Constrained Model Predictive Controller Design For Industrial Cascade Process

Cascade PID control algorithm is popular applied in industrial systems. Though disturbance effect is greatly restrained, cascade PID can not handle constaints. If these constraints are ignored, serious consequences may be incurred, such as a performance penalty or unsafty. Thus handling constrainsts in control system design is an important issue. Model predictive control has a natural ability to handle constraints. This paper proposes the constrained cascade model predictive control based on the systems with cascade construture, that is, it employs model predictive control for both inner and outer loops. Cascade model predictive control has both the advantages of cascade control and model predictive control. The main contents can be divided into the following three parts:(1) Considering structure, energy and safety requirements, there are many constraints in real-world systems and they are calssfied by importance and site repectively. Model predictive control permits constraints on allowable control action to be explicitly included in the computation of an optimal control action. Thus cascade model predictive control can handle constraints. A new method handling constraints based on linear programming is proposed in this paper. Compared with the traditional methods, this one requires modest computation cost. Simulation is implemented, and the result shows that the proposed method is effective.(2) The proposed constrained cascade model predictive control is applied in the power plant superheated steam temperature system and distillation column system. Meanwhile, application in the process control sytem TQC117 is experimented. First, main loops are analyzed and controlled system models of inner loop and outer loops are developed. Then the objective cost functions are employed. Meanwhile, the system constraints and interactions between subsystems are studied. In the end, the proposed method is applied to control temperature.