汽车轮缸压力传感器故障重构方法研究与仿真

针对汽车液压制动系统中轮缸压力传感器的故障重构问题,建立了汽车液压制动系统的轮缸压力非线性模型。在此基础上,给出了一种基于滑模观测器的轮缸压力传感器故障重构方法,并将滑模观测器的设计问题转化为LMI求解问题。最后对轮缸压力传感器断路故障进行了仿真研究,讨论了不同增益对故障估计的影响。

Abstract：

A fault reconstruction scheme based on the sliding mode observer was proposed for the fault reconstruction of the wheel cylinder pressure sensor in the vehicle hydraulic braking system, as well as a nonlinearity model of the wheel cylinder in the hydraulic braking system. Furthermore, the design of the sliding-mode observer was converted to the solving of the LMI. In conclusion, the wheel cylinder pressure sensor open circuit fault simulation experiment and the influence on fault estimation over the different gain were proposed.     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

AN INTELLIGENT CONTROL SYSTEM BASED ONRECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information ρy/ρu for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is im-plemented to construct RNNM on line. Lastly, in order to evaluate the performance of the proposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.     

Heuristic based data scheduling algorithm for OFDMA wireless network

A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.     

Distributed consensus algorithm for networked Euler-Lagrange systems with self-delays and uncertainties

A distributed coordinated consensus problem for multiple networked Euler-Lagrange systems is studied. The communication between agents is subject to time delays, unknown parameters and nonlinear inputs, but only with their states available for measurement. When the communication topology of the system is connected, an adaptive control algorithm with selfdelays and uncertainties is suggested to guarantee global full-state synchro-nization that the difference between the agent’s positions and ve-locities asymptotically converges to zero. Moreover, the distributed sliding-mode law is given for chaotic systems with nonlinear inputs to compensate for the effects of nonlinearity. Finally, simulation results show the effectiveness of the proposed control algorithm.     

Novel ensemble learning based on multiple section distribution in distributed environment

Because most ensemble learning algorithms use the centralized model, and the training instances must be centralized on a single station, it is difficult to centralize the training data on a station. A distributed ensemble learning algorithm is proposed which has two kinds of weight genes of instances that denote the global distribution and the local distribution. Instead of the repeated sampling method in the standard ensemble learning, non-balance sampling from each station is used to train the base classifier set of each station. The concept of the effective nearby region for local integration classifier is proposed, and is used for the dynamic integration method of multiple classifiers in distributed environment. The experiments show that the ensemble learning algorithm in distributed environment proposed could reduce the time of training the base classifiers effectively, and ensure the classify performance is as same as the centralized learning method.     

Shape control on probability density function in stochastic systems

A novel strategy of probability density function （PDF） shape control is proposed in stochastic systems. The control er is designed whose parameters are optimal y obtained through the improved particle swarm optimization algorithm. The parameters of the control er are viewed as the space position of a particle in particle swarm optimization algorithm and updated continual y until the control er makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The control er is excellent in making the state PDF fol ow the expected PDF and has the very smal error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.     

Multi-constraint quality of service routing algorithm for dynamic topology networks

An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks, such as satellite networks and Ad-hoc networks. The AMQRA is a distributed and mobile-agents-based routing algorithm, which combines ant quantity system （AQS） with ant colony optimization （ACO） that is used in AntNet routing algorithm. In dynamic topology networks, the AMQRA achieves timely optimization for concave metric QoS constraint and fast convergence. The proposed routing algorithm is simulated in Iridium satellite constellation on OPNET. The results show that AMQRA not only outperforms the AntNet in convergence rate in dynamic topology networks but also can optimize concave metric QoS constraint and reasonably allot bandwidth to the load to avoid networks congestion.     

Joint power and spectrum allocation algorithm in cognitive radio networks

The spectrum sharing problem between primary and cognitive users is mainly investigated.Since the interference for primary users and the total power for cognitive users are constrained,based on the well-known water-filling theorem,a novel one-user water-filling algorithm is proposed,and then the corresponding simulation results are given to analyze the feasibility and validity.After that this algorithm is used to solve the communication utility optimization problem subject to the power constraints in cognitive radio network.First,through the gain to noise ratio for cognitive users,a subcarrier and power allocation algorithm based on the optimal frequency partition is proposed for two cognitive users.Then the spectrum sharing algorithm is extended to multiuser conditions such that the greedy and parallel algorithms are proposed for spectrum sharing.Theory and simulation analysis show that the subcarrier and power allocation algorithms can not only protect the primary users but also effectively solve the spectrum and power allocation problem for cognitive users.     

