的低成本解决方案。可以对调节闸门和水泵进行操作，以最大限度地储存系统内的峰值负荷，从而最大限度地减少每次风暴事件期间的CSO。上述问题是一个大系统控制算法的设计问题。PI计划采用模型预测控制(MPC)策略，并为西雅图大都会污水收集系统开发控制方案。对他的控制方案的最终考验将在这个真正的、全面的设施上进行。具体地说，该项目涉及：(1)具有多个操纵变量和多个输出变量的对象的动态响应的优化，(2)未测量的对象扰动的预测，(3)考虑对大多数输出变量的不等式约束；(4)考虑包括积分器状态、非线性特性、滞后和时滞在内的对象动态。

Many cities in the U.S. are faced with the problem of combined sewer overflows (CSOs). Combined sewers carry a mixture of municipal sewage and storm runoff. During storm conditions, the capacity of such sewers (and/or the associated treatment plants) may be exceeded and the combined sewage must be released to the environment untreated. Real-time control of the combined sewage conveyance system is a potential low cost solution to the problem. Regulating gates and pumps can be manipulated to maximize storage of peak loads within the system to minimize the CSOs during each storm event. The problem described above is the design of a control algorithm for a large-scale system. The PI plans to adapt Model Predictive Control (MPC) strategies and develop a control scheme for the Metropolitan West Seattle sewage collection system. The ultimate test of his control scheme will be on this real, full scale facility. Specifically the project involves: (1) optimization of the dynamic response of a plant in which there are many manipulated variables and many output variables, (2) forecasting of unmeasured plant disturbances, (3) accounting for inequality constraints on most output variables, and (4) incorporating plant dynamics that include integrator states, nonlinear characteristics, and lags and time delays.