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This paper describes the principles of BP algorithm and the improved BP neural network be used in the traditional PID control, neural network and PID control law integration, with both self-learning neural networks, adaptive and capacity to approximate any function, Conventional PID control structure also has a simple, high reliability characteristics. Avoid the network into a local minimum; it can...
Considering the atrocious load property when the rocket is launched, including the huge variation of load inertia, unbalanced torque and the disturbance from the air current impulsion, an optimal PID position controller based on improved Elman network is presented. The context neurons of output layer are added to the original Elman network, and self-feedback gain coefficients are trained as connective...
The Intelligent control methods are drawing great attentions due to their strong adaptive ability and learning ability. Because of strongly nonlinear magnetic characteristics of switched reluctance motor(SRM), modeling of the torque characteristics is difficult. In this paper, the new torque modeling approach based on artificial neural network(ANN) is investigated, where the training data are obtained...
According to the nonlinear and parameter time-varying characteristics of vehicle lateral stability control, a novel algorithm of vehicle lateral stability control based on single neuron network was proposed. Based on self-learning and adaptive ability of single neural network, the parameters of vehicle lateral stability controller were self-tuning on-line and the problem of large computation time...
For the electric heating coupling furnace problem, according to Jacobian information identification of DRNN neural network, the PID decoupling control method based on DRNN neural network setting is proposed for electrical heater furnace. And the electrical heater furnace time-varying system is simulated. Simulation results show that compared with the general PID decoupling control, the PID decoupling...
BP (Back-propagation) neural network and conventional PID (Piping and Instrument Diagram) control combined. A PID control strategy of self-adjusting parameter brought forward. It solved the problems of the parameter certain complex and large computational quantity and realizes online adjustment PID parameters. According to control characteristic of PH value adjustment links in sewage treatment system,...
According to the nonlinear and parameter time-varying characteristics of vehicle stability control, a novel algorithm of vehicle stability adaptive PID control with single neuron network was proposed. Based on self-learning and adaptive ability of single neural network, the parameters of vehicle stability PID controller were self-tuning on-line and the problem of large computation time brought by...
Large-scale Generating Unit in heat power is a system which is complex nonlinear, multivariable, time-variant with long-time delay and difficult to establish accurate model, and etc. So it is hard to make system gain optimum running effect with conventional control strategy. A PID network which has dynamic character is used to identify the coordinated control system for establishing a predictive model...
A traditional PID regulator is usually used to control water level of pressurizer in modern reactors. For such a non-linear and complex time-varying control system, PID regulator is often shown by the large amount of overshoot and long setting time which are not the desired results. In this paper, a compound controller based on cerebellar model articulation controller (CMAC) neural network and PID...
Nowadays congestion control problem of the intermediate nodes in the Internet has received extensively attention in networking and control community. In this paper, a novel adaptive PID (Proportional-Integral-Differential) controller based on neural networks for the problem of AQM with ECN marks is presented. Considering a previously developed nonlinear dynamic model of TCP/AQM system and the queue...
Proportional, integral and differential are defined as a neuron respectively, combined with neural network in this paper. PID neural network (PIDNN) is built and the structure of PIDNN is also simple. Using the PID neural network, the strong coupled time-varying system can be decoupled and controlled easily. The doubly fed hydro-generator system is a novel type of hydraulic generation system. Considering...
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