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In this paper, two methods of control for high-voltage Full Bridge Series-Parallel Resonant (FBSPR) DC-DC converter are proposed and the results are compared. Soft switching operation using Zero Current Switching (ZCS) and Zero Voltage Switching (ZVS) technologies is employed to decrease the losses and optimize the efficiency of converter. The way of obtaining small-signal model of FBSPR converter...
Multi-motor drive is a multi-input multi-output (MIMO), nonlinear and strong-coupling system. Its high precision coordinated control performance can meet the requirements of many drive applications, such as urban rail transit, paper making, electric vehicle drive, and steel rolling. To decouple the velocity and the tension of the three-motor drive system, a new control strategy is proposed by incorporating...
To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN), builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer...
In this paper self adaptive RBF neutral network PID controller for linear elevator is presented. RBFNN is widely used in many fields to solve computational problems that are difficult to solve by conventional method. Nowadays with the rapid development of permanent magnet linear synchronous motor (PMLSM) application in elevators, the design of PID controllers of these systems are very important. This...
The maximum power point tracking (MPPT) aims to increase the efficiency of Photovoltaic (PV) systems by operating their PV panels at the optimum power point. Many strategies have been introduced to achieve this objective. However, these strategies vary in their tracking performance, computational complexity and cost. The rapid changes in environmental conditions and the nonlinearity in the current-voltage...
In this paper, we propose an intelligent approach to power system stabilization using a system-centric controller. This architecture uses a control algorithm based on a supervisory loop concept implemented with a system centric controller combining both a Dual Heuristic Programming (DHP) and Model Reference Adaptive Controller (MRAC) controller. The controller performance is tested on a Single Machine...
From the dynamic model of robot arm, a new fuzzy neural network controller is proposed to control the robot arm's trail in this paper. System simulation analysis is given by fuzzy controller and fuzzy neural network controller respectively. Results show that fuzzy neural network control methods has better trail track than the fuzzy control method.
There are many kinds of indexes in desulfurization technology selection of thermo-electricity project, between which complicated nonlinear relation is presented. It is difficult to calculate and lack of effective evaluation methods. In this paper, a comprehensive evaluation index system of desulfurization technology selection has been built, the BP neural network evaluation method is applied to desulfurization...
In this paper, two methods of control for high-voltage Full Bridge Series-Parallel Resonant (FBSPR) DC-DC converter are proposed and the results are compared. Soft switching operation using Zero Current Switching (ZCS) and Zero Voltage Switching (ZVS) technologies is employed to decrease the losses and optimize the efficiency of converter. The way of obtaining small-signal model of FBSPR converter...
By merging the feed forward neural network, the competitive learning algorithm and the fuzzy control, the neural network-based adaptive fuzzy control algorithm is proposed. This system can produce more reasonable fuzzy rules by the competitive (clustering) algorithm, and control the object by the optimized fuzzy rules. The analysis of the system, the experimental result and considerations are given.
Along with the social requirements of power quality improvement, power quality problems are becoming the higher demands of social, reactive power compensation increasingly become the research focus. Based on neural network, non-linear mapping characteristics, puts forward a suitable for direct current control of static VAR generator detecting reactive and harmonic current method, established the neural...
Artificial neural network is a class of biologically-inspired systems that offer solutions to various real-life problems. In this talk, we will present recent advances on neural systems at anatomical and system levels. Recent researches on C. elegans disclose the complete wire diagram of the synaptic connections and morphology of C. elegans associated with functionalities. This enables not only the...
This paper presents the results of Internal Model Control (IMC) for InnoSAT attitude control based on Neural Network (NN). IMC is composed of an inverse model connected in series with the plant and a forward model connected in parallel with the plant. The controller is achieved by estimating the plant and then finding its inverse model of the InnoSAT plant using the NN. The control signal error is...
In consideration of the common sensor failures in aero-engine control system, a new approach is proposed using dual redundant predictors based on neutral network in this paper. The neutral network temporal redundant predictor and spatial redundant predictor are created over the time series redundant information of single sensor and the space redundant information of multi-sensor respectively. The...
The paper deals with an application of non-traditional type of neural network, concretely an orthogonal neural network, for adaptive control. It was provided for real control system, i.e. compressed-air aggregate. The paper includes a description of the method of adaptive inverse control, a description of the orthogonal neural network's structure for this real control system and evaluating of the...
According to the demands and the characteristics of the expert system of electronic control engine fault diagnosis, combined with neural network technology, this paper uses the technical solution of IIS+ASP+SQL-Server combined with Web server to design the expert system of electronic control engine fault diagnosis, which is based on the B/S structure. The study focuses on neural network fault diagnosis...
This paper introduces control requirements, control method choosing, hardware design and software for the automatic mixture system of magnesia refractory bricks. S7–200 series of PLC are used to realize automatic control for batching system. Because of complexity of automatic mixture system, conventional compensating control method can not meet the required question precisely. A kind of mixture control...
Strip rolling is a very complicated nonlinear process, and characterized by couple between automatic shape control (ASC) and automatic gauge control (AGC), the coupling relationship between ASC and AGC heavily affects the improvement of combined controlling quality. This paper presents a kind of multivariable combination control system based on CMAC-PID. The perfect simulation results indicate that...
The grinding industrial production system is a typical complex nonlinear multivariable process with strongly coupling and large time delays. The up-to-the-minute research results of integrated modeling and intelligent control of grinding process are summarized. It mainly includes the fuzzy control, artificial neural network control, soft-sensor modeling and hybrid intelligent control strategy. In...
This paper deals with the synchronization problem of delayed chaotic neural networks with stochastic disturbances using periodically intermittent control. Lyapunov stability theory and free-weighting matrix method are utilized to establish stochastic synchronization criteria for designing intermittent control law, which guarantees that the synchronization error system is exponentially stable in the...
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