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2006年04月03日

【期刊论文】SIMPLE RECURRENT NEURAL NETWORK-BASED ADAPTIVE PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS

陈增强， Xiang Li， Zengqiang Chen， and Zhuzhi Yuan

Asian Journal of Control, Vol. 4, No.2, pp. 231-239, June 2002，-0001，（）：

-1年11月30日

Making use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one-step neural predictive controller (OSNPC) is designed. The whole closed-loop system's asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations.

Neural adaptive predictive control,， simple recurrent neural networks,， stability passivity.，

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2006年04月03日

【期刊论文】Generating Chaos by an Elman Network

陈增强， Xiang Li， Zengqiang Chen， Zhuzhi Yuan， and Guanrong Chen

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS-I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 48, NO.9, SEPTEMBER 2001，-0001，（）：

-1年11月30日

摘要

Abstract-Anew nonlinear neuron-activation function is introduced into the framework of an Elman network, thereby enabling the neural networkto generate chaos. Basic analysis of its fixed point stability and parameter selection criterion for chaos generation is given, and is verified via simulation studies.

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2006年04月03日

【期刊论文】Analysis of Robustness of PID-GPC Based on IMC Structure*

陈增强， CHEN Zengqiang**， MAO Zongxing， DU Shengzhi， SUN Qinglin， and YUAN Zhuzhi

Cjomese K. Cje,. Emg. 11(1)55-61(2003)，-0001，（）：

-1年11月30日

摘要

Proportion integral differential generalized predictive control(PID-GPC), a new type of generalized predictive control (GPC) is introduced, and its quality is analyzed with internal model control (IMC). A very important characteristic, which distinguishes CPC from ordinary IMC, and the robust effect are found. At the same time, a robust region is obtained according to the control laws, so that the defect that the robust analysis could be carried out only with stable models is overcome. It is verified that the robustness of PID-GPC is stronger than general GPC.

process control,， internal model control,， predictive control