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Prediktive controllers with principles of artificial intelligence
Matys, Libor ; Mikšánek, Vojtěch (referee) ; Pivoňka, Petr (advisor)
Master’s thesis deals with problems of predictive control especially Model (Based) Predictive Control (MBPC or MPC). Identifications methods are compared in the first part. Recursive least mean squares algorithm is compared with identification methods based on neural networks. Next parts deal with predictive control. There is described creation MPC with summing element and adaptive MPC. There is also compared fixed setting PSD controller with MPC. Responses on disturbance and changes of parameters of controlled plant are compared. Comparing is made on simulation models in MATLAB/Simulink and on physical model connected to PLC B&R.
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Electrical Drives Predictive Control Algorithms
Mynář, Zbyněk ; Vavřín, Petr (referee) ; Václavek, Pavel (advisor)
This work deals with the predictive control algorithms of the AC drives. The introductory section contains summary of current state of theory and further description and classification of most significant predictive algorithms. A separate chapter is dedicated to linear model predictive control (linear MPC). The main contribution of this work is the introduction of two new predictive control algorithm for PMSM motor, both of which are based on linear MPC. The first of these algorithms has been created with the aim of minimizing its computational demands, while the second algorithm introduces the ability of field weakening. Both new algorithms and linear MPC were simulated in MATLAB-Simulink.
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Electrical Drives Predictive Control Algorithms
Mynář, Zbyněk ; Vavřín, Petr (referee) ; Václavek, Pavel (advisor)
This work deals with the predictive control algorithms of the AC drives. The introductory section contains summary of current state of theory and further description and classification of most significant predictive algorithms. A separate chapter is dedicated to linear model predictive control (linear MPC). The main contribution of this work is the introduction of two new predictive control algorithm for PMSM motor, both of which are based on linear MPC. The first of these algorithms has been created with the aim of minimizing its computational demands, while the second algorithm introduces the ability of field weakening. Both new algorithms and linear MPC were simulated in MATLAB-Simulink.
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Prediktive controllers with principles of artificial intelligence
Matys, Libor ; Mikšánek, Vojtěch (referee) ; Pivoňka, Petr (advisor)
Master’s thesis deals with problems of predictive control especially Model (Based) Predictive Control (MBPC or MPC). Identifications methods are compared in the first part. Recursive least mean squares algorithm is compared with identification methods based on neural networks. Next parts deal with predictive control. There is described creation MPC with summing element and adaptive MPC. There is also compared fixed setting PSD controller with MPC. Responses on disturbance and changes of parameters of controlled plant are compared. Comparing is made on simulation models in MATLAB/Simulink and on physical model connected to PLC B&R.
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