提高车辆安全性的输出反馈自适应控制

研究含有时变参数的车辆动力学模型的输出跟踪控制问题.控制目标是使车辆的横摆角速度和质心侧偏角分别跟踪理想的设定值,通过反推方法设计输出反馈自适应控制器.控制器的输出为主动横摆力矩,通过控制主动横摆力矩来控制车辆的输出响应跟踪理想的输出信号,从而提高车辆的安全性.仿真结果表明,该控制器能更好地适应车速和路况的变化,鲁棒性强.     

重载组合列车的分布式鲁棒H_∞协同控制方法

针对重载组合列车在复杂地形路况存在通信延迟、通信短时中断以及扰动的问题,提出了一种重载组合列车多机车间的分布式鲁棒协同控制器。在重载组合列车纵向动力学模型的基础上,结合其实际运行特点,设计了协同控制器。该控制器在满足各子系统期望性能指标的同时,可使所有子系统准确地跟踪期望运行速度。仿真实例表明了所设计控制器的稳定性和鲁棒性。     

基于参数自学习的无人车越野环境跟踪控制方法

针对无人车在越野环境下难以高速、高精度地跟踪复杂路况的问题,设计了一种参数自学习的前馈补偿控制器,与模型预测控制方法构成前馈-反馈的控制结构。在该控制结构中,前馈控制根据实时状态的跟踪误差在线更新学习系数,有效考虑车辆高速运动过程中无法精确建模的非线性动力学特性以及复杂路况不断变化的曲率和路面条件等的影响,在保证稳定性的同时快速减小跟踪误差。在越野场景进行了高速的S型与直角弯路径跟踪实车实验来验证参数自学习控制器的有效性,结果表明,所设计的参数自学习控制器相比传统的模型预测控制器跟踪误差和横摆都较小,在跟踪精度和车辆稳定性上都有较大改善。     

基于最优预见控制的高速列车速度控制器研究

针对高速列车自动驾驶(ATO)速度控制器的设计及性能问题,以列车运行过程中的安全性、舒适性、控制输入为约束条件,提出用最优预见算法设计控制器。该算法以列车动力学为基础,确定列车模型传递函数,进行极点配置之后使控制系统稳定;以列车模型为该算法的控制对象,将列车运行过程中的线路附加阻力和基本阻力作为干扰,实现列车ATO模式下目标速度的自动跟踪控制。选取京津城际北京南站至武清站间线路数据进行仿真验证,仿真结果表明该算法在降低列车运行能耗、提高旅客舒适性与列车运行准点率方面的有效性。     

基于迭代学习控制的列车自动运行研究

针对列控系统难以建立精确的动力学模型问题,利用列车运行过程中包含的大量重复信息,选用迭代学习算法对列车动力学模型中的未知参数进行辨识并提出基于迭代学习控制的列车自动运行控制算法。算法核心是利用历史数据生成新的控制量控制列车自动运行。仿真结果表明,经过一定次数的迭代,参数辨识值保持稳定并且列车能够严格跟踪目标曲线行驶,保证列车高精度、高平稳、高安全的运行。     

基于强化学习的波动鳍推进水下作业机器人悬停控制

本文针对波动鳍推进水下作业机器人的悬停控制问题开展研究. 首先, 给出了波动鳍推进水下作业机器人 的运动学模型、动力学模型和波动鳍的参数–力映射模型, 建立了基于马尔可夫决策过程的悬停控制训练框架. 其 次, 基于模型结构和训练策略, 使用强化学习的方法进行网络训练, 得到最佳的悬停控制器. 最终, 在室内水池中完 成了波动鳍推进水下作业机器人的悬停控制实验, 实验结果验证了所提方法的有效性.     

提高车辆安全性的输出反馈自适应控制

研究含有时变参数的车辆动力学模型的输出跟踪控制问题.控制目标是使车辆的横摆角速度和质心侧偏角分别跟踪理想的设定值,通过反推方法设计输出反馈自适应控制器.控制器的输出为主动横摆力矩,通过控制主动横摆力矩来控制车辆的输出响应跟踪理想的输出信号,从而提高车辆的安全性.仿真结果表明,控制器能更好地适应车速和路况的变化,鲁棒性强.

