An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Application of PID Controller Based on BP Neural Network in Export Steam’s Temperature Control System

By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.

An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems

A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK

According to advantages of neural network and characteristics of operatingprocedures of engine, a new strategy is represented on the control of fuel injection and ignitiontiming of gasoline engine based on improved BP network algorithm. The optimum ignition advance angleand fuel injection pulse band of engine under different speed and load are tested for the samplestraining network, focusing on the study of the design method and procedure of BP neural network inengine injection and ignition control. The results show that artificial neural network technique canmeet the requirement of engine injection and ignition control. The method is feasible for improvingpower performance, economy and emission performances of gasoline engine.

Parameters Optimization of the Heating Furnace Control Systems Based on BP Neural Network Improved by Genetic Algorithm

The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Novel flow control mechanism based on improved BP neural network in cognitive packet network

In this paper, a novel flow control mechanism in cognitive packet network (CPN) based on the improved back propagation (BP) neural network is proposed, considering the flow distribution status predicted by BP neural network when packets are routed. The objective is to increase the capacity of CPN and improve the quality of service (QoS) by achieving flow balance. Besides, considering the slow convergence speed of traditional BP algorithm and the quick change of the flow status in cognitive packet network, an improved BP algorithm with dynamic learning rate is designed in order to achieve a higher convergence speed. The mechanism, which we propose, regards the predicated traffic data as an important factor when packets are routed to implement flow control. By achieving balance, the quality of network can be improved obviously. The simulation results show that the proposed mechanism provides better average time delay and packets loss ratio.

基于BP神经网络的集中供热二次网回水温度预测控制研究

针对集中供热系统二次管网存在的水力失调问题,设计了二次网水力平衡调节及回水温度预测模型,并实施智能控制策略,以实现二次网回水温度的精准控制。首先,构建BP神经网络预测模型,将此模型的输出视为二次网回水温度给定值;其次,在整个系统控制中,实施BP神经网络与PID控制器相结合的策略,进行二次网回水温度的控制。以高邑县某小区换热站数据为基础,通过阶跃响应曲线法建立二次网回水温度控制系统的数学模型,并通过BP-PID控制进行仿真实验。实验结果表明,与传统PID控制器相比,BP-PID控制器具有调节时间短、超调量小的优点,能够快速达到平稳状态。

采用改进BP-PID控制的机器人避障仿真研究

针对移动机器人避障过程中行驶路径长、寻路速度慢等问题,提出了一种改进反向传播-比例-积分-微分(BP-PID)控制器,并对移动机器人避障效果进行仿真验证。利用移动机器人在二维坐标系的避障简图,得出了移动机器人运动方程式。引用比例-积分-微分(PID)控制器和3层BP神经网络结构,利用BP神经网络的学习能力调整PID控制器参数。引用粒子群算法进行改进,通过改进粒子群算法在线优化BP-PID控制器,确保移动机器人BP-PID控制器收敛于全局最优值,从而使移动机器人避障效果更好。在不同环境中,采用Matlab软件对移动机器人避障效果进行仿真,比较改进前和改进后的移动机器人避障效果。结果显示:在不同环境中,改进前和改进后的BP-PID控制器均能使移动机器人安全地躲避障碍物;但是采用改进的粒子群算法优化BP-PID控制器,可以使移动机器人运动路径更短,迭代次数更少,搜索时间更短。采用改进BP-PID控制器,能够提高移动机器人避障过程中寻路速度,缩短行驶路径,效果更好。

基于GRU-BP算法的高精度动态物流称重系统

针对动态物流秤测量精度对载重、采样频率、带速较为敏感的问题,提出了一种高精度动态物流称重系统。首先,采用三因素五水平正交试验法,结合皮尔逊相关性检验原则,使用低通巴特沃斯与卡尔曼滤波器对传感器压力信号进行了滤波降噪处理,并将加速度信号作为模型输入信号,进行了特征补偿;然后,基于深度学习算法,提出了一种改进的门控循环单元模型,在该模型采样区间内将压力与振动改写为时序化信号,并将其共同输入门控循环单元(GRU)模型;最后,对GRU模型进行了改进,对其结构输出了层堆叠误差反向传播神经网络(BP),有效加强了模型的非线性映射能力。研究结果表明:在各类传动速度及测试货物下,该模型的最大测量误差相对于同类型深度学习模型长短期记忆(LSTM)神经网络、循环神经网络(RNN)时序模型及传统数值平均模型的误差,依次降低了16.14%、27.14%、76%,可用于各类称重系统。

基于模糊BP神经网络的智能轮椅BLDCM控制

现阶段多数轮椅电机仍使用传统PID控制,该控制方式存在控制精准度较低、超调量较大以及抗扰动能力差等问题。为解决以上问题,通过对无刷直流电机进行研究,在分析了其控制方法后,提出一种基于模糊BP神经网络的BLDCM控制方法。首先,研究了BLDCM结构并搭建数学模型。其次,在模型基础上构建了模糊BP神经网络PID控制器。最后,在Matlab/Simulink中搭建整个电机控制系统进行三种不同工况下的运动控制仿真,并与传统PID控制算法进行对比。实验结果表明:模糊BP神经网络PID控制策略能获得更好的PID控制参数,具有良好的抗扰动能力,有效的改善了整个轮椅控制系统的动态性能。

