A numerically efficient fuzzy MPC algorithm with fast generation of the control signal

International Journal of Applied Mathematics and Computer Science

Marusak Piotr M.¹ 

[1] Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665Warsaw, Poland;

关键词: model predictive control;    fuzzy systems;    fuzzy control;    nonlinear control;   

DOI  :  10.34768/amcs-2021-0005

来源: DOAJ

【 摘 要 】

Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If a model used for prediction is linear (or linearized on-line), then the optimization problem is a standard, i.e., quadratic, one. Otherwise, it is a nonlinear, in general, nonconvex optimization problem. In the latter case, numerical problems may occur during solving this problem, and the time needed to calculate control signals cannot be determined. Therefore, approaches based on linear or linearized models are preferred in practical applications. A novel, fuzzy, numerically efficient MPC algorithm is proposed in the paper. It can offer better performance than the algorithms based on linear models, and very close to that of the algorithms based on nonlinear optimization. Its main advantage is the short time needed to calculate the control value at each sampling instant compared with optimization-based numerical algorithms; it is a combination of analytical and numerical versions of MPC algorithms. The efficiency of the proposed approach is demonstrated using control systems of two nonlinear control plants: the first one is a chemical CSTR reactor with a van de Vusse reaction, and the second one is a pH reactor.

【 授权许可】

Unknown