飞机防滑刹车控制方法综述

飞机防滑刹车系统具有复杂动态特性,防滑刹车控制器的性能对飞机着陆的安全性具有重要作用。针对此问题,回顾了飞机防滑刹车系统的发展历程和主要控制方式,分析了飞机刹车系统的动态特性及影响刹车性能的主要因素。评述了基于数学模型的传统控制方法和基于模糊控制、神经网络等人工智能技术的智能控制方法在机轮防滑刹车控制中的研究与应用情况,并探索了当前防滑刹车控制方法研究中所面临和需要解决的关键问题,展望了未来控制系统的发展趋势。     

RBF-PID控制在防滑刹车系统中的应用研究

为了研究飞机防滑刹车系统,在分析滑移率控制式飞机防滑刹车系统的工作原理基础上,将基于RBF神经网络算法的PID控制方法引入飞机防滑刹车系统中,实现最佳滑移率式的飞机防滑刹车控制.以某型飞机为例,针对不同的跑道(干、湿、冰)情况,将该方法和传统的PID控制方法在MATLAB环境下进行了数字仿真,仿真结果表明:基于RBF神经网络PID的控制方法较传统的PID控制方法,有更好的刹车控制效果,并具有较强的鲁棒性;采用基于滑移率式的RBF神经网络PID控制可以大大地提高飞机防滑刹车效率,为飞机防滑刹车系统的控制提供一条新的思路.     

基于非线性干扰观测器的飞机全电刹车系统滑模控制设计

飞机防滑刹车具有典型的强非线性、强耦合和参数时变等特点, 并且跑道环境的干扰容易对飞机的地面滑跑性能造成不利影响. 本文提出了一种基于非线性干扰观测器的飞机全电防滑刹车系统滑模控制设计方法. 首先, 考虑了实际刹车不确定性干扰条件下的防滑刹车动力学建模问题, 通过对高阶非线性刹车系统进行反馈线性化处理, 简化了基于严格反馈的模型. 其次, 基于对主轮打滑原因的深入分析, 设计了非线性干扰观测器对干扰进行在线估计, 并在控制律设计中引入补偿部分. 通过构造递归结构的快速终端滑模控制器来跟踪实时变化的最佳滑移率并建立稳定性条件, 实现了飞机全电防滑刹车系统的有限时间快速稳定并有效抑制了主轮锁定打滑. 通过在不同跑道状态下进行模拟仿真, 验证了本文提出的飞机防滑刹车控制策略可以有效地提高刹车效率.     

基于模糊PID的飞机防滑刹车系统动态仿真

在飞机防滑优化控制问题的研究中,存在飞机防滑刹车系统响应速度慢、抗干扰能力差等问题。由于飞机着陆时发动机处在慢速工作状态,防滑系统处于复杂的非线性过程。为提高刹车效率,提出将遗传算法优化的模糊控制应用到飞机防滑刹车系统中。采用遗传算法对采用"串联二进制编码"的隶属函数参数进行联合优化,并将优化过的控制规则用于设计模糊控制器。将设计的控制器和刹车系统模型在Simulink环境下进行数字仿真。仿真结果表明,遗传算法的模糊PID控制,在飞机防滑刹车控制中,具有良好的控制效果和抗干扰能力,为防滑刹车系统控制设计提供了一条新的手段。     

基于自适应线性自抗扰控制的飞机防滑刹车系统重构控制

飞机防滑刹车系统是确保飞机安全起飞、着陆和滑跑的重要航空机电系统. 除了其动力学中的强非线 性、强耦合以及参数时变外, 潜在的执行器等组件故障也会严重降低防滑刹车系统的安全性与可靠性. 为满足故障 及扰动状态下系统的性能需求, 本文提出了一种基于自适应线性自抗扰控制的飞机防滑刹车系统重构控制方法. 根据飞机防滑刹车系统的组成结构及工作原理对其进行数学建模, 并对执行器注入故障因子. 设计了自适应线性 自抗扰重构控制器, 同时分析了整个闭环系统的稳定性. 该控制器将组件故障、外部干扰以及测量噪声等视为总扰 动, 根据状态误差反馈和系统输出信息, 利用BP神经网络在线优化更新扩张状态观测器和状态误差反馈律参数, 从 而更精确地观测与补偿总扰动带来的不利影响. 最后, 在不同跑道环境下的仿真结果验证了所提出重构控制器的适 应性和鲁棒性.     

