Accuracy and computational efficiency of suboptimal nonlinear predictive control based on neural models

作者：Lawrynczuk Maciej^*

来源：Applied Soft Computing, 2011, 11(2): 2202-2215.

DOI：10.1016/j.asoc.2010.07.021

• Model predictive control
• Process control
• Neural networks
• Linearisation
• Optimisation
• Quadratic programming

This paper shows control accuracy and computational efficiency of suboptimal model predictive control (MPC) based on neural models. The algorithm uses on-line a neural model of the process to determine its local linear approximation and a nonlinear free trajectory. Unlike the fully-fledged nonlinear MPC technique, which hinges on non-convex optimisation, thanks to linearisation the suboptimal algorithm requires solving on-line only a quadratic optimisation problem. Two nonlinear processes are considered: a polymerisation reactor and a distillation column. In the first case MPC based on a linear model is unstable, in the second case it is slow. It is demonstrated that the suboptimal algorithm in comparison to the nonlinear MPC with full nonlinear optimisation: (a) results in similar closed-loop control performance and (b) significantly reduces the computational burden.

• 出版日期2011-3