基于自适应迭代学习控制的列车自动驾驶算法

针对高速列车自动驾驶系统受到时变外部扰动和受限状态的情况,提出一种基于迭代学习控制的自适应控制算法. 基于Lyapunov 函数,利用列车运行过程中的状态偏差,推导出自适应迭代学习控制律和参数学习更新律. 构造类Lyapunov 函数的复合能量函数,通过迭代域的差分,证明其差分负定性和收敛性. 采用所提控制算法对列车跟踪性能进行计算机仿真和实例仿真验证,结果表明,所提出的自适应迭代学习控制算法对列车期望曲线跟踪具有较高的精度和较快的收敛速度,能够在较短的迭代次数实现对期望曲线的精确跟踪.     

基于预测控制的动车组迭代学习控制方法

针对动车组运行过程中存在非线性扰动、参数时变等问题，以提高动车组的速度跟踪精度和乘客舒适性要求为目标，提出了一种基于预测控制的高速动车组迭代学习控制方法；通过采集动车组先前运行过程中的输入输出数据，使用带遗忘因子的最小二乘法实时辨识广义预测控制(GPC)中的预测模型参数并计算预测输出，根据以往过程的平均模型误差修正该预测输出，利用修正后预测输出引出迭代学习控制律，在线实时计算得到新的控制量，实现动车组速度跟踪；采用修正后预测输出设计二次型迭代学习控制律，通过充分学习列车系统的重复性特性来解决传统比例积分微分(PID)型迭代学习参数整定难、收敛速度慢和鲁棒性差等问题，并给出算法的收敛性证明；以实验室配备的CRH380A型动车组半实物仿真平台对该方法进行了测试，建立了列车的三动力单元模型，使其跟踪设定速度曲线，并与一些传统算法进行对比。仿真结果表明：在第8次迭代过程，基于预测控制的高速动车组迭代学习控制方法得到的动力单元速度与其设定的速度和加速度误差分别在0.3 km·h^-1和0.5 m·s^-2以内，且变化平稳，其性能优于PID、GPC和P型迭代...     

对一类非严格重复系统的两种基于二阶内模的迭代学习控制器设计

通过对系统中的不确定参数建立迭代域上的二阶内模来研究一类非线性连续时间系统的非严格重复性,并依照内模原理提出了两种基于二阶内模的参数迭代学习控制器：二阶迭代学习控制器和平行迭代学习控制器．本文分别使用不同的Lyapunov函数,证明了两种控制器在各自的可行范围内都可以保证系统的跟踪误差收敛．通过对这两种学习机制的比较分析,说明了平行迭代学习控制设计的合理性．两个数值仿真不仅证实了两种算法的有效性,也展示了平行迭代学习控制器更好的收敛特性．     

Guaranteed Cost Iterative Learning Control for Multi-Phase Batch Processes

Batch process is a typical multi-phase process. Due to the interaction between the phases of the batch process, high precision control in a single phase cannot guarantee high precision control of the whole batch process. In order to solve this problem, the guaranteed cost iterative learning control (ILC) of multi-phase batch processes is studied in this paper. Firstly, through introducing the output error, the state error and the extended information, the multi-phase batch process is transformed into an equivalent 2D switched system which has different dimensions. In addition, under the measurable condition, the guaranteed cost iterative learning control law with extended information is designed. The proposed control law ensures not only the stability of the system but also the optimal control performance. Next, in order to study the stability of the system and the minimum running time under the condition of stable running, the multi-Lyapunov function method is used. By means of the average dwell time method, the sufficient conditions ensuring system to be exponentially stable are given in the form of linear matrix inequality (LMI). Finally, the injection molding process is taken as an example to make simulation, which shows the feasibility and effectiveness of the proposed method.     

