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The electrohydraulic servo system of a certain type of mines weeping plough is a complex and nonlinear system. It is difficult to construct its accurate model by first principle method and to achieve satisfactory control performance by traditional PID controller. In this paper, the radial basis function neural network with orthogonal least square learning algorithm is used to model the electrohydraulic...
During to the non derivability for triangle wave reference trajectory, whose high-frequency harmonics will stimulate the resonant modes in the high-frequency voice coil motor (VCM), the traditional speed and acceleration feed forwards can not be achieved. In this paper, the triangle wave was Fourier transformed to get its Fourier series composed of several sine functions, which are high order differential...
Because of the dead-zone nonlinearity characteristic in pneumatic system, high steady state error and overshoot have occurred in the position response as a typical imperfect implementation of associated control dynamic. In this situation, there is a necessity to be able to effectively utilizing the intelligent controller framework. The primary purpose of this research is to develop a control algorithm...
This The pellet sintering process is a physical chemistry process, which is pure time-delay and non-linear with more variables and discrete parameters. In order to solve this problem, a new BP algorithm of NN for the temperature identification of sintering shaft furnace is introduced in this paper. BP network is combined with PID control and simulation effects are given with MATLAB. The experiment...
According to a multi-delay dynamic neural group network, the Lyapunov function is constructed firstly to achieve the judgment criteria of global asymptotical stability. Then, the synchronization states are analysized based on the complex coupled network. Through the Lyapunov stability theorem, the multi-delay dependent judgment criteria of synchronization states is presented. Finally, the controller...
In this paper, the state-space disturbance observer was successfully applied to servo motors to estimate and compensate for load variation. Furthermore, an auto-tuning procedure was developed accordingly to identify the varied parameters for state-space disturbance observer of the motor. Then, a real-time IP position controller based on identified parameters is designed by neural network for permanent...
This work is devoted to present a control application in an industrial process of iron pellet cooking in an important mining company in Brazil. This work uses an adaptive control in order to improve the performance of the conventional controller already installed in the plant. The main strategy approached here is known Multi-Network-Feedback-Error-Learning (MNFEL), it uses multiple neural networks...
A turning control strategy based on BP neutral network and modified by a PID algorithm is presented in this paper for the dual electric tracked vehicle. Firstly analyzed the tracked vehicle turning dynamics and establish the torque distribution strategy. The BP network is trained by data from the distribution strategy simulation results; the PID algorithm is used to eliminate the dependence of terra...
The paper presents a novel adaptive neural-network based nonlinear model predictive control (NMPC) methodology for hybrid systems with mixed inputs. For this purpose an online self-organizing growing and pruning redial basis function (GAP-RBF) neural network is employed to identify the hybrid system using the unscented Kalman filter (UKF) learning algorithm. A receding horizon adaptive NMPC is then...
Based on the static Preisach hysteresis model of Piezoelectric Smart Materials, the technology of Artificial Neural Networks is applied for the hysteresis modeling of Piezoelectric Smart Materials. The BP network is chosen as the method of identifying the non-linear hysteresis sysytem. Adopting this method one sample's hysteresis model is built. The results have shown that this method is correct and...
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...
On the basis of results of previous studies, this paper overviews several kinds of typical artificial muscles' structural principle, the technological study condition and application status in main fields, and classifies artificial muscles. This paper mainly summarizes domestic and foreign scholars' research results on the control technology of artificial muscles, including neural network control,...
According to the nonlinear and parameter time-varying characteristics of vehicle stability control, a sliding control algorithm is proposed based on radial base function (RBF) neural network. The algorithm not only can reduce the chattering caused by the conventional sliding mode, but also improve the robust of the adaptive neural network control. The simulation results show the algorithm ensures...
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