起落架磁流变减摆器模糊PID控制算法的研究

为了减小飞机机轮的摆振,提高飞机乘坐的舒适性和驾驶的安全性,将磁流变控制技术应用于飞机起落架减摆器,实现减摆器阻尼力的实时智能控制;针对磁流变减摆器,建立了飞机起落架摆振的半主动控制非线性动力学模型,设计了模糊PID控制算法,并使用Matlab/Simulink建立了半主动控制仿真模型;通过调节流过磁感线圈的电流大小改变磁流变减摆器的阻尼力,从而减小机轮摆动实现半主动控制;通过动力学仿真,在给定速度下分别对比未安装减摆器、被动控制下以及半主动控制下机轮摆角、侧向位移、侧滑角随时间变化的曲线,结果表明了模糊PID控制算法的正确性和可行性,该控制策略可以较好的抑制机轮的摆振,同时也表明模糊PID控制算法具有良好的可控性,减摆效果也明显优于传统的被动控制。     

基于粒子群算法的自抗扰飞行控制器优化设计

提出了一种基于粒子群算法的自抗扰飞行控制器优化设计方法。方法中,利用自抗扰控制方法抗干扰能力强、鲁棒性好与对模型参数变化适应能力强的特点,设计了自抗扰纵向飞行控制器,以提高飞行控制性能。同时针对所设计的自抗扰飞行控制器参数较多,难以设计的问题,应用粒子群优化算法进行了控制器参数的自寻优设计。仿真结果表明:不需要人工调参,通过粒子群优化算法自寻优获得的飞行控制器参数具有良好的控制性能,提高了设计效率。     

飞机俯仰运动自抗扰控制器设计

提出了利用自抗扰控制器在大包线范围内设计飞机俯仰运动控制器的新方法．利用二阶自抗扰控制器补偿系统模型扰动和外扰，实现了纵向运动俯仰角变量的跟踪控制．自抗扰控制器直接依据飞机的非线性模型，符合飞机动力学模型摄动大的特点，在很大的包线范围内不需要改变控制器的结构和参数，简化了飞行控制律的设计过程．大包线范围内的仿真结果表明，系统具有良好的动态和稳态性能，控制器具有很强的鲁棒性，为解决大包线范围内的飞行控制问题提供了一种有效的新途径．     

基于改进粒子群算法的四旋翼自抗扰控制器优化设计

针对四旋翼飞行器自抗扰控制器参数较多,人工整定困难且难以得到最优控制效果的问题,提出一种基于改进粒子群算法的四旋翼自抗扰控制器优化方法。在设计了四旋翼飞行器的自抗扰控制器之后,将自抗扰控制器的参数作为粒子群中的粒子进行迭代寻优,同时在传统的粒子群算法基础上,参考遗传算法,对适应值不好的粒子进行交叉保优,以提高粒子的多样性,加快寻优速度。仿真结果表明,对比人工整定参数的控制器,优化后的控制器超调更小,调节时间更快。该方法能够解决四旋翼飞行器自抗扰控制器人工参数整定困难的问题,且优化后的控制器具有更好的控制效果。     

基于U模型的模糊免疫自抗扰控制方法研究

针对一类非线性系统在持续扰动下的控制问题,设计基于U模型的模糊免疫自抗扰控制方法。首先,引入U模型方法进行被控对象建模,提高处理非线性系统的能力,结合自抗扰控制方法,设计基于U模型的改进自抗扰控制器。在非线性反馈环节引入模糊免疫方法实现非线性智能反馈,设计基于U模型的模糊免疫自抗扰控制系统。最后仿真实验表明：基于U模型的模糊免疫自抗扰控制方法在保持了基于U模型的自抗扰控制的简洁性和良好抗扰性能的基础上,简化了控制器参数调节过程,在持续未知扰动下的跟踪速度、精度都更优。     

飞机失事后救生舱氧气系统的数学建模仿真

建立飞机中救生舱氧气控制模型,可实现飞机失事后救生舱中氧气浓度得精确控制.飞机失事后面临的破坏性和环境都是大随机事件,救生舱氧气系统的控制需要对压力、气体温度和氧气系统参数的时间差数据的准确掌握,实现氧气系统性能的定量评估,由于参数容易受到失事时外部环境的影响,无法预先设定.传统的氧气系统控制模型无法准确评估参数在恶化环境下的变化过程,仅能通过设定固定参数评估短期机组氧气性能变化情况,存在较大缺陷.提出采用改进遗传算法的自抗扰控制器氧气系统参数优化模型,塑造考虑控制约束的自抗扰控制器参数优化设计目标函数,并用一种改进自适应混沌遗传算法对氧气系统参数进行整定,实现失事飞机救生舱内氧气系统的有效控制.仿真结果表明,所提控制模型增强了系统的动态性和静态性,可有效应对系统参数的动态性,具有较高得控制性能.     