Decoupling Control Method Based on Neural Network for Missiles

In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

基于BP神经网络的Smith-Fuzzy-PID算法在阀门定位中的应用研究

为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。

基于SO-BP神经网络的温室环境预测模型研究

由于温室环境中温湿度的调控过程存在滞后响应特性,预测温室环境变化趋势是构建温室精准控制系统中不可或缺的一部分。针对传统神经网络算法在温室预测方面精度不足等问题,提出一种基于蛇优化算法(snake optimizer,SO)优化BP神经网络的温室环境预测方法。试验结果表明,该方法预测15 min内温度的决定系数R^(2)为0.9564,比BP模型、HHO-BP模型分别提高14.87%、2.19%,平均绝对误差MAE、平均绝对百分比误差MAPE、均方根误差RMSE值分别为0.4813、2.2378、0.6729;预测15 min内湿度的R^(2)为0.9821,比BP模型、HHO-BP模型分别提高13.12%、2.37%,预测指标MAE、MAPE、RMSE值分别为1.7090、2.5842、2.2838。该模型的预测结果较理想,可用于温室温湿度预测。

基于BP-FWA算法的无人艇载火箭炮回转机构伺服控制系统研究

针对目前传统艇载火箭炮回转机构控制误差大、精度低及行驶中抗干扰能力弱等问题,提出基于烟花算法(BP-FWA)神经网络算法无人艇载火箭炮回转机构伺服控制研究。构建某无人艇载火箭炮回转机构伺服控制构架;建立回转机构数学模型,结合DSP进行控制信号处理;提出BP-FWA算法模型设计,实现对回转机构伺服控制的精确优化控制。结果表明:该算法下的伺服控制系统响应速度快、控制精度高、误差小及抗干扰能力强,提升无人艇载火箭炮的运行效果,具有很重要军事工程应用价值,为实现我海军艇载武器装备的智能化水平提供重要的支撑。

基于BP神经网络PID的节水灌溉施肥系统研究

中国的化肥使用率常年居世界首位,且农业用水利用率较低,依靠个人经验的方法不仅造成了肥料和水资源的浪费,而且使当地生态环境也受到污染。由于管路运输等原因,节水灌溉施肥系统具有模型的时变性、非线性与时滞性的特点,普通控制器很难对节水灌溉施肥系统的流量进行精准控制。针对上述问题,设计了一种基于BP神经网络PID的控制器,以期实现节水灌溉施肥系统对液体肥流量的精准控制;同时,与传统PID控制器进行对比,用MatLab软件进行仿真分析,得到阶跃响应曲线。研究结果表明:基于BP神经网络PID的控制器具有优异的控制效果,可以满足节水灌溉施肥系统精准控制的实际要求。

基于PSO-BP模糊PID的变距取苗机构控制系统设计

为满足番茄、辣椒等蔬菜作物的移栽需求,基于向下取苗原理设计了一种适用72穴和128穴两种主要番茄钵苗穴盘规格的变距取苗机构,通过建立数学模型获得了取苗机械手参数的目标函数,并利用粒子群和模拟退火混合算法对其结构参数进行优化。同时,为实现变距取苗机构的精确控制,提出了一种基于PSO-BP的模糊PID算法以提高控制精度,介绍了系统的结构与工作原理,并通过选型计算与分析建模建立了控制系统的数学模型。针对传统PID控制器稳定性差、响应速度慢等不足之处,利用PSO-BP模糊PID对控制器的参数进行在线调整,以满足控制过程中对参数的不同需求。仿真结果与试验数据的分析表明:在参数相同条件下,基于PSO-BP模糊PID控制系统系统稳定性更好、响应速度更快,具有良好的鲁棒性,提升取苗成功率的同时降低了基质损伤率,能够满足变距取苗机构高精度快速稳定控制的需求。

基于BP神经网络算法的超声电源频率追踪技术

超声振动系统主要由超声换能器和超声复合加工电源组成。其中超声换能器在加工过程中受到多种因素影响,会发生谐振频率漂移现象;超声复合加工电源需要输出对应频率的电信号,保证超声振动系统的稳定工作。为避免超声换能器损坏,设计一种基于BP神经网络算法的控制模型,通过分析超声电源运行历史数据来实现对超声电源输出频率的控制。利用Multisim对电路进行仿真,并通过试验采集理论谐振频率在25 kHz的超声换能器稳定工作时超声复合加工电源的输出信号,通过Matlab建模仿真来验证BP神经网络模型的控制精度和可靠性。结果表明,产生的模拟输出与实际输出频率最大误差不超过5%,有助于超声复合加工电源的稳定工作。