     

双马尔可夫决策过程联合模型

人类在处理问题中往往分为两个层次,首先在整体上把握问题,即提出大体方案,然后再具体实施.也就是说人类就是具有多分辨率智能系统的极好例子,他能够在多个层次上从底向上泛化(即看问题角度粒度变"粗",它类似于抽象),并且又能从顶向下进行实例化(即看问题角度变"细",它类似于具体化).由此构造了由在双层(理想空间即泛化和实际空间即实例化)上各自运行的马尔可夫决策过程组成的半马尔可夫决策过程,称之为双马尔可夫决策过程联合模型.然后讨论该联合模型的最优策略算法,最后给出一个实例说明双马尔可夫决策联合模型能够经济地节约"思想",是运算有效性和可行性的一个很好的折中.     

隐马尔可夫模型在信息工程质量监理中的应用

基于隐马尔可夫模型HMM提出了一种新的信息工程监理质量控制方法,并建立了模型.该模型将信息工程质量监理过程视为两层随机过程,通过抽取监理过程的特征,将隐马尔可夫模型的5个要素与质量监理过程相对应.利用该模型可以求解质量度量要素观测序列和质量状态的匹配度,通过调整模型的初始参数可以将该模型应用于不同的监理软件,并以概率的形式预测监理过程的结果.通过仿真实验,验证了该方法应用于信息工程监理质量控制的可行性.     

具有数据包丢失的网络控制系统主动容错控制

在保证稳定性的前提下,研究具有数据包丢失的网络控制系统主动容错控制问题.基于一类包含马尔可夫丢包过程的网络控制系统模型,考虑了执行器可能失效的情况.利用Lyapunov稳定性理论和线性矩阵不等式方法,获得了系统的保性能被动容错控制器.从控制性能的角度考虑,针对不同的执行器失效模式,得到了丢包网络环境下系统的主动客错控制器的设计方法.数值示例表明,该控制器设计方法是有效的.     

A solution for the cooperative formation-tracking problem in a network of completely unknown nonlinear dynamic systems without relative position information

In this paper, a solution of the formation-tracking problem is provided for a network that contains nonlinear agents with completely unknown dynamics and working under unknown disturbances. By the combination of a cooperative observer and an adaptive model-free controller, the requirement of inter-agent relative position information in the network is eliminated. Here, a cooperative observer is designed to estimate the time-varying reference trajectory and the time-varying parameters of the desired formation topology at each agent in the network. The stability of the proposed cooperative observer is analysed using Lyapunov analysis. Utilising the cooperative observer, the formation-tracking problem in the network of dynamic agents is transformed to a tracking problem in a single agent system. Moreover, an adaptive model-free control policy is applied to each agent for providing the tracking objective. Utilising the algebraic connectivity originating from graph theory, this model-free control algorithm is formulated to scale-up for a network of multi-agents. The proposed decentralised controller includes two model-free adaptive laws for online estimating of the completely unknown dynamics at each agent in the network. The application of the proposed solution is simulated for a network of four quadrotors with unknown internal dynamics and unknown external disturbances.     

Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization

Eco-driving is a traffic operation measure that may lead to important energy savings in high speed railway systems. Eco-driving optimization has been applied offline in the design of commercial services. However, the benefits of the efficient driving can also be applied on-line in the regulation stage to recover train delays or in general, to adapt the driving to the changing conditions in the line. In this paper the train regulation problem is stated as a dynamic multi-objective optimization model to take advantage in real time of accurate results provided by detailed train simulation. If the simulation model is realistic, the railway operator will be confident on the fulfillment of punctuality requirements. The aim of the optimization model is to find the Pareto front of the possible speed profiles and update it during the train travel. It continuously calculates a set of optimal speed profiles and, when necessary, one of them is used to substitute the nominal driving. The new speed profile is energy efficient under the changing conditions of the problem. The dynamic multi-objective optimization algorithms DNSGA-II and DMOPSO combined with a detailed simulation model are applied to solve this problem. The performance of the dynamic algorithms has been analyzed in a case study using real data from a Spanish high speed line. The results show that dynamic algorithms are faster tracking the Pareto front changes than their static versions. In addition, the chosen algorithms have been compared with the typical delay recovery strategy of drivers showing that DMOPSO provides 7.8% of energy savings.     

A Stable Output Feedback Position Control With Integral Action For Robot Manipulators

In this paper, the problem of robust regulation of robot manipulators using only position measurements is addressed. The main idea of the control design methodology is to use an observer to estimate simultaneously the velocity and the modeling error signal induced by model/system mismatches. The controller is obtained by replacing the velocity and the modeling error in an inverse dynamics feedback by their estimates, which leads to a certainty equivalence controller. The resulting controller has a PID‐type structure which, under least prior knowledge, reduces to the PI₂D regulator studied in [20]. Moreover, the controller is endowed with a natural antireset windup (ARW) scheme to cope with control torque saturations. Regarding the closed‐loop behavior, it is proven that the region of attraction can be arbitrarily enlarged with high observer gains only, thus we prove semiglobal asymptotic stability. Our result supersedes previous works in the direction of performance estimates; specifically, it is also proven that the performance induced by a saturated inverse dynamics controller can be recovered by our PID‐type controller. In this sense, our work reveals some connections between PID‐type and inverse dynamics controllers.     

