| 摘要 |
A multivariable process controller controls a chemical, polymer or other physical process. Slow tuning and over-conservative controlled variable values are employed during step testing. While all controlled process variables are within safe limits, only one manipulated variable (MV) at a time is step changed. Several manipulated variables are moved when process variables exceed safe limits to ensure that the controlled process variables return to the safe range, such that suitable MV targets for step testing are able to be automatically discovered within a closed loop control environment. Thus, the step test is able to be conducted mostly unsupervised and/or remotely via a telephone or network connection. A new process perturbation approach simultaneously perturbs multiple or all of the process input variables in such a way that the process responses (process outputs) are maximized, while the process variables are maintained inside its predefined operating constraints. It uses magnitude modulated Generalized Binary Noise (MGBN) signals to excite multiple process variables, and uses a specially designed model predictive controller (MPC) to safeguard the process. The specially designed MPC controller uses minimal move and discrete control action to reduce interference with the GBN perturbations and prevents unwanted feedback from contaminating the data quality, while keeping the process operating in a desired range.
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