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- Title
Constrained generalised minimum variance controller design using projection-based recurrent neural network.
- Authors
Toshani, Hamid; Farrokhi, Mohammad; Alipouri, Yousef
- Abstract
In this study, a generalised minimum variance control (GMVC) method using the projection-based recurrent neural network (PRNN) is proposed to minimise the error variance in the output of the non-linear plant. One the main drawbacks of the conventional GMVC approaches is the lack of a systematic procedure to deal with the input constraints. In this study, the PRNN is employed for incorporating the input constraints to the minimum variance index. This network is based on the optimality conditions of a constrained problem and is designed using projection theorem. To formulate the proposed approach, by considering an ARMAX model of the system and converting the cost function to a quadratic programming problem, the dynamics and output equations of the PRNN is obtained. The stability and global convergence of the PRNN is analytically shown. Moreover, suitable conditions for the weighting matrices of the cost function are determined to ensure the closed-loop stability. The proposed control method is applied to the non-linear quadruple tank and a comparative analysis between MVC, GMVC and the proposed approach is performed.
- Subjects
MINIMUM variance estimation; NONLINEAR control theory; NEURAL circuitry; CONSTRAINTS (Physics); GRAPHICAL projection; QUADRATIC programming
- Publication
IET Control Theory & Applications (Wiley-Blackwell), 2017, Vol 11, Issue 2, p143
- ISSN
1751-8644
- Publication type
Article
- DOI
10.1049/iet-cta.2016.0141