Broad Learning Aided Model Predictive Control With Application to Continuous Stirred Tank Heater
Mo Tao, Tianyi Gao, Xianling Li, Kuan Li
Representative MFC — matched on the paper’s system type only, not its reactor or geometry.
Reported parameters
No values extracted from this paper yet.
Open in lab for full controls, parameter editing, and template overlays.
Open in lab →What they did
- System
- MFC
What worked
No outcome metrics extracted yet.
Abstract
This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.
Keywords
Identifiers
- Journal
- Frontiers in Control Engineering
- Year
- 2021