基于自适应二阶终端滑模的飞行器再入姿态控制

针对飞行器再入过程中存在着模型不确定性因素以及气动环境复杂等鲁棒控制问题,提出一种基于自适应二阶非奇异终端滑模的控制方案.设计的控制器保证姿态跟踪误差在有限的时间内收敛于零,不需要内外扰的先验知识,通过在线自适应辨识扰动上界以消除其影响.最后以气动参数摄动50%作为扰动条件进行了飞行器再入姿态控制仿真,结果表明了该方案的快速性和鲁棒性.     

飞行器偏角和倾斜角控制优化算法的仿真分析

无人驾驶的飞行器在偏角和倾斜角控制过程中,角度控制的滞后性问题一直无法解决,高速飞行使得飞行器角度控制再入过程中的动压、过载、热流等物理约束过程复杂化,利用传统的PID飞行参数控制方法对飞行器进行控制,为了保证精度,仅仅通过改变航向角的方式来进行转弯偏角和倾斜角控制,导致转弯半径很大,无法保障控制精度.提出利用改进PID神经网络算法的飞行器偏角和倾斜角控制优化模型.根据飞行器控制系统及动力学原理获取飞行过程的动力方程,构建飞行器角度控制数学模型,根据改进后的PID神经网络算法进行最优角度值求解,降低控制系统的跟踪误差、保证算法的收敛性,实现飞行器角度控制模型的优化.实验结果表明,利用改进算法进行飞行器控制模型的优化,能够保证飞行器飞行过程的航偏角和倾斜角控制精度.     

全状态受限飞行器的自适应有限时间姿态控制

针对全状态受限飞行器系统的姿态跟踪,构建了一种新的自适应有限时间跟踪控制器,以消除经典反步法中由于对虚拟信号求导所导致的计算复杂性问题,并保证飞行器姿态系统中所有状态均限制在设计区域内。对于飞行器系统非线性模型中的未知部分,采用神经网络进行逼近。在此基础上,设计了误差补偿机制用于补偿滤波误差,并使用障碍李雅普诺夫函数证明了尽管存在输入饱和,但是飞行器的姿态仍然可以在有限时间内以一定的精度跟踪期望信号。最后,对该控制方法进行了仿真验证。     

非脆弱递归滑模动态面自适应神经网络控制

针对一类非匹配不确定非线性系统的跟踪控制问题, 提出了一种递归滑模动态面自适应控制算法. 采用神经网络(neural network, NN)在线逼近系统不确定项, 通过设计递归滑模动态面有效综合反推步骤中每步跟踪误差之间相互影响和制约的关系. 该方法避免了反推法存在的“微分爆炸”问题, 克服了传统动态面方法对其低通滤波器时间常数和神经网络自适应参数摄动脆弱的缺点. 稳定性分析证明了该方法能够保证闭环系统所有状态半全局一致最终有界, 且跟踪误差可以收敛至原点的任意小邻域.     

基于模糊前馈的ASV再入姿态的模糊鲁棒跟踪控制方法

提出一种模糊鲁棒跟踪控制方法,并应用于研究空天飞行器(ASV)再人段姿态角的跟踪问题.基于ASV再入段存在外界干扰的不确定姿态动态系统的T-S模糊模型,考查姿态角跟踪参考信号的跟踪误差,引入模糊前馈,得出跟踪误差指数稳定的约束条件.并在镇定控制是前馈控制的先决条件的前提下,研究模糊前馈跟踪控制器和具有极点约束的H∞模糊镇定控制器的设计问题,基于Matlab的线性矩阵不等式(LMI)和模糊逻辑控制(FLC)工具可实现此问题的求解.仿真结果验证本文算法的有效性.     

