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Research On Nonlinear Time Delay System Control Based On Time Delay Neural Network

Posted on:2010-03-25Degree:DoctorType:Dissertation
Country:ChinaCandidate:B HanFull Text:PDF
GTID:1118360275457883Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
In this article,the modeling and control methods are discussed for time delay systems, which is familiar in industrial process.On the base of dynamic neural network theory,the purpose of this paper is constructing a methodology to identify system parameters and model for unknown time delay systems.Furthermore,an effective control method is proposed for dynamic systems with time delay.Dynamic neural network with adaptive time delay parameters is adopted both online and offline for unknown time delay system identification and controller design in this article.Corresponding theory is also analyzed.The main research contents and research conclusions are listed as follows.(1) Research on nonlinear time delay system identification based on Universal Learning Network(ULN).Due to multiple branches and the arbitrary time delay of ULN,an adaptive algorithm is designed to choose the time delay parameters of ULN and adopt this algorithm to identify the nonlinear process with time delay.The performance of ULN has close relationship with the time delay on the branches which connect with the output node, especially.Based on this the large time delay initial value is adopted for increaseing the converge speed.At the same time,an evaluation function is designed for adjusting time delay parameters of ULN during the training the process.The optimized parameters can be used for the time delay identification of object process.Moreover,the stability analysis of ULN with state disturbance is presented,in order to confirm the stability condition of the adaptive algorithm.The simulation results show that the adaptive algorithm of ULN can not only embody the characters of a blackbox nonlinear system but also can identify the pure time delay of the object system well.(2) Research on nonlinear time delay control based on Universal Learning Network.For the time delay system control,a ULN based predictive control method is mentioned in this article.According to the method,a input/output off-line model and time delay parameter of a unknown time delay process is identified by ULN.The ULN that can be a neural network predictor is used for a prediction model of the time delay systems.For the control structure,a neural network based Smith predictive control with internal model control structure is presented for unknown time delay system control.In modern industry processes,due to nonlinear character and long time delay part,pH neutralization process is highly difficult to control such process.Based on the internal mechanism analysis of pH neutralization,a simulation of the proposed method is given.The simulation results show that the proposed controllers can stabilize the pH neutralization process effectively,and have a good robustness.(3) Research on time delay system identification and control based on a novel dynamic neural network.To overcome the limitations of Dynamic recurrent neural network that is a slow convergence method and not suitable for on-line identification,a feedforward neural network with dynamic neuron is presented in this article.The stability of the network is given by the state equation of the network.The gradient descent method is adopted to optimize both weights and time delays parameters.The proposed algorithm can not only embody the dynamic behavior of a poorly known time delay system,but also identify the pure time delay very well.In order to improve the generalization ability of neural networks for poorly known nonlinear dynamic system with long time delay,a modified Particle Swarm Optimization algorithm is proposed for neural network training.Otherwise,to overcome the particles' premature convergence,the white noise and Logistic mapping are used to enhance the particles' search performance and learning efficiency.Based on the dynamic nueral network and robust fault-tolerant control structure,a predictive method is proposed for unknown nonlinear systems with time delay.Finally,simulation results show that the proposed controllers can stabilize the unknown nonlinear time delay systems effectively with robustness.
Keywords/Search Tags:Dynamic neural network, Time delay system, System identification, Predictive control, Particle swarm optimization
PDF Full Text Request
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