【作者】 张新良；

【导师】 刘春生；

【作者基本信息】 南京航空航天大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 本文详细研究了两种典型的前向神经网络(BP网络和RBF网络)的学习和训练算法，提出了一种新颖的基于紧支集余弦函数的径向基神经网络，其克服了常用的高斯型RBF神经网络虽具有紧支集但各基函数非正交的不足，其收敛速度快、网络参数选取有理论依据且相比于高斯型RBF神经网络具有更强的泛化能力，仿真验证了其有效性。 将神经网络与非线性动态逆相结合，利用神经网络对系统的逆模型进行建模，对实现的伪线性系统分别设计PID控制和自适应控制两种方案进行综合。在基于神经网络自适应控制中，在分析了系统误差的基础上，用一个自适应动态神经网络在线消除系统的近似逆误差和正向模型的辨识误差，并给出了神经网络权值调整规律，仿真对有效性进行了验证。 针对一类广泛存在的含有不确定性的非线性系统，提出了一种基于输入输出线性化的非线性观测器的设计方法，并分别对含有和不含有建模不确定性的非线性系统设计了观测器。在设计含有建模不确定性的系统的观测器时，用RBF神经网络对不确定性进行建模，在给定Lyapunov函数的基础上设计了神经网络加权系数的调整规律并将非线性观测器应用于神经网络实现的动态逆控制系统，设计了仿真对其有效性进行了比较验证。 针对神经网络采用一维反向传播训练算法速度较慢且易于陷入局部极小点的不足，设计了一种间接自校正模糊神经网络控制系统，利用遗传算法(CA)对隶属度函数的结构和参数进行优化，仿真比较表明该控制比模糊PID控制具有更优的性能。更多还原

【Abstract】 In this paper, two typical multi-layer feedforward artificial neural networks- BP network and RBF network have been studied detailedly. Their learning and training rules have been analyzed profoundly and their abilities to approximate arbitrary nonlinear function have been testified and compared by the simulation. A new RBF neural network has been presented which uses a raised-cosine function as activation transfer function. It provides a wider generalization in comparison with Gaussian RBF neural networks by simulation as well as strong approximation ability, fast convergence, a rule to select the parameters of the networks.This paper uses multi-layer feedforward artificial neural networks (abbr. MLF NNs) to approximate the nonlinear dynamic inversion on the basis of analyzing principle and characteristics of nonlinear dynamic inversion and neural networks. The pseudo-linear system has been synthesized by classic PID control and self-adaptive control separately. For the latter, on the basis of analysis of the errors, a RBF NNs has been applied to approximate the forward model of the system and another dynamic neural network has been applied to compensate on line the errors from nonlinear dynamic inversion approximation and forward model identification. The training rule of the neural networks has been presented. The simulation results suggest that the control strategies can provide better robustness.The RBF NNs has been used to approximate the uncertainty in the process of an observer design for a class of nonlinear system. At the same time, the observers of nonlinear system with and without uncertainty have been analyzed and designed based on the input-output linearization theory. The training rule for the weights of the RBF NNs has been proposed in the paper based on the known system model and given Lyapunov function. The simulation shows effectivity of the designed observer.An indirect self-adaptive fuzzy-neural network controller (FNNC) has been proposed with its parameters and the structure tuned simultaneously by GA in virtue of the powerful optimization property of GA. The structure of the controller is based on the Radical Basis Function (RBF) neural network with Gaussian membership functions. The performance of the proposed FNNC is compared with a conventional fuzzy-PID controller and the simulation results show that the FNNC presents encouraging advantages.更多还原