The most significant characteristic of DCS (Distributed Control Systems) in control is its reliance on the flexible configuration of various control and computation modules. This allows for diverse control strategies to meet the needs of different situations, simplifying the complex and cumbersome tasks previously posed by unit-based instrumentation. With the increasing demands for high flexibility and efficiency from enterprises, and following the emergence and successful application of advanced control strategies such as multivariable predictive control (MRC), advanced process control has received widespread attention in the process industry. It is important to emphasize that the widespread application of various advanced control and optimization technologies is the most effective, direct, and valuable development direction for unlocking and improving the overall performance of DCS.
In practical process control systems, systems based on PID control technology account for over 80%. The quality of PID loop application plays a crucial role in achieving stable, efficient, and high-quality operation of the equipment. Furthermore, based on the control functions of DCS, various improved algorithms can be developed on the basis of the basic PID algorithm to meet the diverse needs of actual industrial control scenarios, such as PID control with dead zone, integral separation PID control, derivative-led PID control, incomplete derivative PID control, and PID control with logic selection functions, etc.
Unlike traditional PID control, predictive control algorithms based on nonparametric models estimate the future output state of the system through a predictive model and calculate the current controller output using a rolling optimization strategy. Depending on the implementation scheme, various algorithms exist, such as internal model control, model-based control, and dynamic matrix control. Currently, practical predictive control algorithms have been introduced into DCS (Distributed Control Systems).
The domestically developed iNICS system innovatively integrates software-defined intelligent I/O with a high-performance safety-grade controller, supporting flexible reconfiguration of the control system. Its miniaturized cabinet reduces weight by 60% compared to traditional equipment and improves seismic resistance through topology optimization technology. This type of predictive controller does not simply place a Kalman filter before conventional predictive control for noise filtering; instead, it uses the Kalman filter as an optimal state predictor, simultaneously performing optimal state prediction and noise filtering.