车辆多目标自适应巡航显式模型预测控制

为了兼顾车辆自适应巡航控制(ACC)系统的跟踪控制效果和实时性, 提出了基于显式模型预测控制(EMPC)理论的车辆多目标自适应巡航控制方法; 基于车辆间运动学关系建立自适应巡航控制运动学模型, 根据预测控制理论推导预测时域内的跟踪误差预测模型, 并确定车辆安全性、跟踪性、经济性和舒适性等多性能目标函数和约束条件; 运用显式模型预测控制中的多参数规划理论, 将基于反复在线优化计算的闭环模型预测控制系统转化为与之等价的显式多面体分段仿射(PPWA)系统, 通过离线计算获得期望加速度与距离误差、速度误差、自车加速度和前车加速度等状态变量之间的最优控制律, 并设计在线查表的搜索流程, 通过定位当前状态所处分区, 并应用该分区的显式控制律实现自适应巡航控制; 进行了纵向跟踪工况仿真验证, 并与传统MPC-ACC控制方法进行对比。对比结果表明: 在前车正弦加减速工况下, EMPC-ACC控制器单步运算速度比MPC-ACC控制器平均提升了53.51%, EMPC-ACC控制下的平均距离跟踪误差为0.220 3 m, 平均速度误差为0.340 1 m·s-1; 在前车阶跃加减速工况下, EMPC-ACC控制器单步运算速度比MPC-ACC控制器平均提升了72.96%, EMPC-ACC控制下的平均距离跟踪误差为0.331 9 m, 平均速度误差为0.399 1 m·s-1。可见, 提出的EMPC-ACC控制算法在保证纵向跟踪性能的前提下, 有效地提高了自适应巡航控制的实时性。      

融合稳定性的高速无人驾驶车辆纵横向协调控制方法

提出了一种纵横向协调控制的路径跟踪控制方法; 建立了车辆预瞄误差模型和考虑路面地形的高速车辆等效动力学模型, 以此引入道路曲率地形因素; 基于模糊规则设计了预瞄距离发生器, 解决预瞄误差模型中固定预瞄距离的问题; 建立了预测时域与道路曲率的函数关系, 运用模型预测控制算法求解前轮转角, 从而建立路径跟踪控制器; 运用指数模型表示车辆期望车速, 设计了比例积分微分纵向控制器控制车速以改善路径跟踪精度; 运用质心侧偏角相平面图表征车辆稳定性特征, 设计比例积分微分稳定性控制器以改善车辆稳定性。研究结果表明: 提出的控制方法能在不同附着系数路面上对车辆跟踪性能进行优化, 在干燥沥青路面以车速90 km·h-1行驶时, 与只运用模型预测控制算法进行路径跟踪控制的车辆相比, 最大横向误差可减少33%;在潮湿沥青路面以车速70 km·h-1行驶时, 与只运用模型预测控制算法进行路径跟踪控制的车辆相比, 最大横向误差可减少30%;在冰雪路面以车速55 km·h-1行驶时, 与只运用模型预测控制算法进行路径跟踪控制的车辆相比, 最大横向误差可减少16%。可见, 所提出的控制方法能有效改善路径跟踪精度。      

Finite-Time Attitude Tracking Control of Spacecraft with Actuator Saturation

The attitude tracking control problem of a rigid spacecraft with actuator saturation is investigated in this paper. A finite-time attitude tracking control scheme is presented by incorporating sliding mode control (SMC) and adaptive technique. Specifically, a novel time-varying sliding mode manifold is first developed that aims at regulating the attitude tracking error to equilibrium point within a certain finite time. Moreover, it can be specified a priori by the designer according to the mission requirement. Subsequently, an adaptive controller is derived by using the SMC in conjunction with adaptive technique. The designed controller is capable of ensuring that the state trajectories reach to sliding regime within a finite time, and hence that attitude tracking error can converge to zero in a finite time with the aid of the developed sliding dynamics, despite the presence of exogenous disturbances, unknown inertia properties and saturation nonlinearities. Finally, the simulation experiments are carried out to demonstrate the effectiveness of the proposed control scheme.     

基于强化学习的智能车人机共融转向驾驶决策方法

针对智能车人机共融驾驶系统中人和自主驾驶系统的驾驶权连续动态分配问题,尤其是因建模误差导致的权重分配方法适应性低的难题,提出了基于强化学习的人机共融转向驾驶决策方法;考虑驾驶人的转向特性,搭建了基于双点预瞄的驾驶人模型,并采用预测控制理论建立了智能车自主转向控制模型,构建了智能车人机同时在环的转向控制框架;基于Actor-Critic强化学习架构,设计了用于人机驾驶权分配的深度确定性策略梯度(DDPG)智能体,以曲率契合度、跟踪精确性和乘坐舒适性为目标,提出了基于模型的收益函数;构建了人机共融驾驶权分配强化学习框架,包含驾驶人模型、自主转向模型、驾驶权分配智能体以及收益函数;为了验证方法的有效性,招募了8位驾驶人开展共计48人次的模拟驾驶试验。研究结果表明:在曲率适应性验证中,人机共融-DDPG方法优于人工驾驶和人机共融-Fuzzy方法,跟踪性平均提升70.69%、39.67%,舒适性平均提升18.34%、7.55%;在速度适应性验证中,车速为40、60和80 km·h-1条件下,驾驶人权重大于0.5的时间占比分别为90.00%、85.76%、60.74%,且跟踪性相轨迹和舒适性相轨迹都能有效收敛。可见,提出的方法能够适应曲率和车速变化,在保证安全性的前提下提升了跟踪性和舒适性。      