无人飞艇的自抗扰控制研究和控制器优化

研究了一种多螺旋桨推力飞艇的控制问题,首先对螺旋桨和舵偏进行了控制分配,设计了线性自抗扰控制器,并在风干扰下验证了控制效果。介绍了灰狼优化算法的模型和概念,设计了灰狼优化在控制器参数中的优化步骤,对线性自抗扰俯仰角和偏航角控制器进行优化,经过对比分析,灰狼优化在很小的迭代次数下即可得到控制品质较好的控制器参数。     

基于PSO优化的电动舵机自抗扰控制器设计

将自抗扰控制技术(ADRC)引入电动舵机控制系统,针对系统输出的测量噪声,使用反正切非线性函数对ARDC进行改进,并针对自抗扰控制器参数较多,整定难度大的特点,采用粒子群优化算法(PSO)优化参数.利用Matlab/Simulink对某电动舵机控制系统进行建模、实现基本自抗扰控制器得出基本ADRC控制性能数据,采用粒子群优化算法对ADRC参数进行优化,得出PSO优化ADRC控制性能数据.分析两组数据得出经过粒子群算法优化后的参数更能发挥自抗扰控制器的效能,优化过程对ADRC实际应用具有指导意义.     

基于改进鲨鱼优化算法的自抗扰控制参数整定

非线性自抗扰控制器耦合参数多,常规经验整定法难以获得最优参数,以至于影响控制器的控制精度.单一机制的优化算法整定出的自抗扰参数均可能是局部最优解,不能有效提高自抗扰控制器的控制精度.针对此问题, 提出一种基于改进鲨鱼优化算法的自抗扰控制器参数优化设计方法.为解决基本鲨鱼优化算法易陷入局部最优解、算法后期收敛速度慢的问题,提出混合交叉变异策略与双种群协同机制,以ITAE指标为自抗扰控制器参数选择的优化目标,并以二自由度机械臂为例进行仿真验证.结果表明,优化后的自抗扰控制器具有更小的超调量和更高的控制精度,在加入外界干扰后,控制器可以很快抑制干扰,具有很好的抗干扰能力,改进后的鲨鱼优化算法可以用于复杂非线性系统自抗扰控制器的参数优化.     

基于DE算法的自抗扰控制器设计

提出了一种基于微分进化算法(DE)的自抗扰控制器设计方法;在对飞机短周期运动进行分析的基础上,设计了典型状态下自抗扰纵向飞行控制器;针对所设计控制器可调参数多的问题,对各控制器参数进行了分析,并利用微分进化算法对控制器进行了参数整定;仿真结果表明,经DE算法整定的自抗扰控制器实现了实时指令信号跟踪控制,调节时间小于1.5s,超调量小于2%,具有良好的动态性能;在摄动条件下,舵偏角振荡被抑制在25°以内,显示了较强的鲁棒性。     

斜拉索的磁流变半主动自适应控制器设计

考虑描述磁流变(MR)阻尼器力学行为的LuGre动态摩擦模型,建立了新的斜拉索-MR阻尼器系统模型,该模型能够很好地描述斜拉索-阻尼器系统的动态特性,且能够实时辨识MR阻尼器内部参数.对于新的系统模型,基于Lyapunov直接法设计了抑制斜拉索振动的半主动自适应控制方法;原系统可变换为带有小参数的奇摄动系统,采用奇摄动理论对该系统进行分析,得到了另一类半主动自适应控制方法.仿真结果表明提出的两种控制方法均能够很好地抑制拉索振动.     

Application of generalised neural network for aircraft landing control system

 It is observed that landing performance is the most typical phase of an aircraft performance. During landing operation the stability and controllability are the major considerations. To achieve a safe landing, an aircraft has to be controlled in such a way that its wheels touch the ground comfortably and gently within the paved surface of the runway. The conventional control theory found very successful in solving well defined problems, which are described precisely with definite and clearly mentioned boundaries. In real life systems the boundaries can't be defined clearly and conventional controller does not give satisfactory results. Whenever, an aircraft deviates from its glide path (gliding angle) during landing operation, it will affect the landing field, landing area as well as touch down point on the runway. To control correct gliding angle (glide path) of an aircraft while landing, various traditional controllers like PID controller or state space controller as well as maneuvering of pilots are used, but due to the presence of non-linearities of actuators and pilots these controllers do not give satisfactory results. Since artificial neural network can be used as an intelligent control technique and are able to control the correct gliding angle i.e. correct gliding path of an aircraft while landing through learning which can easily accommodate the aforesaid non-linearities. The existing neural network has various drawbacks such as large training time, large number of neurons and hidden layers required to deal with complex problems. To overcome these drawbacks and develop a non-linear controller for aircraft landing system a generalized neural network has been developed.     

