A control method was proposed, which combines the human-imitating control theory and the optimization capability of chaotic neural networks (CNN). Firstly, a qualitative control law(linear or nonlinear control law)was formed according to human-imitating control. Then, the qualitative control law was quantified by CNN. Hopfield neural network is recognized as a useful tool for optimization problems. However, it is often trapped to a local minimum solution. Therefore, gradually reducing chaotic noise is added to the networks to form a powerful globe optimization algorithm. Applying the proposed control method to a double inverted pendulum, the results of numerical simulations and experiments both demonstrate the valid of this control method.
������,�����,����쿣����˿��ƶ�ά��������[J]�����������,2002,17(1):53~56 Zhang Minglian, Sun Changling, Yang Yawei�� Hunan-imitating control for 2-D inverted pendulum[J]�� Control and Decision, 2002,17(1):53~56(in Chinese)
He Yuyao.Chaotic simulated annealing with decaying chaotic noise[J].IEEE Trans On Neural Networks.2002,13(6):1526-1531
Kwok T, Smith A. A unified framework for chaotic neural-network approaches to combinatorial optimization[J].IEEE Trans On Neural Networks.1999,10(4):978-981
�� ��,֣����,�������������Ż��������о���չ[J]��������������Զ���, 2001,20(1):1~5 Wang Ling, Zheng Dazhong, Li Qingsheng��Survey on chaotic optimization methods ��Computing Technology and Automation, 2001,20(1):1~5(in Chinese)
˾�������������ܿ��Ƽ�������ת���о� .����:�������պ����ѧ�Զ�����ѧ���������ѧԺ,2003 Si Changlong��Research on human-imitating intelligent control and the control law quantifying ��Beijing:School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, 2003(in Chinese)