A Partial Least Squares Aided Intelligent Model Predictive Control Approach
提出一种结合改进偏最小二乘与模型预测控制的数据驱动方法，无需先验知识、计算简单且可在线更新模型，在连续搅拌加热器仿真中验证了其高预测精度和动态处理能力。
A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.