Jietron based on model predictive control algorithm

Nonlinear Predictive Control Technology

Modeling mechanism that can adapt to the changeable production environment (expansion, transformation)

Online Model Switching Technology

Technology that enables online modification and tuning of controller model parameters

Dynamic Soft Sensing Technology

Soft sensing method that can adapt to changing operating conditions (such as raw material properties, large changes in load)

Integrate advanced control technologies such as process mechanism, intelligent control technology, expert operation knowledge, multi-variable predictive control technology and online optimization technology into one “intelligent” advanced control technology for a class of devices

An overall scheduling optimization solution based on the integration of advanced control, online device optimization, raw material blending and scheduling optimization, and product blending optimization

Scroll optimization

★ Calculate the optimal control sequence once in each sampling period, and calculate it again in the next sampling period, and perform rolling execution over and over again, taking into account various uncertainties in the actual control process

★ The error of the model and the disturbance of the system are corrected by feedback through the measured value of the controlled variable, so that the system can reach the actual optimum.

Model estimation

★ Use the model to estimate the motion and error of the controlled object in the future, and use it as the basis for determining the current control effect

★ Adapt the control strategy to the storage, causality and hysteresis of the controlled object, and obtain a better control effect

Feedback correction

★ Use measurable information to correct the estimated value at each sampling time to suppress errors caused by model mismatch and interference

★ Using the revised estimated value as the basis for calculating the optimal control, the robustness of the control system is significantly improved