Search results

The purpose of this paper is to design and test a linear predictive control algorithm with elements of fuzzy logic in the non-linear speed region of a two-mass system with a flexible shaft.

To compensate the non-linearity of friction in the low-speed region, the elements of the Q matrix have been retuned with the use of fuzzy logic. First, the influence of the Q matrix on the dynamics of the drive has been discussed. On the basis of these findings a fuzzy system has been developed.

It has been demonstrated that applying a relatively simple fuzzy system can reduce unwanted non-linear phenomena in the low-speed region; at the same time, the dynamics of the drive in the other regions is not deteriorated.

The solutions presented in the paper are original and have not been published so far. The influence of non-linear friction on the work of the drive in the low-speed region at different values of the matrix Q has been shown. Also, a novel system of online adjustment of the values of the Q matrix in a predictive speed controller has been introduced. Besides, the system has been compared against the classical predictive regulator.

Various control structures and approaches are in use nowadays. Development of new ideas allows to obtain better quality in control of different industrial processes and hence better quality of products. As it may seem that everything in the classical systems has already been discovered, more and more research centres are tending to incorporate fuzzy or neural control systems. The purpose of this paper is to present an application of the adaptive neuro-fuzzy PID speed controller for a DC drive system with a complex nonlinear mechanical part.

The model of the driven object including such elements as nonlinear shaft with backlash and friction has been modelled using Matlab-Simulink software. Afterwards experimental verification has been made using a dSPACE control card and experimental system with two DC motors connected with an elastic shaft.

The presented study shown that the adaptive controller is able to damp the torsional vibration effectively even for the wide range of the system nonlinearities. What is more the design approach for controllers design parameters has been described. Proposed approach is based on requested properties of system. Using proposed tuning scheme no detailed information about the object are needed.

This paper presents for the first time fully an PID adaptive neuro-fuzzy controller. The inputs are the weighted tracking error, error’s derivative and integrated error. What is more the adaptation algorithm consists of a model tracking error its derivative and integer. Also the proposed tuning algorithm in such a form is an original outcome.

The paper sets out to deal with the off‐line identification of induction motor (IM) parameters at standstill. Determination of values of the IM parameters is essential in sensorless drives with regard to accuracy and quality of the control system.

The presented identification method is based on the reconstruction of stator current response to the forced stator voltage step change; thus the cost function is defined in the classical form of the mean squared error between the computed and experimental data. The identification via evolutionary algorithms (EAs) is presented. The considered problem is continuous and thus a continuous version of EA is suggested as more suitable.

This approach has been shown to have several advantages over classical optimisation methods like the ability to cope with ill‐behaved problem domains exhibiting attributes such as: discontinuity, time‐variance, randomness, and, what is particularly important in this application, the ability to cope with the signals disturbed by noises. Owing to this ability the EAs could be implemented directly for the identification of IM parameters not only in simulations but also in the industrial applications for the motor control, though the electrical signals acquired from real motor and used as input data in the identification procedures are to a large extent disturbed by the electrical noises.

Two versions of the suggested approach are compared: the EA with hard selection and with soft selection. Both algorithms were tested in a simulation and experimental set‐up using digital signal processor for control and signal processing of the voltage inverter‐fed IM drive. Satisfactory results were obtained for the identification procedure based on the selected EA.

The aim of the research was to find out a method of adaptive speed control robust against variation of selected parameters of system like moment of inertia, time constant of torque control loop or torque coefficient of the motor.

The main goal of the research was achieved due to application of artificial neural network (ANN), which was trained on line on the base of speed control error. The good results were gained by elaboration of enough fast and precise training algorithm and proper ANN structure.

The work shows a structure of artificial neural network (ANN), applied as adaptive speed controller, and presents an algorithm of ANN training. Some versions of this algorithm were analysed and verified by simulation and experimental tests.

The research should be continued to determine a final version of training algorithm and its influence on controller properties.

The elaborated adaptive controller can be easily used by applying microprocessor system available now on the market. The proposed control solution is robust against parameters variation as well as their imprecise identification. The controller has ability of self‐tuning which can have great practical advantage.

Social implications are difficult to determine.

The paper presents a new solution of adaptive speed controller, which means a new ANN structure and new training algorithm.

The purpose of this paper is to develop the Permanent Magnet Synchronous Motors drive which has possibility to work in sensorless mode at low speed based on back EMFs estimation.

Estimation uses modified Luenberger observer and the preprocessing of the EMFs before calculating the speed, using derivative and the Kalman filter (KF) to obtain smooth waveform of the estimated speed. This modification is needed because of the nonlinear change of the estimated back EMFs amplitude as a function of speed, in low-speed range.

How to use back EMF observer to estimate a speed in the low-speed range was found in the course of the work. Simple and effective algorithm uses KF and can work even with a relatively big deformation of the estimated back EMF.

Such sensorless drive may be used in low-cost constant or variable speed drive in domestic use or industrial application. Such drive may work properly where there is no initial load torque and the sign of the speed does not change.

Presented results challenge the view that at low-speed range (not the standstill), the back EMF-based method of position estimation is very difficult or impossible. However, the problem lays in proper speed estimation, not the position estimation.