LAUSR

In industrial processes, zone control is often applied to some control systems that do not have set‐point control requirements. Zone control involves constraining controlled variables within a range of feasible solutions, so there is a tension between interval constraint intensity and interval feasibility. To meet the requirements of interval feasibility, general control schemes often soften constraint intensity, resulting in frequent constraint violations. However, for some irreversible processes such as chemical and biochemical processes, violating constraint can lead to unpredictable results. This study attempts to solve this problem through improving the performance index of general model predictive controls with soft constraints. This goal is achieved through introducing additional margin to controlled variables in order to strengthen control intensity without increasing computational complexity. This approach effectively reduced the frequency of zone violations and the size of output errors, and guaranteed zone feasibility. Furthermore, this approach was implemented without significant increase in energy consumption and actuator operation. The stability of the algorithm was proven using Lyapunov theorem. Comparative simulation results demonstrated the effectiveness of the proposed method compared with conventional methods.