基于T-S模糊神经网络的飞机防滑刹车系统研究

航空业的发展对飞机防滑刹车系统提出了更高的要求,而传统PID+PBM控制器存在着低速打滑、刹车效率较低等问题;针对刹车过程中的不确定性和非线性问题,提出采用T-S模糊神经网络来进行防滑刹车控制器设计;在MATLAB/SIMULINK平台建立飞机刹车总体仿真模型,将设计的控制器与传统控制器进行对比仿真试验;仿真结果表明,基于T-S模糊神经网络的控制器解决了传统PID+PBM系统存在的问题,具有良好的控制效果,系统具有鲁棒性,能够适应变化的跑道情况,为飞机防滑刹车控制提供了一种新的方法。     

一种混合控制算法在飞机防滑刹车系统中的应用研究

由于飞机防滑刹车系统的复杂性和非线性,建立其准确的动力模型比较困难。常规的PID控制算法因过于依赖于模型的精确性,对飞机防滑刹车系统的控制难以达到预期效果。基于以上因素,以飞机防滑刹车系统为对象,提出了一种新的神经网络模糊PID混合控制算法,并在惯性模拟实验台测试。实验结果表明：采用此控制算法,提高了刹车的效率．缩短了刹车距离．提升了飞机制动性能,增强了系统的鲁棒性。     

基于模糊神经网络的飞机防滑刹车系统研究

以某型飞机刹车系统为研究对象,为了使该系统以最佳滑移率工作,防止陷入深度打滑和获得最大的刹车结合系数,提出了一种智能飞机防滑刹车系统的设计方案,制定出刹车控制规律并对整个刹车系统进行了仿真。改进了现有飞机刹车防滑系统的控制算法,应用神经网络BP算法实时获取最佳滑移率,利用模糊神经网络实现快速逼近给定滑移率,并采用基于数字信号处理器（DSP）的硬件电路实现了智能刹车控制。实验结果表明,飞机防滑刹车效率有了明显改进,鲁棒性增强。     

飞机刹车模糊神经网络DSP嵌入式控制系统

押对现有飞机刹车防滑系统的控制算法进行了改进,采用了神经网络的BP算法和模糊实时控制,并用数字信号处理器(DSP)在嵌入式系统中实现了神经网络算法。结果表明,飞机防滑刹车效率有了明显改进,鲁棒性增强。     

飞机防滑刹车系统模糊滑模控制研究北大核心

针对现在飞机广泛应用的“PD+PBM”控制律难以对具有强非线性和不确定性的飞机防滑刹车系统进行高性能控制的问题,提出飞机防滑刹车系统基于模糊指数趋近律的滑模变结构控制律。先建立具有不确定扰动的飞机防滑刹车系统的地面动力学模型以消除模型误差所带来的不利影响,然后设计了基于指数趋近律的滑模变结构控制律以改善控制性能,并利用李雅普诺夫定理证明了系统的稳定性,最后基于模糊理论对滑模控制律进行优化以抑制抖振。仿真结果表明,基于模糊指数趋近律的滑模变结构控制律控制效果优于“PD+PBM”控制律和传统的滑模控制律,抑制控制器输出抖振效果良好,刹车效率高,控制方法合理有效。     

Intelligent anti-skid brake controller using a neural network

The performance of current anti-skid brake controllers on aircraft becomes degraded due to the uncertain nature of the runway conditions. This paper presents the design of an intelligent anti-skid neural controller to overcome this problem. Their learning ability, nonlinear mapping ability and pattern-recognition capability are the features of neural networks that are ideally suited to the design of intelligent controllers. The controller described here identifies the runway condition from the aircraft-wheel responses, and modulates the brake torque for optimum braking. The proposed controller exhibits robustness under variations in brake characteristics and runway conditions. Simulation results confirm the satisfactory performance of the controller in adapting to changes in runway conditions.     