面向安全评估的高速铁路CTCS-2列控系统安全性测试环境

高速铁路CTCS-2列控系统是典型的安全苛求系统。根据安全苛求系统的特点,针对高速铁路CTCS-2列控系统的安全性测试和评估需求,设计了场景—事件驱动的测试脚本语言SED_TSL,提出了高速铁路CTCS-2列控系统测试环境的功能、系统框架、测试策略,实现了基于SED_TSL的CTCS-2列控系统自动化测试环境,并投入到铁道部的CTCS-2列控系统产品制式检测中,有效地实现了列控系统产品的功能与安全性测试。     

非最小相位不稳定对象的鲁棒最优模型匹配

研究了单输入单输出(SISO)线性时不变系统的鲁棒最优模型匹配问题.采用二自由度的控制器,对于被控对象具有乘性摄动时,充分考虑控制器的自由参数,将问题转化成有约束的H∞控制问题,使闭环系统既是H∞范数意义下的最优模型匹配,又具有更大的鲁棒稳定性.最后,通过解两个H∞范数的最优内插问题,给出了一种新的设汁方法来确定控制器的参数.该方法简单、直接,对非最小相位系统和不稳定系统均适用.     

Learning to Acquire Whole-Body Humanoid Center of Mass Movements to Achieve Dynamic Tasks

This paper presents a novel approach for acquiring dynamic whole-body movements on humanoid robots focused on learning a control policy for the center of mass (CoM). In our approach, we combine both a model-based CoM controller and a model-free reinforcement learning (RL) method to acquire dynamic whole-body movements in humanoid robots. (i) To cope with high dimensionality, we use a model-based CoM controller as a basic controller that derives joint angular velocities from the desired CoM velocity. The balancing issue can also be considered in the controller. (ii) The RL method is used to acquire a controller that generates the desired CoM velocity based on the current state. To demonstrate the effectiveness of our approach, we apply it to a ball-punching task on a simulated humanoid robot model. The acquired whole-body punching movement was also demonstrated on Fujitsu's Hoap-2 humanoid robot.     

移动闭塞条件下基于双曲列控策略的发车间隔时间计算

列车间隔时间关乎铁路行车安全和效率。对未来铁路移动闭塞系统发车间隔时间的科学计算进行探讨,引入了能够反映经验丰富司机驾车列车优化行为的基于双曲函数的列车行为控制模型;然后,讨论了列车分段运动方程和车站发车作业、进路解锁等环节,以及各种误差因素,并将它们与基于双曲函数的列车行为控制模型有机结合起来,建立了计算移动闭塞条件下车站发车间隔时间的数学模型,给出了两种不同情形的车站发车间隔时间计算方法,数值仿真试验验证了算法的有效性和可行性。对移动闭塞条件下车站发车环节的列车行为控制和行车组织有较大参考价值。     

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.     

Adaptive Linear Quadratic Regulator for Continuous-Time Systems With Uncertain Dynamics

In this paper, adaptive linear quadratic regulator (LQR) is proposed for continuous-time systems with uncertain dynamics. The dynamic state-feedback controller uses input-output data along the system trajectory to continuously adapt and converge to the optimal controller. The result differs from previous results in that the adaptive optimal controller is designed without the knowledge of the system dynamics and an initial stabilizing policy. Further, the controller is updated continuously using input-output data, as opposed to the commonly used switched/intermittent updates which can potentially lead to stability issues. An online state derivative estimator facilitates the design of a model-free controller. Gradient-based update laws are developed for online estimation of the optimal gain. Uniform exponential stability of the closed-loop system is established using the Lyapunov-based analysis, and a simulation example is provided to validate the theoretical contribution.      

铁路客户服务中心旅客列车正晚点查询系统设计与实现

现有12306网站列车正晚点查询系统功能单一、时效性弱,难以支撑铁路客户服务中心在此方面的咨询、投诉受理工作。针对此状况本文通过讨论相关业务,分析系统实现的难点问题,给出对应解决方案并设计实现了新的铁路客户服务中心旅客列车正晚点查询系统。通过实际生产数据测试比对检验,该系统效果明显优于既有系统。