载人登月飞行器高速返回再入制导技术研究

返回再入段是载人登月任务完成后,保证宇航员安全返回地球的关键阶段,其跳跃式再入制导方法的研究是我国载人登月任务需要突破的一项重要关键技术.由于探月返回飞行器速度极高,其弹道特性与神舟飞船一类的近地轨道返回的飞行器有较大差别,也给制导导航与控制(简称GNC,以下同)系统设计带来较大挑战.与无人再入飞行器相比,载人飞行器需要具备防过载超限能力、大范围再入航程适应能力、高精度落点控制能力.为了满足上述要求,本文提出了一套基于全系数自适应校正的预测制导方案.在再入前,通过对基本倾侧角进行校正,提高了规划弹道对再入初始条件散布的适应性;再入后利用外环的预测与全系数自适应校正实现对规划弹道的持续修正,保证规划弹道与飞行器状态的匹配性,内环则采用短周期的弹道跟踪制导;对于横向制导,本文给出和比较了采用实时漏斗制导律、独立预测–校正制导律方案以及横航向独立自校正和耦合自校正方案.     

互补滤波算法在四旋翼飞行器姿态解算中的应用

针对四旋翼飞行器飞行过程中的姿态最优估计问题,本着准确、快速的原则,选择了基于陀螺仪、加速度计和电子罗盘的捷联式惯性测量系统.由于这些传感器存在温度漂移和噪声干扰等问题,采用互补滤波算法,通过融合IMU多传感器的数据信号,对测得的姿态数据进行补偿修正,解算出高精度的姿态角.为了验证互补滤波算法的有效性和实用性,通过实际的四旋翼飞行器角度测量系统对互补滤波算法展开研究.结果表明姿态角解算中采用互补滤波算法能够快速、稳定的输出高精度姿态数据,姿态角最大跟踪误差控制在±2°以内,满足四旋翼飞行器飞行控制的要求,成功完成了姿态的最优估计.     

基于模糊控制的高超声速飞行器二阶滑模姿态控制

考虑高超声速飞行器飞行过程中气动参数变动导致的不确定,将模糊控制与二阶滑模控制相结合,形成自适应模糊二阶滑模控制器,用于控制高超声速飞行器姿态的飞行系统中.依据奇异摄动理论,设计快速和慢速双闭环系统控制角速率和姿态角.设计二阶滑模控制器用于有效地衰减抖振,同时对姿态角指令实现准确和快速跟踪.采用自适应模糊逻辑逼近高超声速飞行器动力学和运动学模型中的不确定部分,以对控制器进行有效补偿,基于Lyapunov稳定性理论,推导模糊规则参数的自适应律,确保整个闭环控制系统的稳定.仿真结果表明,所提出的高超声速飞行器的自适应模糊滑模控制系统能够有效抑制气动参数摄动的影响,对姿态角指令有较好的跟踪性能.     

高超声速飞行器姿态自适应解耦控制方法研究

研究飞行器优化姿态控制问题,高超声速飞行器具有的快时变、非线性、强耦合特性给姿态控制系统设计带来一定难度.针对飞行器的特性分析,将姿态动力学模型分解为姿态角与角速度跟踪的内、外两回路,采用动态逆方法设计了双回路控制系统结构,从而在实现完全解耦的同时有效降低了设计难度.同时针对动态逆方法过于依赖精确数学模型的局限性,设计PID神经网络控制器,利用神经网络的无限逼近能力调整自身网络权重矩阵参数值,使控制器对不确定因素与未知干扰具有一定的自适应能力.在标称和拉偏情况下进行仿真,结果表明,控制姿态角的跟踪超调量可在1.5％以内,侧滑角的耦合量不足1度,满足对飞行器控制优化的要求.     

基于自适应动态规划的非线性鲁棒近似最优跟踪控制

为克服现有近似最优跟踪控制方法只能跟踪连续可微参考输入的局限,本文针对一类具有未知动态的连续时间非线性时不变仿射系统,提出了一种新的基于自适应动态规划的鲁棒近似最优跟踪控制方法.首先采用递归神经网络建立系统模型,然后建立评价神经网络对最优性能指标进行估计,从而得到最优性能指标偏导数的估计值,进而得到近似最优跟踪控制器,最后利用系统输出与参考输入之间的跟踪误差设计鲁棒项对神经网络建模误差进行补偿.分别针对两个非线性系统进行仿真实验,仿真结果表明了所提方法的有效性和优越性.     

Integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control for Lead-Wing close formation systems

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm.     