基于神经网络的飞机静液刹车系统设计

针对无人机的滑跑安全问题,为有效缩短刹车距离,提高刹车效率,设计了一种新型的静液刹车系统;根据新型刹车系统的特点,并综合考虑飞机机体、机轮、跑道状况的特性,以及刹车系统的非线性和不确定性,难以精确控制的特点,设计了神经网络控制器(NNC);并将神经网络控制器,新型刹车系统和飞机滑跑模型应用于仿真环境,建立了整体的仿真模型;仿真结果表明,采用神经网络的刹车系统鲁棒性增强,刹车效率提高,明显优于采用传统控制律的刹车系统。     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

考虑非线性因素的飞机防滑刹车系统控制

飞机的刹车过程存在较强的非线性,目前广泛应用的速度差加压力偏调式(PBM)控制律难以实现对飞机刹车的高性能控制.本文提出了一种考虑飞机刹车过程中非线性因素的滑模控制律.首先建立考虑轮胎跑道非线性和刹车盘摩擦系数非线性的的飞机防滑刹车系统非线性模型,然后设计了滑模观测器对飞机速度进行估计,并在此基础上设计了一种滑模变结构控制律,最后基于模糊理论对滑模控制律进行优化,从而抑制控制器的抖振.仿真结果表明,基于模糊指数趋近律的滑模变结构控制律控制效果优于传统“PD+PBM”控制律,抑制控制器输出抖振效果良好,能够很好的适应刹车过程中的复杂非线性因素,刹车效率高,控制方法合理有效.     

A fuzzy logic controller for an ABS braking system

Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy, d) On rough roads, the tire slip ratio varies widely and rapidly due to tire bouncing, and e) The braking system contains transportation delays which limit the control system bandwidth. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal, based on current and past readings of the slip ratio and brake pressure. The controller detects wheel blockage immediately and avoids excessive slipping. The ABS system performance is examined on a quarter vehicle model with nonlinear elastic suspension. The parallelity of the fuzzy logic evaluation process ensures rapid computation of the controller output signal, requiring less time and fewer computation steps than controllers with adaptive identification. The robustness of the braking system is investigated on rough roads and in the presence of large measurement noise. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance on various road types and under rapidly changing road conditions     

无人机全电式自主刹车系统滑模极值搜索控制

常规主动刹车系统采用在线辨识跑道特征的算法,但仍需依赖摩擦模型先验知识,难以应对复杂跑道工况.为克服上述问题,提出一种滑模极值搜索控制策略并应用于无人机全电式自主刹车系统.考虑电动作动机构非线性特性,建立系统的状态空间模型并合理简化为严格反馈形式,采用超扭曲算法估计结合系数的梯度,结合反馈线性化控制律得到刹车压力参考值,证明此控制作用下可实现对未知最优滑移率的渐近跟踪.采用反演控制的思想设计无抖振滑模控制器实现对参考刹车压力的跟踪.利用Lyapunov方法获得系统的渐近稳定性条件并分析控制参数对系统的影响.半实物仿真试验结果表明控制策略的有效性.     

Neural-network hybrid control for antilock braking systems

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.     

基于滑模变结构的飞机防滑刹车控制器设计

以飞机全电刹车为研究背景,采用滑模变结构控制策略,设计刹车防滑控制策略,解决传统刹车效率低、机轮深度打滑、低速刹车性能差等问题.在控制策略中,以最佳滑移率为目标函数,设计滑模面,实现刹车防滑控制.由于滑模控制的强鲁棒性,可有效提高系统的抗干扰能力.仿真结果可知,滑移率控制在最佳滑移率附近,刹车效率高,可消除机轮深度打滑现象,防滑效果优良.     

Backstepping dynamic surface control for an anti-skid braking system

The electric aircraft landing system, as one of the important components of more electric aircraft (MEA) and all electric aircraft (AEA), has been a subject of interest in recent years. An anti-skid braking system (ABS), which is the crucial component of the electric aircraft landing system, has the function of regulating the wheel slip ratio such that the braking process operates in a stable state. In this paper, an approach that combines a nonlinear backstepping dynamic surface control (DSC) and an asymmetric barrier Lyapunov function (ABLF) is presented to not only track the reference slip ratio but also to avoid the slip ratio in the unstable region. We demonstrate that the proposed controller can guarantee the boundedness of the output constraints and the stability of the overall system. Using the ABLF allows one to relax the required initial conditions on the starting values of the wheel slip ratio and subsequently make the wheel slip constraints more flexible for various runway surfaces and runway transitions. The DSC is introduced to eliminate repeated differentiation resulting from ABLF synthesis, which can relax the restrictions on the high-order differentiability for stabilizing functions and the high power of wheel slip tracking error transformation. The proposed controller can avoid the negative effects of disturbance produced by repeated differentiation and can construct a simple controller for wheel slip control. The results of simulations with varying runway surfaces have validated the effectiveness of the proposed control scheme, in which the output constraints on the wheel slip ratio are guaranteed not to be violated and self-locking is avoided.