The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time-consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.
In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult because the plant is nonlinear and no stationary. Furthermore, it deals with the situation where the plant becomes uncontrollable without any restrictive assumptions. In contrast to previous work  on the same subject, the number of neural networks has been reduced to only one network. The number of the neurons in a network structure as well as choosing certain design parameters was specified a priori. The computer test results have been presented to show performance of proposed neural controller.
The development of digital signal processors and the increase in their computing capabilities bring opportunities to employ algorithms with multiple variable parameters in active noise control systems. Of particular interest are the algorithms based on artificial neural networks. This paper presents an active noise control algorithm based on a neural network and a nonlinear input-output system identification model. The purpose of the algorithm is an active noise control system with a nonlinear primary path. The algorithm uses the NARMAX system identification model. The neural network employed in the proposed algorithm is a multilayer perceptron. The error backpropagation rule with adaptive learning rate is employed to update the weight of the neural network. The performance of the proposed algorithm has been tested by numerical simulations. Results for narrow-band input signals and nonlinear primary path are presented below.
In monitoring vertical displacements in elongated structures (e.g. bridges, dams) by means of precise geometric levelling a reference base usually consists of two subgroups located on both ends of a monitored structure. The bigger the separation of the subgroups, the greater is the magnitude of undetectable displacement of one subgroup with respect to the other. With a focus on a method of observation differences the question arises which of the two basic types of computation datum, i.e. the elastic and the fixed, both applicable in this method, is more suitable in such a specific base configuration. To support the analysis of this problem, general relationships between displacements computed in elastic datum and in fixed datum are provided. They are followed by auxiliary relationships derived on the basis of transformation formulae for different computational bases in elastic datum. Furthermore, indices of base separation are proposed which can be helpful in the design of monitoring networks. A test network with simulated mutual displacements of the base subgroups, is used to investigate behaviour of the network with the fixed and the elastic datum being applied. Also, practical guidelines are given concerning data processing procedures for such specific monitoring networks. For big separation of base subgroups a non-routine procedure is recommended, aimed at facilitating specialist interpretation of monitoring results.