Chebyshev神经网络电路设计

以Ｃｈｅｂｙｓｈｅｖ 神经网络为基础，给出了非线性函数的仿真实例，并提出了用模拟电路实现Ｃｈｅｂｙｓｈｅｖ 神经网络的方法。  

基于模拟电路的Chebyshev神经网络电路设计

以Ｃｈｅｂｙｓｈｅｖ神经网络为基础，给出了３个非线性函数的仿真实例，并提出了用模拟电路实现Ｃｈｅｂｙｓｈｅｖ神经网络的方法。  

基于Chebyshev神经网络的图像复原算法

退化图像的点扩散函数难以准确确定，为此，提出一种基于Chebyshev正交基函数的前向神经网络图像复原算法。该算法以一组Chebyshev正交基为隐层神经元的激励函数，采用BP算法对权值进行修正，达到收敛目标。给出2类Chebyshev神经网络的实现步骤及其相应衍生算法的图像恢复实现步骤。实验结果表明，该算法能较好地实现图像复原。  

多航天器相对轨道与姿态耦合分布式自适应协同控制

基于一致性理论,在有向通讯拓扑结构下对多航天器系统相对轨道及姿态的耦合协同控制问题进行了研究.本文考虑近地航天器相对轨道的非线性方程以及用罗德里格参数描述的航天器姿态运动方程,建立了考虑控制输入耦合的六自由度航天器运动模型.在仅有部分跟随航天器可获取参考状态(记为领航航天器)的情形下,针对航天器存在未建模动态以及外部环境干扰等问题,提出了一种基于切比雪夫神经网络(Chebyshev neural networks,CNN)的自适应增益控制律,使得各跟随航天器在轨道交会的同时姿态保持一致.因为每个航天器上的控制算法仅依赖其自身及相邻航天器的信息,因此控制算法是分布式的.同时考虑到航天器之间的相对速度及相对角速度难以测量,提出了无需相对速度及角速度信息的分布式自适应协同控制律使得各航天器保持一定的队形且具有期望的相对指向.最后对6颗航天器的编队飞行进行了仿真分析,仿真结果表明本文设计的分布式自适应协同控制律是有效可行的.  

Distributed consensus control for multi‐agent systems using terminal sliding mode and Chebyshev neural networks

This paper investigates the problem of consensus tracking control for second‐order multi‐agent systems in the presence of uncertain dynamics and bounded external disturbances. The communication flow among neighbor agents is described by an undirected connected graph. A fast terminal sliding manifold based on lumped state errors that include absolute and relative state errors is proposed, and then a distributed finite‐time consensus tracking controller is developed by using terminal sliding mode and Chebyshev neural networks. In the proposed control scheme, Chebyshev neural networks are used as universal approximators to learn unknown nonlinear functions in the agent dynamics online, and a robust control term using the hyperbolic tangent function is applied to counteract neural‐network approximation errors and external disturbances, which makes the proposed controller be continuous and hence chattering‐free. Meanwhile, a smooth projection algorithm is employed to guarantee that estimated parameters remain within some known bounded sets. Furthermore, the proposed control scheme for each agent only employs the information of its neighbor agents and guarantees a group of agents to track a time‐varying reference trajectory even when the reference signals are available to only a subset of the group members. Most importantly, finite‐time stability in both the reaching phase and the sliding phase is guaranteed by a Lyapunov‐based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controller and show that the proposed controller exceeds to a linear hyperplane‐based sliding mode controller. Copyright © 2011 John Wiley & Sons, Ltd.