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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...
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...
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...
A novel approach is promoted for fuzzy neural ship controllers. A RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network...
A novel compound controller is developed for the vehicle active suspension with an electro-hydraulic actuator. The models of the actuator and the quarter vehicle are both first established. A compound controller is designed to utilize RBF neural network approximate the output of PID, and to combine PID algorithm with a RBF neural network output as its control laws. This controller synthesizes both...
A radial basis functions (RBF) neural network terminal sliding mode strategy is developed to control a uncertain coupled chaotic system with the two freedoms. Based on the designed adaptive update laws, the weights of RBF neural network are trained on-line so that the designed controllers can be updated adaptively. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled...
Direct Torque Control (DTC) is a high performance control method. The stator flux observer is a key part in the method. The accuracy of the stator flux estimation directly affected the performance of DTC. The traditional induction motor stator flux observation method have been analyzed in This paper. And for the shortcomings of existing methods, a on-line identification methods based on Radial Basis...
Three phase electrical machines are normally exposed to lowered levels of supply voltage quality conditions which can appear simultaneously due to voltage disturbances of overvoltage or undervoltage, voltage unbalance and voltage waveform distortions. These voltage disturbances can cause effects of seriously overheating winding insulation resulting in degradation and reduced lifespan of the machines...
In this paper, the sliding-mode lag synchronization control scheme is proposed based on the neural network to synchronize two different delayed chaotic systems. An integral delayed sliding surface is presented to design the sliding mode control. The lag synchronization controller is achieved by combining the RBF (radial basis function) neural network with sliding-mode control. Numerical simulations...
Large inertia, serious delay, nonlinear parameters and complex structure are current problems of process of medium frequency induction furnace, the traditional PID control is difficult to meet the control requirements. As the development of intelligent control theory has been made tremendous results, adaptive PID control program is proposed based on the RBF neural network, using neural network identifier...
Among numerous control schemes for flexible joint robots, the main problem is that the full state variable of acceleration and jerk must be known, which are difficult to measure, and the noise may be merged in the main signal. To solve this problem, a self adaptive composite control scheme is developed to control the flexible joint robots with modeling errors and subject to uncertain disturbances,...
This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the face of both structured and unstructured uncertainties. First, a nonlinear controller based on the equations of motion of the robot is elaborated. Its aim is to produce a stable control. Then, an adaptive RBF neural controller is implemented to compensate structured and unstructured uncertainties...
Nonlinear motion model of HEV electronic throttle is built. Aiming at the problem that is difficult to set the nonlinear control system optimum parameters for the traditional PID control, and based on the advantages of the fast convergence and strong universal approximation ability of the neural network, the method neural network setting PID control electronic throttle based on the Radial Basis Function...
For magnetostrictive actuator (GMA) inherent hysteretic, a new real time hysteresis compensation method consisting of radial basis function neural network (RBF) feedforward and PID feedback controller is presented to achieve the position tracking control of GMA. Simulation results show the control strategy is effective for GMA hysteresis which is changed by the input signal, it can set up the hysteresis...
The control problem of Wood Vacuum Dehumidifier is very complicated. A PID control method based on RBF neural network algorithm is designed. The controller is based on the conventional PID control, . makes use of the study ability of the nerve network to turning the PID control parameters, and proceeds the simulation research using matlab software. From the simulation results, it is can be shown that...
For the defects of conventional direct torque control (DTC) system of switched reluctance motors (SRM), RBF neural network (RBFNN) and fuzzy adaptive PID controller are applied to direct torque control system of SRM in the paper. Switching state table is replaced by RBFNN; fuzzy adaptive PID controller is applied to the outer loop for speed adjustment. In order to verify the validity of the method,...
The sliding mode controller is presented for automotive Anti-lock Braking System (ABS), and the drawback of control chattering occurred in the classical sliding mode control can be alleviated with the proposed control scheme. Moreover, the robustness of neural network adaptive control system can be improved to some extent. Simulation research is performed to the vehicles brake on the wet road situation,...
Aimed at the difficulty of nonlinear system control, take advantage of the high exactitude approaching ability of RBF neural network to nonlinear system, a model identification method based on neural network is formed. At the same time, use the single-neuron Net to construct an auto-adaptive PID controller. Combine them, a new control method appear. The result of the simulation indicated that the...
In order to increase the elevators running efficiency and quality of service, the optimizing control strategy of elevators is studied. In this paper a new hybrid control method which optimizes passenger service in an elevator group is described. It is capable of optimizing the neural-controller based on Particle Swarm Optimization (PSO) of an elevator group controller. Starting from the operation...
In this paper, a novel control scheme based on RBF Neural Network is proposed for High-precision Servo System. The aim of this study is to reduce the influence which arises from modeling error, unknown model dynamics, parameter variation and disturbance acted on the practical system and to achieve high tracking precision. This scheme consists of a Neural Network controller (NNC), a Feedforward controller...
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