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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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O časové parametrizaci uživatelských požadavků v mechatronice
Belda, Květoslav
Time parameterization of user demands (demands on course of path, position etc.) is one of inherent preparative operations before starting real control of any of mechatronic systems. The main objective is to generate the reference inputs i.e. desired, required values with appropriate timing of used control system. In general, the time parameterization itself represents generating a time sequence of the reference values according to some deterministic way defined beforehand, where this time-reference sequence interpolates the initial parameters arising from user demands. This paper addresses optimally-smooth time parameterizations intended for mechatronic systems particularly for machining robotic applications.
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Prediktivní řízení pro mechatronické laboratorní modely
Belda, Květoslav
The paper deals with the design of discrete adaptive model-based predictive control for simple mechatronic systems. Simple mechatronic systems are considered as Single-Input/Single-Output systems or possibly systems with low number of inputs and outputs. However, the methods of adaptation and model-based control are not generally limited to this condition. In the paper, a combination of on-line identification and generalized predictive control will be introduced. The identification is based on least squares. The predictive control arises from state-space formulation. This idea is applied to ARX models representing Input/Output formulation. The presented algorithms are derived in computationally suitable square-root form and their correctness is documented by tests on laboratory models.
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Prediktivní řízení s časově proměnnými stavovými modely.
Belda, Květoslav ; Böhm, Josef
The papres is focused on Predictive control for time-variant state-space models both SISO and MIMO type, specially on model identification/composition, further on consideration of system nonlinaerities in equations of predictions and state-space estimation. The algorithms will by presented in coputationally-efective square-root forms and demonstrated by several simulative examples with simple linear SISO systems and with one nonlinear systems represented by MIMO model of planar redundant parallel robot.
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Prediktivní řízení aplikované na rovinné paralelní roboty
Belda, Květoslav ; Böhm, Josef ; Valášek, M.
The design of suitable control is essential part of application of parallel robots. Together with appropriate control, the parallel robots are promising way to improve accuracy and speed of machine tools in industry. This paper deals with the design and results of one control approach represented by predictive control in absolute formulation. It explains exact linearization of used, initially nonlinear models, which are transformed to discrete space form. Control algorithm is derived in square-root form.
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State-space generalized predictive control for redundant parallel robots
Belda, Květoslav ; Böhm, Josef ; Valášek, M.
The paper deals with the design and properties of Generalized Predictive Control for path control of the redundant parallel robots. It summarizes classical and root minimization of the quadratic criterion and direct and two-step design of actuators respectively. As an example, the planar redundant parallel robot is used. Moreover, the paper presents several possibilities to use Predictive control for compliance of additional requirements (smooth trends of actuators or fulfillment antibacklash condition).
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Adaptivní prediktivní LQ řízení s omezením
Böhm, Josef
Controller design for real systems must consider levels of signals in the system. This contrasts with the most design techniques that are based on linearity assumption. The Model predictive control (MPC) is a technique able to consider desired levels of signals. Paper deals with the situation in LQ controller design. It is shown that the latest results in MPC control can be also considered as an LQ approach.
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