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For complex plants the composition of the intellectual control systems and neurocontrol is offered. The developed approach is based on: 1) synthesis of the stabilizing control law on the aprioristic information; 2) plant's parametrical identification on experimental data; 3) synthesis of the «exact» control law on the basis of the specified plants model; 4) realization of the «exact» control law an...
Monitoring and analysis of energy use and indoor environmental conditions is an urgent need in large buildings to respond to changing conditions in an efficient manner. Correct estimation of occupancy will further improve energy performance. In this work, a smart controller for maintaining a comfortable environment using multiple random neural networks (RNNs) has been developed. The implementation...
This paper presents the use of a training algorithm based on a Lyapunov function approach applied to a stator current controller based on a state variable description of the induction machine plus a reference model. The results obtained with the proposed controller are compared with a previously reported method based on a Nonlinear Auto-Regressive Moving Average with eXogenous inputs (NARMAX) description...
The diesel-photovoltaic (PV) based hybrid AC microgrid systems with conventional control philosophies deliver very good performance in the grid connected mode. However, once the microgrid is isolated from the main grid the same philosophies which control the PV at its maximum power can make the microgrid unstable. In this connection, this study proposes a novel neuro-fuzzy controller to ensure the...
Synchronous generator output is proportional to generator load angle but as the parameter moved up the power system security is at stack. Hence, generators are operated well below their steady state stability limit. This raises demand for efficient and fast controllers. Artificial intelligence, specifically Artificial Neural Network (ANN) is emerging very rapidly and has become an efficient tool for...
Feed-forward artificial neural network are applied to establish network model and carry out training and testing and make predictions on coagulant dosage of a certain water plant in North China, which has achieved sound prediction effect.
In this paper we provide a short review of radial basis function (RBF) and its properties. In addition RBF NN was used to estimate the non-Isothermal CSTR. For achieving this goal because of on lining data it was necessary to use the RLS each time that new data come to system. By using this method (RLS), RBF NN updates its weights for mapping the input and output of the system.
A variable structure neural network control (VSNNC) is proposed for a class of uncertain nonlinear SISO systems, in which neural network is used as an estimator for the system unknown nonlinear functions. And variable structure control strategy is improved by adding the continuous function item to control input. It can adjust discontinuous item in control variable adaptively according to the distance...
Investigate mobile robot's history, obstacle avoidance is one of most important research area and also the foundation of building robot's successful behaviors. This paper proposes a Neural Network control system that is able to guide the mobile robots (AmigoBot and P3DX) traverse through a maze with arbitrary obstacles. The pattern is trained by using Matlab toolbox and Aria library for motion control...
Voice coil motor (VCM) is one of the linear electric machine which has faster transient response, higher accuracy. It is widely used in the field of high performance direct drive servo valve. The mathematic model of voice coil motor was analyzed and the position control system of direct driver valve that based on voice coil motor was designed. Against the disturbance in the system, a new position...
The principle of immune feedback and immune-neural network PID algorithm are respectively introduced in this paper. An immune-neural network PID controller is designed by which an adaline neural network is selected as antibody inhibition and adjustment function and the parameters of immune-neural network PID controller are determined by simulation. The transfer function of hydraulic servo system of...
In this paper, a three-dimensional (3D) self-adaptive region fuzzy guidance law based on radial basis function (RBF) neural networks for some attacking UAV was proposed. Firstly, 3D motion equations for pursuit-evasion of UAV and maneuvering target are given. Secondly, the proposed method was applied to decreasing the miss distance distance, which is mostly arisen from the fixed navigation rates of...
This paper presents a novel approach to optimize pattern recognition system using genetic algorithm (GA) to identify the type of hand motion employing artificial neural networks (ANNs) with high performance and accuracy suited for practical implementations. To achieve this approach, electromyographic (EMG) signals were obtained from sixteen locations on the forearm of six subjects in ten hand motion...
In this paper we describe the application of genetic algorithms for optimal type-2 fuzzy system design. We illustrate the approach with two cases, one of designing optimal neural networks and the other of fuzzy control. Simulation results show the feasibility of the proposed approach of using hierarchical genetic algorithms for designing type-2 fuzzy systems.
This paper implement an online training of dynamic neural networks (NNs) for identification and control of permanent magnet synchronous motor (PMSM) servo system. Utilizing two multilayer feed-forward NNs, it makes no such assumptions. The two networks work in tandem to simultaneously achieve system identification and adaptive control. The proposed control system is designed and its effectiveness...
In this work is presented a design and implementation of an intelligent controller applied to dynamic systems. The main objective is to design a management intelligent system that acts in the controllers of Direct current machine (DC) and thermo-eletric control systems drives. The purpose is to evaluate the performance of algorithms for intelligent control that integrate DC and thermal actuators to...
In this paper, we present the principal techniques of modeling a wheelchair like mobile robot by neural network. Direct and inverse neural models are elaborated with the consideration of complex, non-linear and non-holonomic system. Our aim is to obtain the perfect model, by generalizing the model, to use it later for control. One will describe the steps to model the system by neural networks.
This paper investigates the adaptive neural network (NN) control problem for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function (RBF) neural networks are used to approximate unknown nonlinear functions. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control (DSC) technique...
This paper describes an Adaptive Backstepping Neural Network (ABNN) method used for a ship course tracking control. The ship model is described by a third order nonlinear model whose parameters are unknown. The control design uses estimate values of unknown parameters of the system. Then, adaptive laws of the estimation of these values have been proposed. The stability of the controlled system has...
Elevators play an important role in today urban life. The elevator group control (EGC) problem is related to many factors, such as stochastic traffic states, the number of customers, running condition, and it is difficulties in analysis, design and control. In order to increase the elevators running efficiency and quality of service, the optimizing control strategy of elevators is studied in this...
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