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This paper deals with the robust control of the wastewater treatment process, considered as a multivariable system, using QFT (Quantitative Feedback Theory) method.
Some effective parameter-independent conditions are developed in a concise manner for the global asymptotic stability of the multivariable system using LMI's techniques and principle of Rayleigh's min/max matrix eigenvalue inequality.
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In this paper we present a new algorithm for discrete sliding mode control of linear multivariable systems using fast output sampling feedback.
This paper will describe a method to locate faults in multivariable systems using such input excitation and its application to the intake air system of a modern common rail Diesel engine.
In this study, a multivariable linear quadratic control system using a new state space structure was developed for the chamber pressure in the industrial coke furnace.
This paper investigates the problem of stable identification of multivariable nonlinear systems using a multilayer feedforward neural network.
It requires the identification of a full dynamic model of the multivariable system, and uses constrained nonlinear optimization techniques to find the controller parameters.
Unit vector approach, which is a sliding mode control structure for multivariable systems, is used and a robust adaptive nonlinear controller is designed.
It is based on the identification of a state space model of the multivariable system, and it uses constrained nonlinear optimization techniques to find the controller parameters.
Thanks to the ability to easily deal with multivariable systems, make use of the references and disturbances preview, and enforce time-varying input/output constraints, MPC is the perfect candidate to fulfill this task.
The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty.
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