Robust Constrained Fuzzy Affine Model Predictive Control With Application to a Fluidized Bed Combustion Plant

• Published in: IEEE transactions on control systems technology Vol. 16; no. 5; pp. 1047 - 1056
• Main Authors: Tiejun Zhang, Tiejun Zhang, Gang Feng, Gang Feng, Jianhong Lu, Jianhong Lu, Wenguo Xiang, Wenguo Xiang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Combustion; Computer science; control theory; systems; Constraints; Control system analysis; Control theory. Systems; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Fluidization; Fluidized bed combustion; Fluidized beds; Fuzzy; Fuzzy control; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Installations for energy generation and conversion: thermal and electrical energy; Linear matrix inequalities; Lyapunov method; Mathematical models; model predictive control; piecewise quadratic Lyapunov functions; Predictive control; Predictive models; Robust control; Robust stability; robustness; Thermal power plants; Fluidized bed combustion; Convex programming; Transients; Model predictive control; Fuzzy control; Control constraint; Lagrange multiplier; Uncertain system; Linear matrix inequality; Robustness; fuzzy systems; Predictive control; State constraint; Transient response; Matrix inequality; piecewise quadratic Lyapunov functions; Real time; Affine transformation; Bilinear form; Real time system; Constrained optimization; Closed feedback; Robust control; Fuzzy logic; Model reference adaptive control systems; Ellipsoid; Non convex analysis; Lyapunov function; Quadratic function
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2007.916320

In this paper, robust constrained model predictive control of uncertain fuzzy affine systems is considered. Based on piecewise quadratic Lyapunov functions, highly efficient robust constrained predictive control approaches are developed so that the closed-loop stability is guaranteed, and the transient control performance is improved even under input or state constraints. Moreover, by using approximate ellipsoid and S -procedure, the solution to the fuzzy affine model based predictive control can be cast as a convex optimization problem subject to some linear matrix inequalities, rather than a nonconvex problem via bilinear matrix inequalities as in conventional fuzzy affine model-based control. The proposed controllers are thus easier for real-time implementation in industry. Simulation results on the fluidized bed combustion plant have demonstrated the performance of the proposed approaches.