An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem

This paper proposes a hybrid particle swarm optimization algorithm in a rolling horizon framework to solve the aircraft landing problem (ALP). ALP is an important optimization problem in air traffic control and is well known as NP-hard. The problem consists of allocating the arriving aircrafts to runways at an airport and assigning a landing time to each aircraft. Each aircraft has an optimum target landing time determined based on its most fuel-efficient airspeed and a deviation from it incurs a penalty which is proportional to the amount of deviation. The landing time of each aircraft is constrained within a specified time window and must satisfy minimum separation time requirement with its preceding aircrafts. The objective is to minimize the total penalty cost due to deviation of landing times of aircrafts from the respective target landing times. The performance of the proposed algorithm is evaluated on a set of benchmark instances involving upto 500 aircrafts and 5 runways. Computational results reveal that the proposed algorithm is effective in solving the problem in short computational time.     

Observer‐Based Adaptive L2 Disturbance Attenuation Control of Semi‐Active Suspension with MR Damper

A novel nonlinear observer‐based adaptive disturbance attenuation control strategy was proposed for a quarter semi‐active suspension system with a magneto‐rheological (MR) damper in light of the intrinsic nonlinearity, parameter uncertainty, state immeasurability and road randomness. Adaptive adjusting parameters were adopted to avoid the curve fitting and identification of the system parameters by a great deal of experimental data for shortening the development cycle of the control system. Based on the reduced‐order observer, the system states including the immeasurable virtual state of MR damper and inconveniently measured states of suspension system were estimated for the realistic frame of the proposed controller in practice. The dissipative system theory was utilized to reduce the influence of the road disturbance on the system control performance. Simulation results in the bump road and B‐class road indicate that, whether there are perturbations of the system parameters or not, the proposed control scheme always ensures a better performance on the suspension travel, ride comfort and handling stability in comparison with other existing methods.     

基于GSAGA的模糊PID控制在自动油门系统中的应用研究

针对现代民用飞机非线性和时变的特点,设计了一种用于民机自动油门控制系统的模糊PID控制器;模糊控制器以速度跟踪误差及其微分信号作为输入调节PID控制器的比例、积分及微分参数,进而控制油门开度以调节发动机推力,最终实现对速度的控制;文中进一步采用广义自适应遗传算法(GSAGA)对模糊PID控制器的输出因子进行优化,在着陆模态下采用所设计的优化控制策略与传统模糊PID控制进行了对比仿真,仿真结果显示,在民机自动油门控制系统中基于GSAGA的模糊PID控制器的控制效果优于传统模糊控制方法,仿真结果符合飞行品质指标,控制效果良好。     

基于粒子群算法的自动着陆非线性PID控制器

针对线性PID控制器在自动着陆控制中难以达到满意的控制效果的缺点,在分析各增益参数与偏差信号之间非线性关系的基础上,设计了一种P/I/D各部分参数根据控制与误差之间的调节规律连续变化的非线性PID控制器。为了选择合适的非线性PID控制器的权系数,利用粒子群算法对其各参数进行寻优整定。针对某型飞机自动着陆系统,给出了设计的一般步骤。仿真结果表明,与线性PID控制器相比,设计的非线性PID控制器能更准确地跟踪期望轨迹并满足着陆指标的要求,并具有良好的动态品质。     

非独立悬架车辆自激摆振的数值仿真分析

基于一个三自由度的转向系统模型,利用数值仿真方法分析了横拉杆刚度、主销后倾角、转向机刚度、轮胎侧偏刚度、轮胎拖距、转向机阻尼、绕主销当量阻尼等参数对载重汽车自激型摆振的影响.仿真分析的结果表明,上述参数发生变化时,可诱发自激摆振,但车速也是影响摆振的关键因素之一.在确定的系统参数和车速下,初始激励不仅可能诱发稳定的自激摆振,还可能是发散的运动.与受迫型自激摆振不同,自激型摆振的频率变化与车速的变化并不一致.     

Fuzzy modelling control for aircraft automatic landing system

Aircraft landing control based on fuzzy modelling networks is presented. The proposed scheme uses a fuzzy controller combined with a linearized inverse aircraft model. A multi-layered fuzzy neural network is used as the controller, providing the control signals at each stage of the aircraft-landing phase. The algorithm used to train the network is the Backpropagation Through Time. The linearized inverse aircraft model provides the error signals that will be used to back-propagate through the controller at each stage. The objective of this study is to improve the performance of conventional automatic landing systems. The simulation results are described for the automatic landing system of a commercial aeroplane. Tracking performance and robustness are demonstrated through software simulations. Simulation results show that the fuzzy controller can successfully expand the safety envelope to include more hostile environments such as severe turbulence.     

Intelligent automatic landing system using time delay neural network controller

This paper presents an intelligent automatic landing system that uses a time delay neural network controller and a linearized inverse aircraft model to improve the performance of conventional automatic landing systems. The automatic landing system of an airplane is enabled only under limited conditions. If severe wind disturbances are encountered, the pilot must handle the aircraft due to the limits of the automatic landing system. In this study, a learning-through-time process is used in the controller training. Simulation results show that the neural network controller can act as an experienced pilot and guide the aircraft to a safe landing in severe wind disturbance environments without using the gain scheduling technique.