The first part of this article introduces formulas for dimension of motor-size in the case in which an electrical machine replaces the combustion engine. There were shortly described various types of electrical drives that can be applied to drive electrical cars: DC machine, brushless DC machine and induction motor. There are derived differential equations both of electric and mechanic part of the system. As a result of simulation that was performed using MATLAB/SIMULINK there are compared two alternative time-responses of acceleration: with an automatic controlled gearbox and with a manual operation. There were simulated motor-current, battery-current, angular speed of the motor and speed of the car during acceleration. and Obsahuje seznam literatury
A control system architecture design for an underwater ROV, primarily Class I - Pure Observation underwater ROV is presented in this paper. A non-linear plant model was designed using SolidWorks 3D modeling tool and is imported to MATLAB as a 3D model. The non-linear modeled plant is linearized using the MATLAB linear analysis toolbox to have a linear approximate model of the system. The authors designed controllers for the linear plant model of underwater ROV. PID controllers are utilized as a controller of the modeled plant. The PID tuning tools by MATLAB are utilized to tune the controller of the plant model of underwater ROV. The researchers test the control design of underwater ROV using MATLAB Simulink by analyzing the response of the system and troubleshoot the control design to achieve the objective parameters for the control design of underwater ROV.
A model following control system (MFCS) can output general signals following the desired ones. In this paper, a method of nonlinear MFCS will be extended to be a nonlinear descriptor system in discrete time. The nonlinear system studied in this paper has the property of norm constraint ||f(v(k))||≤α+β||v(k)||γ, where α≥0, β≥0 , 0≤γ<1. In this case, a new criterion is proposed to ensure the internal states be stable.
A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.
This paper develops an Adaptive Wavelet Neural Network Control (AWNNC) algorithm for radar active heat dissipation system. The radar core processor belongs to a highly precision component which consists of the electronic device of radio frequency integrated circuit (RFIC) with high power and high performance. The radar core processor should be operated in a narrowly closed environment without convection, which will increase the heat sink effect inside the core processor and further affect its reliability and life-time. The AWNNC comprises a wavelet neural network (WNN) controller and a robust compensator. The WNN controller is a principal tracking controller which is utilized to mimic an ideal controller; and the parameters of WNN are online tuned by the derived adaptation laws based on the gradient descent method. The robust compensator is designed to dispel the approximation error between the ideal controller and the WNN controller, thus the asymptotic stability of the closedloop system can be achieved. Based on National Instruments-PCI extensions for Instrumentation (NI-PXI) system, combined the Thermo Electric Cooler (TEC) with a duct heater, active heat dissipation intelligent control system is designed to fix the problem of heat dissipation in long distance in a narrowly closed environment without convection. According to the amount of thermal source and thermal energy, the smart control system can help to adjust the rate of heat dissipation by taking advantage of an adaptive control so that the performance of heat dissipation may be accumulated by its numbers. Last but not least, compared the traditional analog circuit controller with adaptive wavelet neural network controller, the research proves that its proposed active heat dissipation intelligent control system can reach an excellent and accurate temperature control. Speaking more precisely, adaptive wavelet neural network controller can be easily adaptive to any environment. It is equipped with a good capability of tracking and searching; and in terms of the effect of temperature control, it never actually jitters due to an input of voltage saturation compared with traditional analog circuit controller. All these can make chips able to adjust its adaptive rate of heat dissipation in accordance with the thermal source of the chips in a narrowly closed environment without convection.
Visual displays play the key-role in almost every human-controlled system. In the process of development of such systems industrial designers need to design legible visual displays in ergonomic sense.
Readability of visual display can be affected by many factors including its position, the size of graphic details and also by visual defects of a man. At recent time it is possible to get some of visual displays properties using a computational modelling. The modelling is applicable, for example, to used font size or to the speed of movable parts of display determination ([3], [8], [9]). But computational models cannot involve all the substantial factors, which have an indispensable effect to the readability of visual displays, especially more complex factors. Position of stimulus created by a visual display on the humane retina affects an ability of recognizing the shape, the colour and the movement, for example. This and similar factors can be taken into account by knowledge modelling, by an expert system. The intersection of these two types of modelling - the computational modelling and the knowledge modelling - can be termed as a hybrid modelling.
This paper is concerned with design of an expert system which is suitable for integration into the hybrid model. and Obsahuje seznam literatury
This paper concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation and reduce the chattering phenomenon introduced by sliding mode control. So first, we conceived a sliding mode controller of the induction motor. A new approach is applied to the cascade structure is presented. For this purpose, a new decoupled and reduced model is first proposed. Then, a set of simple surfaces and associated control laws are synthesized. However, as the magnitude of the piecewise smooth function depends closely on the upper bound of uncertainties, which include parameter variations and external disturbances, we propose a new form of this piecewise smooth control function with a threshold which ensure a significant reduction of the chattering but could not eliminate it. To overcome such a limitation of this control, adaptive neuro fuzzy inference controllers (ANFIS) are designed. Simulation results reveal some very interesting features.
One of the approaches adopted to generate multiclass classifiers from binary predictors is to decompose the multiclass problem into multiple binary subproblems. Among the existing decomposition approaches, one may cite the use of Directed Acyclic Graphs (DAG) to combine pairwise classifiers. This work presents a study on the influence of the DAG structure in the performance obtained in multiclass problems when Support Vector Machines are used in the induction of the binary predictors.
Artificial neural network (ANN) model has been used for years to conduct research in stock price prediction for three reasons. First, it has a higher prediction accuracy rate in empirical research. Second, it is not subject to the assumption of having samples from a normal distribution. Third, it can deal with non-linear problems. Nevertheless, the accuracy of prediction relies on the parameter settings of neural network as well as the complexities of problems and the neural network architecture; the results of the analysis could be even more significant with the selection of optimal parameters and network architecture. Currently, as a way of setting parameters, most researchers employed the trial and error method. However, this method is very time-consuming and labor-intensive and may not result in the optimal parameters. Therefore, this research took advantage of a back propagation neural network (BPNN) for the purpose of parameter optimization through constructing a model of stock price prediction, applying design of experiment (DOE) to systematize experiment scheduling, and methods of main effects analysis and interaction analysis. The research used two datasets of financial ratios from 50 blue chip companies in Taiwanese stock market and 40 listed American banks in New York stock exchange as experimental samples. Research results showed that the correlation forecasting, root mean squared error (RMSE), and computing time, which can effectively increase the accuracy of stock price prediction, are better than traditional statistical methods and conventional neural network model.
One of the most recent and perspective gas detectors is a smart wave\-guide acoustic detector, in which chromatogram represents the mass concentration of the gas to be detected. On the other hand, with respect to design criterions and limits (cost and size), an alternative numerical-based detector has been designed, using a multi-layer perceptron neural network to estimate the frequency and mass concentration of the unknown gas (sample). Experimental data were used for designing the database of the neural network-based detector. The proposed NN-based detector was compared with the real one to validate the proposal. Finally, numerical results were obtained to evaluate its performance.