Neural network based adaptive fuzzy PID-type sliding mode attitude control for a reentry vehicle

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and external disturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilization of radial basis function (RBF) neural network. In order to improve the transient performance and ensure small steady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by using adaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loop system is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term of sliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of a priori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbance information. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numerical simulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving the transient performance and robustness.

     

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network (RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle (NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances.      

高速转向工况下的无人驾驶车辆路径跟踪

为提高无人驾驶车辆在高速转向工况下的路径跟踪精度与行驶稳定性,基于三自由度单轨车辆模型与模型预测控制理论,分析前轮转角约束对车辆跟踪精度与行驶稳定性的影响,提出一种自适应于侧向附着力的路径跟踪控制方法.以Pacejka'89魔术公式轮胎模型为基础,分析轮胎纵向受力,以此推算轮胎的侧向附着力,从而建立前轮转角约束随车辆状...     

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.     

基于自适应MPC算法的轨迹跟踪控制研究

为了保证智能车辆在低附着且变速条件下跟踪控制的精确性和稳定性,提出一种基于自适应模型预测控制（MPC）的轨迹跟踪控制算法。针对低附着条件下轨迹跟踪存在行驶稳定性较差的问题,对车辆动力学模型添加侧偏角软约束,分别设计有无添加侧偏角约束的MPC控制器。仿真结果表明,添加侧偏角约束后MPC控制器性能更优,车辆行驶稳定性得到有效提高。在此基础上,又提出了一种自适应的轨迹跟踪控制策略,能够根据车辆速度的变化,实时产生预测时域[(Hp)],分别设计自适应的MPC控制器与4组定值[Hp]的MPC控制器。仿真结果表明,基于自适应模型预测控制的轨迹跟踪控制算法在提高低附着且变速条件下智能车辆轨迹跟踪控制的精度和稳定性方面具有一定的有效性和先进性。     

基于阿克曼原理的4WID/4WIS汽车循迹控制研究

针对四轮独立驱动、独立转向汽车循迹控制精度和转向稳定性兼容问题,同时考虑减小轮胎磨损,延长轮胎使用寿命,本文基于阿克曼转向原理和RBF神经网络PID理论,提出了一种自适应的循迹控制方法.首先,设计了基于RBF神经网络PID理论的自适应转向控制器,用于控制前内轮转角,保证循迹精度;其次,后内轮以减小质心侧偏角为目标进行辅助转向,保证转向稳定性;接着,基于阿克曼转向原理,确定外轮转角,保证各轮侧偏力分配合理;最后,采用同一瞬心法,确定各车轮转速,以减小轮胎滑动率.本文搭建了CarSim和MATLAB/Simulink联合仿真平台,进行了仿真实验,结果表明：本文提出的循迹控制方法,不仅能获得较小的循迹偏差和质心侧偏角,保证了足够的循迹控制精度和转向稳定性,同时还减小了轮胎滑动率,有利于减小轮胎的磨耗.     

基于复合执行机构的再入弹头自适应反演控制

为了提高再入弹头命中精度和机动突防能力, 将质量滑块和单框架控制力矩陀螺(SGCMG) 配合使用, 以在弹头再入全过程中产生足够的姿态控制力矩. 针对再入系统物理参数及外界环境干扰的不确定性, 利用反演方法设计再入弹头姿态自适应控制器. 该控制器可以对转动惯量不确定性进行自适应补偿, 并且有效抑制力矩干扰对姿态控制系统的影响. 对某型再入弹头的仿真研究表明, 所提出的控制器可以实现姿态角的良好跟踪.

     

Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.      

基于控制性能的MIMO非线性系统动态面控制

针对一类不确定非线性MIMO(multiple-input multiple-output)系统,在动态面控制方法的基础上,提出了自适应跟踪控制方案.通过引入性能函数和输出误差转换,保证输出信号具有指定的跟踪速度、跟踪误差、最大超调量.为了避免控制奇异问题,采用神经网络直接逼近期望控制信号.该方案无需估计神经网络的权值,仅对1个参数进行自适应律设计.理论证明了闭环系统所有信号有界,仿真结果验证了所提方案的有效性.