Real-time follow-up head tracking in dynamic complex environments

In the modes of both object motion and camera motion, an enhanced Camshift algorithm, which is based on suppressing similar color features of background and on joint color probability density distribution image, is proposed to real-time track head in dynamic complex environment. The system consists of face detection module, head tracking module and camera control module. When tracking fails, a self-recovery mechanism is introduced. At first the Adaboost face detector based on Haar-like features is implemented to find frontal faces, the false positive is filtered according to the skin color criterion, and the true face is used to initialize the tracking module. In hue saturation value (HSV) colorspace, the hue-saturation (H-S) histogram of face skin and the saturation-value (S-V) histogram of hair are built to produce the joint color probability density distribution image, and this is intended to realize the head tracking with arbitrary pose. During tracking, region of interest (ROI) is introduced, and the color probability density distribution of a specified background area outside the ROI is learned, similar color features in the head are suppressed according to the learning result. The background suppression step is intended to resolve the problem that the tracker maybe fails when the head is distracted by backgrounds having similar colors with the head. A closed loop control model based on speed regulation is applied to drive an active camera to center the head. Once tracking drift or failure is detected, the system stops tracking and returns to the face detection module. Our experimental results show that the presented system is well suitable for tracking head with arbitrary pose in dynamic complex environments, also the active camera can track moving head smoothly and stably. The system is computationally efficient and can run in real-time completely.     

基于误差交叉耦合的多电磁铁悬浮系统滑模协同控制

针对磁浮列车传统的单点悬浮控制方法没有考虑多电磁铁间的协调同步问题,将多电磁铁的跟踪误差交叉耦合来设计高精度的协同控制方法,在减少了多点悬浮系统的跟踪误差和同步误差的同时,提高了系统抗干扰能力. 首先,通过动力学分析了考虑未知扰动的4个电磁铁（2个控制模块）悬浮系统的动力学特征;其次,针对系统中的未知扰动,引入干扰观测器来估计扰动并进行扰动补偿;然后,考虑到相邻电磁铁控制模块之间存在耦合动力学特性,设计一种误差交叉耦合的滑模协同控制器;最后,在不作任何线性化处理的前提下,证明了闭环系统的渐近稳定性. 研究结果表明:通过多电磁铁的悬浮架实验证明所提方法可以考虑补偿电磁铁模块之间的协调关系,抑制扰动的影响,减小间隙跟踪误差达40%,显著减少了电磁铁之间的耦合扰动作用.      

Arranging transient process used in iterative learning control system

Considering the same initial state error in each repetitive operation in the iterative learning system, a method of arranging the transient process is given. During the current iteration, the system will track the transient function firstly, and then the expected trajectory. After several iterations, the learning system output will trend to the arranged curve, which has avoided the effect of the initial error on the controller. Also the transient time can be changed as you need, which makes the designing simple and the operation easy. Then the detailed designing steps are given via the robot system. At last the simulation of the robot system is given, which shows the validity of the method.     

船舶航迹迭代非线性滑模增量反馈控制算法

分析了带有状态变量及控制输入约束条件的欠驱动船舶航迹控制问题,结合增量反馈技术,对控制系统输出进行动态非线性滑动模态分解迭代设计,提出了一种基于分解迭代非线性滑模的船舶航迹增量反馈控制方法,以避免定常干扰引起的稳态误差及变结构控制的抖振问题,无需对不确定风、流干扰以及模型参数进行估计,能够同时稳定船舶的航向和航迹。应用“育龙”轮的系统模型进行了仿真,结果表明,控制器对系统参数摄动及外界干扰不敏感,具有强的鲁棒性,且其设计参数物理意义明显,易于调节。     

