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Control of an induction motor using sliding mode linearization

100%
Etien E., Cauet S., Rambault L., Champenois G.
International Journal of Applied Mathematics and Computer Science
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2002
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tom 12
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nr 4
523-531
EN
Nonlinear control of the squirrel induction motor is designed using sliding mode theory. The developed approach leads to the design of a sliding mode controller in order to linearize the behaviour of an induction motor. The second problem described in the paper is decoupling between two physical outputs: the rotor speed and the rotor flux modulus. The sliding mode tools allow us to separate the control from these two outputs. To take account of parametric variations, a model-based approach is used to improve the robustness of the control law despite these perturbations. Experimental results obtained with a laboratory setup illustrate the good performance of this technique.
2
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Weakly nonlinear regression model with constraints I: nonlinear hypothesis

100%
Kubácek L., Tesaríková E.
Discussiones Mathematicae Probability and Statistics
|
2005
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tom 25
|
nr 1
115-133
EN
The problem considered is under which conditions in weakly nonlinear regression model with constraints I a weakly nonlinear hypothesis can be tested by linear methods. The aim of the paper is to find a region around the approximate value of the regression parameter with the following property. If we are certain that the actual value of the regression parameter is in this region, then the linear method of testing can be used without any significant deterioration of the inference.
3
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Positivity and linearization of a class of nonlinear continuous-time systems by state feedbacks

88%
Kaczorek T.
International Journal of Applied Mathematics and Computer Science
|
2015
|
tom 25
|
nr 4
827-831
EN
The positivity and linearization of a class of nonlinear continuous-time system by nonlinear state feedbacks are addressed. Necessary and sufficient conditions for the positivity of the class of nonlinear systems are established. A method for linearization of nonlinear systems by nonlinear state feedbacks is presented. It is shown that by a suitable choice of the state feedback it is possible to obtain an asymptotically stable and controllable linear system, and if the closed-loop system is positive then it is unstable.
4
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Supervisory predictive control and on-line set-point optimization

75%
Tatjewski P.
International Journal of Applied Mathematics and Computer Science
|
2010
|
tom 20
|
nr 3
483-495
EN
The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
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