基于自适应模糊神经网络的反舰导弹非线性控制算法

提出了一种综合运用动态逆、模糊神经网络和滑模控制的非线性控制方法.首先运用动态逆理论对非线性系统进行近似线性化,利用具有在线学习能力的模糊神经网络来抵消系统的误差,建立了基于自适应模糊神经网络的控制结构,根据李雅普洛夫稳定理论导出了网络权值的自适应调整规则,用滑模控制和鲁棒控制分量保证了系统的鲁棒性.并将该非线性控制算法用于某型侧滑转弯导弹的控制系统设计.仿真结果表明,这种方法能有效消除扰动的影响,提高导弹过载控制系统响应的精度.     

Robust Graded Sliding Mode Tracking Control for Low Speed Spinning Ballistic Missiles

The nonlinear dynamic model of spinning ballistic missiles is established during the first boosting phase of the missile. Based on the conventional backstepping sliding mode control and the assumption of a two time-scale separation of missile dynamics, a graded sliding mode controller is designed with two sub-sliding surfaces which have invariability to external disturbances and parameter perturbations, and a matrix which comprises three first order low pass filters is introduced to prevent ＂explosion of terms＂. Owing to the upper bounds of the uncertainties are difficult to obtain in advance, adaptive laws are introduced to estimate the values of the uncertainties in real-time. Eventually, the numerical simulation results given to show the proposed controller can ensure the steady flight of missiles.     

嵌入式无线局域网适配器的设计与实现

针对单词簇上训练朴素贝叶斯文本分类器概率估计偏差较大所导致的分类精度较低问题,在概率分布聚类算法得到的单词簇的基础上,根据单词与簇间互信息建立有序单词子序列,采用有放回随机抽样对序列构造规模相当的样本集,并将估计出的参数的平均值作为训练得到的参数对未知文本进行分类.公共文本实验数据集上的实验结果表明,该文提出的训练方法相对于传统的朴素贝叶斯分类器训练方法能够获得更高的分类精度且过程相对简单.     

关于滑动DFT算法中的频谱泄漏问题的探讨

针对滑动DFT算法中的频谱泄漏效应的处理问题,提出用频域加窗的办法,避免了时域加窗中窗与输入信号的N次相乘,破坏滑动DFT实时计算的简捷性、可靠性,保证了滑动DFT算法的实现与优化。在硬件实现上,用3个结构简单的谐振器,将其谐振频率分别调到3个相邻的DFT频率单元,即可实现此处理过程。     

An output feedback nonlinear decentralized controller for unmanned vehicle co‐ordination

This paper presents vision‐based control strategies for decentralized stabilization of unmanned vehicle formations. Three leader–follower formation control algorithms, which ensure asymptotic co‐ordinated motion, are described and compared. The first algorithm is a full state feedback nonlinear controller that requires full knowledge of the leader's velocities and accelerations. The second algorithm is a robust state feedback nonlinear controller that requires knowledge of the rate of change of the relative position error. Finally, the third algorithm is an output feedback approach that uses a high‐gain observer to estimate the derivative of the unmanned vehicles' relative position. Thus, this algorithm only requires knowledge of the leader–follower relative distance and bearing angle. Both data are computed using measurements from a single camera, eliminating sensitivity to information flow between vehicles. Lyapunov's stability theory‐based analysis and numerical simulations in a realistic 3D environment show the stability properties of the control methodologies. Copyright © 2007 John Wiley & Sons, Ltd.     

New results in robust actuator fault reconstruction for linear uncertain systems using sliding mode observers

This paper presents a robust actuator fault reconstruction scheme for linear uncertain systems using sliding mode observers. In existing work, fault reconstruction via sliding mode observers is limited to either linear certain systems subject to unknown inputs, relative degree one systems or a specific class of relative degree two systems. This paper presents a new method that is applicable to a wider class of systems with relative degree higher than one, and can also be used for systems with more unknown inputs than outputs. The method uses two sliding mode observers in cascade. Signals from the first observer are processed and used to drive the second observer. Overall, this results in actuator fault reconstruction being feasible for a wider class of systems than using existing methods. A simulation example verifies the claims made in this paper. Copyright © 2007 John Wiley & Sons, Ltd.     

SLIDING MODE PREDICTION TRACKING CONTROL DESIGN FOR UNCERTAIN SYSTEMS

In this paper, a tracking control algorithm based on sliding mode prediction for a class of discrete‐time uncertain systems is presented. By creating a special model to predict the future sliding mode function value and by combining feedback correction and receding horizon optimization approaches, which are extensively applied in predictive control strategy, a discrete‐time sliding mode control law for tracking problem is constructed. With the designed control law, closed‐loop systems have strong robustness to matched or unmatched uncertainties as they eliminate chattering. Besides, in the robustness analysis, the boundary condition for uncertainties, which is a universal presupposition in sliding mode control method, is not required. Numerical simulation and cart‐pendulum experiment results illustrate the validity of the proposed algorithm.     

基于IR1150S与UC3854的Boost PFC变换器比较

研究了单周期控制技术在Boost PFC变换器中的应用,回顾了传统的平均电流控制技术,对基于UC3854和IR1150S的Boost PFC变换器的电路结构、工作原理、设计过程以及性能进行了详细的比较,并制作了两台实验样机进行实验验证。结果表明,基于IR1150S的Boost PFC变换器在保持了良好性能的同时,还有电路结构简洁,设计过程简单,成本低等优势。     

大坝电厂#3机组协调控制功能的完善

通过对大坝电厂#3机组协调控制原理的介绍及存在问题的分析,提出了改造完善方案,使协调控制功能得到进一步的完善。     

M‐matrices and global convergence of discontinuous neural networks

The paper considers a general class of neural networks possessing discontinuous neuron activations and neuron interconnection matrices belonging to the class of M‐matrices or H‐matrices. A number of results are established on global exponential convergence of the state and output solutions towards a unique equilibrium point. Moreover, by exploiting the presence of sliding modes, conditions are given under which convergence in finite time is guaranteed. In all cases, the exponential convergence rate, or the finite convergence time, can be quantitatively estimated on the basis of the parameters defining the neural network. As a by‐product, it is proved that the considered neural networks, although they are described by a system of differential equations with discontinuous right‐hand side, enjoy the property of uniqueness of the solution starting at a given initial condition. The results are proved by a generalized Lyapunov‐like approach and by using tools from the theory of differential equations with discontinuous right‐hand side. At the core of the approach is a basic lemma, which holds under the assumption of M‐matrices or H‐matrices, and enables to study the limiting behaviour of a suitably defined distance between any pair of solutions to the neural network. Copyright © 2006 John Wiley & Sons, Ltd.