Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle

An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller.     

Modeling and Algorithm Application of Weapon Assignment System

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.     

一种弹道修正弹SINS任意滚转角快速粗对准方法

针对弹道修正弹药出炮口后滚转角处于随机状态,捷联惯导系统(SINS)失准角过大时卡尔曼滤波收敛困难的问题,提出在卫星拒止环境下利用神经网络快速估计初始滚转角的改进方法。在炮口处布设少量导航信标,建立反向传播(BP)神经网络拟合初始滚转角与观测量间的非线性映射模型。针对信标辅助下姿态弱可观的问题,引入惯导测量参数作为输入,提高网络估计精度。采用主成分分析法进行特征提取,简化网络结构。仿真结果表明,与基于非线性卡尔曼滤波的对准方法相比,本算法可实现任意滚转角下的快速粗对准;对射角、初始俯仰角误差未在训练范围内以及存在布设误差等场景也进行了测试,与未优化的BP网络相比,对准精度更高,鲁棒性更好。     

基于ANFIS的舵阻横摇系统

在MATLAB环境下建立了舵阻摇系统的非线性模型．基于所建立的非线性模型和ANFIS控制算法进行了仿真实验。运用Simulink工具箱得到仿真曲线．通过仿真曲线可以看出此控制器具有良好的鲁棒性能．通过对比减摇前后的仿真曲线得出,ANFIS算法对于舵阻摇系统的拉制有效,减摇率为23%左右,减摇效果较好。     

On developing data-driven turbulence model for DG solution of RANS

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN" method provides robust and steady convergence compared to the ‘‘DG+SA" method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.     

遗传神经网络PID控制技术研究与应用

针对空调控制系统非线性、大滞后、时变性等特点,通过对中央空调总风量控制法的研究,提出了采用遗传算法神经网络PID控制方法,使空调控制系统具有较高的控制精度和良好的动态特性。仿真及实验结果表明该方法有效可行。     

迎角快速校准及误差对控制系统的影响研究

基于迎角传感器数据校准,目前所采用的线性插值算法对于测量区域的数值处理存在精度丢失的问题,采用三次曲线拟合以及神经网络逼近的方法实现对已有测量数据的精确映射,避免了由于二次映射引起的精度损失,同时提高了对于未测量区域的预测能力,保证了控制系统的跟踪性能。将校准方法通过两余度迎角校准系统进行了验证,并将校准系统加入动态逆飞行控制系统进行仿真,结果显示了算法的精确性和可靠性。     

忆阻细胞神经网络及图像去噪和边缘提取中的应用

结合忆阻器的记忆特征和细胞神经网络高效并行处理能力,将忆阻器作为可编程连接权值,提出了一种具有忆阻器特性的细胞神经网络.计算机仿真验证了提出的忆阻细胞神经网络用于图像去噪和边缘提取的有效性.     

Representing space in a PDP network: Coarse allocentric coding can mediate metric and nonmetric spatial judgements

In one simulation, an artificial neural networkwas trained to rate the distances between pairsof cities on the map of Alberta, given onlyplace names as input. Distance ratings rangedfrom 0 (when the network rated the distancebetween a city and itself) to 10. The questionof interest was the nature of therepresentations developed by the network's sixhidden units after it successfully learned tomake the desired responses. Analyses indicatedthat the network used coarse allocentric codingto solve this problem. Each hidden unit couldbe described as occupying a position on the mapof Alberta, and each connection weight feedinginto a hidden unit was related to the distanceon the map between the hidden unit and one ofthe stimulus cities. On its own, a singlehidden unit's response was a relativelyinaccurate distance measure. However, bycombining all six hidden unit responses in acoarse coding scheme, accurate responses weregenerated by the network. In a secondsimulation, a second network was trained tomake similar judgements, but was trained toviolate the minimality constraint on metricspace when trained to judge the distancebetween a city and itself. An analysis of thisnetwork indicated that it too was using coarseallocentric coding.     

集成神经网络在智能故障诊断技术上的应用

针对单子神经网络的局限性提出了将神经网络技术集成用于智能故障诊断的思想,并对集成神经网络的诊断系统的结构和工作原理进行介绍.最后,给出一个基于两个BP子网的故障诊断系统的实例,详细介绍其诊断过程,并对其结果进行了分析.