基于反馈线性化的船舶航向保持模糊自适应控制

针对诺宾（Norbin）非线性船舶模型,基于反馈线性化方法和由径向基函数（RBF）神经网络构建的模糊系统的逼近能力,提出了基于反馈线性化的船舶航向保持模糊自适应控制算法。运用泰勒级数展开的线性化技术,使模糊系统的所有参数均可实时调节,引入了鲁棒控制消除模糊逼近系统带来的误差,在李雅普诺夫稳定性理论的基础上导出了自适应控制率。该算法可以确保闭环系统渐近稳定,使系统的模型跟踪误差为0,优于传统的PID控制策略,具有良好的自适应能力。     

Design of rail pressure tracking controller for novel fuel injection system

This paper proposes a rail pressure tracking controller based on a novel common rail system. A mathematical model, based on physical equations, is developed and used for feed forward control design. Rail pressure peak sampling mechanism is designed to remove the disturbance of rail pressure due to fuel injection. An enhanced tracking differentiator is designed to get smooth tracking signal and exact differential signal from signal with noise. Double loop control strategy is designed to decouple the system and to improve dynamic performance of the system. Experimental results indicate that fluctuation of rail pressure is within ±1 MPa in steady condition, while within ±3 MPa in transient condition, which verifies the effectiveness of the proposed rail pressure control strategy.     

基于PLC的工程运输车辆协同作业的定位与控制

对工程实践中运输车辆搭载的定位控制以及相关系统的设计问题进行简单的讨论，并利用PLC自动化控制系统以及相关的模糊控制算法对翻斗汽车以及运输车进行控制以实现定位。通过函数系统中的误差分析法对定位中所产生的误差进行较为详细的分析，最终结论是该定位与控制方法能够完全满足相关误差要求。     

高维动态函数优化的免疫算法研究

生物免疫系统在识别和清除抗原的过程中,免疫细胞之间信息交互和协作,能够快速适应环境变化,具有很强的学习和自适应控制能力.基于此,本文提出了针对高维动态函数优化的免疫算法.该算法的主要特点是采用Gray码编码、采用不同的克隆繁殖策略、对抗体实施不同概率的超变异和多细胞编辑等操作,提高算法寻优能力和种群的多样性.通过与几种典型算法进行比较,仿真结果证明该算法对动态优化性能及跟踪能力有明显的改善     

一类混合时滞复值神经网络的动态行为分析

为将复值神经网络应用于模式识别,对一类具有混合时滞的复值神经网络平衡点的动态行为进行了探讨.在假定激活函数满足Lipschitz条件的情况下,利用同胚映射相关引理以及向量Lyapunov函数法,研究了确保该系统平衡点的存在性、唯一性以及指数稳定性的充分条件.研究结果表明,用复值神经网络的权系数、自反馈函数及激活函数所构造的判定矩阵是M矩阵.最后,通过一个数值仿真算例验证了所得结论的正确性.      

Inverse Learning Control of Nonlinear Systems Using Support Vector Machines

An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.     

基于Simulink的LDPC码仿真研究

在传统LDPC的构造方法基础上,提出了一种具有满秩特点的校验矩阵构造算法,在AWGN信道下结合Simulink进行了系统级仿真。结果表明,新的校验矩阵构造出的LDPC码系统性能优于Galager构造的LDPC码系统性能,LDPC码的短码误码性能劣于长码,LDPC码的码率越高性能越低,LDPC译码增加迭代次数后性能提升。     

Iterative Learning Based Adaptive Traffic Signal Control

Iterative learning control (ILC), as a branch of data-driven control, has obtained plentiful achievements both in theoretical research and practical application over the past two decades. Taking the traffic signal control system as a plant system, the paper introduces the idea of the ILC and fuzzy logic to design an adaptive data-driven traffic signal controller to improve the capacity of the intersection. The key rule of the signal control logic is described by fuzzy iterative theory, and the control strategy can adapt itself to the changing of traffic flow by iterative learning and handle the uncertainty and randomness in traffic system by fuzzy logic, so as to avoid the modeling of complex transport system and take advantages of data-driven on non-model control. Finally, the proposed method is testified to be applicable and effective based on the simulation results by VISSIM. The simulation result indicates that the effect of the proposed method is more effective than the fixed and actuated control approaches.