A comparative study of self-adaptive long-range predictive control methods
Automatica (Journal of IFAC)
Industrial applications of model based predictive control
Automatica (Journal of IFAC) - IFAC-IEEE special issue on meeting the challenge of computer science in the industrial applications of control
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Computer Controlled Systems: Theory and Design
Computer Controlled Systems: Theory and Design
Fuzzy model based predictive control and comparison with other nonlinear MBPC algorithms
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
Hybrid Fuzzy Modelling for Model Predictive Control
Journal of Intelligent and Robotic Systems
Self-adaptive generalized predictive control of batch reactor
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Feedforward control of a class of hybrid systems using an inverse model
Mathematics and Computers in Simulation
Theoretical and fuzzy modelling of a pharmaceutical batch reactor
Mathematical and Computer Modelling: An International Journal
Adaptive fuzzy control of aircraft wing-rock motion
Applied Soft Computing
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In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.