基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法。控制器由等效控制器和H∞控制器两部分组成。用RBF神经网络逼近非线性函数，并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能。H^∞控制器用于减弱外部及神经网络的逼近误差对跟踪的影响。所设计的控制器不仅保证了闭环系统的稳定性，而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标。最后给出的算例验证了该方法的有效性。     

MES系统在典型离散制造车间的选型与应用分析

企业在MES系统选型时除了要具备一定的前瞻性外,还要对自身现状有比较明确的目标和清晰的认识,相同的软件可以有不同的配置,实施效果的好坏往往不是选定一套优秀的产品就可以决定的.     

一种机器人球腕的模型优化方法

许多机器人的共同特点是在操作臂末端安装了球腕.球腕是机器人上一个重要的独立结构,它的静态误差必然影响操作臂末端执行器的位姿精度.以机器人末端的球腕作为独立的研究对象,在静态误差分析的基础上,提出了一个评价球腕精度的综合指标.根据这一指标建立优化模型,对存在静态误差的球腕模型进行优化,从而建立新的球腕运动学模型.应用新模型进行运动学及动力学解算,可以减小机器人球腕的位姿误差,特别是减小具有较大静态误差的机器人球腕的位姿误差.通过对机器人球腕进行的局部优化,整个机器人末端执行器的运动精度将在一定程度上有所提高.      

弱引力下小行星动量驱动机器人的可控跳跃行为

以小行星表面着陆探测为背景,提出一种动量驱动机器人(MoRo)以满足弱引力复杂环境下的探测需求。该机器人利用弱引力环境下的摩擦和碰撞特性,通过主动辨识环境参数,规划和控制动量轮以产生期望的驱动力矩,完成可控性跳跃及腾空后的稳定拍照等任务。首先,基于MoRo的动量轮刹车机构特性,分析了MoRo在弱引力环境下的跳跃机理并对其跳跃方式进行了规划;接着考虑动量轮驱动机构三闭环伺服系统的非线性特性,基于Herze碰撞模型和Karnopp摩擦模型建立了MoRo在小行星表面的跳跃行为动力学模型;其次,使用机器学习算法建立环境参数和MoRo运动的函数关系,并基于环境参数规划动量轮转速实现跳跃距离和腾空高度的可控。最后,通过数值仿真校验了MoRo跳跃规划方法和控制方法的可行性。     

雷达模拟机管制案例课件制作系统的设计

一、引言 随着民航事业的飞速发展，空中交通流量的不断增加，空中交通管制服务将面临严峻的挑战。如何在新形势下进一步提高管制员培训的质量，以更好地为确保飞行安全服务，成为了每一个管制培训工作者思考的问题。要达到这一目标，对培训设备进行不断改进以适应前进中的管制工作，是一个重要的方面。     

A direct position determination method with combined TDOA and FDOA based on particle filter

The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination (DPD) methods, with combined Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA), do not perform well under low Signal-to-Noise Ratio (SNR), and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound (CRLB). The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.     

Robust control for an unmanned helicopter with constrained flapping dynamics

In this paper, a neural network based adaptive prescribed performance control scheme is proposed for the altitude and attitude tracking system of the unmanned helicopter in the presence of state and output constraints. For handling the state constraints, the barrier Lyapunov function and the saturation function are employed. And, the prescribed performance method is used to deal with the flapping angle constraints for the unmanned helicopter. It is proved that the proposed control approach can ensure that all the signals of the resulting closed-loop system are bounded, and the tracking errors are within the prescribed performance bounds for all time. The numerical simulation is given to illustrate the performance of the proposed scheme.     

基于航路规划的反舰导弹发射顺序和间隔研究

在反舰导弹实际战术使用中,为提高突防概率,主要是采取数量饱和攻击的样式,且要求多枚导弹同时到达目标.目前某些反舰导弹具有航路规划功能,只要合理确定导弹的发射顺序和间隔,可以实现同时到达.本文从2条典型反舰导弹的航路入手,建立了简化的航路规划模型,对不同攻击角的航路航程飞行时间进行了仿真计算,并与直接攻击导弹的航程飞行时间进行比较和分析,得到了齐射多枚反舰导弹时进行航路规划的攻击差角、发射顺序和间隔等的具体结果,有一定的理论价值和实际使用参考价值.     

基于滑模干扰观测器的空空导弹三维预测制导律设计

针对目标大机动情况下制导准确性较差的问题,研究了基于滑模干扰观测器的空空导弹三维预测制导律。利用连续时间非线性预测控制方法,根据导弹当前的状态,选择弹道跟踪误差作为预测性能指标,对其进行在线滚动优化,获得制导指令。同时为了进一步提高导弹制导规律的抗干扰能力,利用滑模干扰观测器对内部不确定性和外部未知干扰进行逼近,并与预测控制方法所得到的制导指令进行综合得到优化制导指令。最后仿真结果表明,所设计的PCG相比于传统的PN,性能有较大的改善,能够实现目标大机动情况下的精确制导,并且所需导弹机动过载小、脱靶量小,具有一定的鲁棒性。     

Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults

This paper studies a robust adaptive compensation Fault Tolerant Control(FTC) for the medium-scale Unmanned Autonomous Helicopter(UAH) in the presence of external disturbances,actuator faults and input saturation.To improve the disturbance rejection capacity of the UAH system in actuator healthy case, an adaptive control method is adopted to cope with the external disturbances and a nominal controller is proposed to stabilize the system.Meanwhile, compensation control inputs are designed to reduce the negative effects derived from actuator faults and input saturation.Based on the backstepping control and inner-outer loop control technologies, a robust adaptive FTC scheme is developed to guarantee the tracking errors convergence.Under the presented FTC controller, the uniform ultimate boundedness of all closed-loop signals is ensured via Lyapunov stability analysis.Simulation results demonstrate the effectiveness of the proposed control algorithm.