Scheduling algorithm based on critical tasks in heterogeneous environments

Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.     

混合动力汽车制动力矩动态分配控制策略研究

混合动力汽车(HEV)大都在原有液压制动系统的基础上增加了电机能量回收制动,能量回收制动力矩的存在使得传统液压制动控制策略必须进行调整以确保制动安全性。提出了一种综合制动控制策略,基于总制动力矩的动态分配,采用模糊控制逻辑对液压制动力矩和能量回收制动力矩进行动态调整,两种制动力矩在该控制策略下能够协同工作,仿真结果表明该控制策略稳定有效,在确保制动安全性的同时能够有效回收制动能量。     

四驱混合动力轿车串联式电液复合制动仿真

针对自主开发的四轮驱动混合动力轿车,设计了串联式制动能量回收系统。基于制动安全性和最大化回收制动能量的原则,提出了ABS液压制动与再生制动协调控制的电液复合制动控制策略。该策略先根据驾驶员的操作实时计算制动强度和总需求制动力矩,然后依据制动强度大小对总需求制动力矩进行分配,并且实时检测车轮滑移率以判断是否切换到ABS独立工作模式。并在Simulink软件平台上建立了样车动力传动、制动系统及控制策略模型,分别在轻度、中度和紧急制动三种制动工况下仿真了串联式制动能量回收系统的性能。仿真结果表明:所提出的电液复合制动控制策略能有效地提高制动能量的回收效率,且在紧急制动时能保证车辆的制动稳定性能。     

提高汽车操纵稳定性的控制器设计

为提高汽车操纵稳定性,设计了一种新颖的两级分层操纵稳定性控制系统。分级控制系统的第一层是一基于线性矩阵不等式的鲁棒模型匹配控制器。当汽车处于不稳定行驶状态时,该控制器优化稳定整车操纵性的横摆控制力矩,并根据该横摆力矩计算目标控制车轮的滑移率。控制系统的第二层是一移动滑模控制器。该控制器可以在预定的时间内精确地跟踪第一层控制器输入的参考滑移率,并对目标控制车轮施加制动力矩来达到稳定汽车操纵性的目的。在各种极限行驶状况下的仿真试验表明,该控制器可以有效地提高汽车操纵稳定性,而且该控制器对不同车速,各种附着系数的路面和车辆物理参数的变化具有很好的鲁棒性。     

超低干扰力矩微纳卫星姿控半物理仿真平台

微纳卫星姿控的反作用飞轮输出力矩小,难以克服普通气浮转台的干扰力矩,为了解决上述问题,实现微纳卫星姿控地面半物理仿真验证,必须对干扰力矩进行有效补偿。为此,对干扰力矩分类并分析了各自的特性,针对性提出了干扰力矩主动补偿方法,研制了主动补偿式超低干扰力矩气浮转台,并基于此开发了微纳卫星姿控半物理仿真平台。研制的气浮转台的干扰力矩达到2×10^-5 Nm,小于微纳卫星反作用飞轮的最小输出力矩,利用半物理仿真平台有效地验证了微纳卫星大角度姿态机动控制算法。     

小型汽车制动能量再生系统参数建模及仿真

针对一种型号的小型汽车制动能量再生系统, 进行了制动减速和起步加速过程相关参数建模,利用Matlab软件对此车制动能量回收及能量释放过程的车辆速度、加速度、液压蓄能器气囊压力、体积进行了仿真.通过对仿真结果的分析,得出的结论为:在制动减速过程,所选择的蓄能器能满足制动减速过程吸收完制动能量的要求;在起步加速过程,该蓄能器利用所储存的能量能够使该小型汽车起步加速到一定车速,从而满足车辆倒拖发动机,使发动机高速自行起动的要求.     

汽车转向/防抱死制动系统的协调控制研究

协同控制结构由执行级和协调级组成。在执行级中,设计基于最佳滑移率的汽车防抱死制动可调节滑模控制器。针对滑模控制中固有抖振缺陷,自动适时调节控制参数增益以消弱抖振。此外,设计基于鲁棒自适应控制的横摆力矩控制器和主动前轮转向控制器力求改善汽车动态响应、鲁棒自适应性和稳定性。在协调级中,设计适用于复杂工况的制动力分配策略,并提出一种协调控制转向系统和制动系统的新方法。最后用仿真结果验证所设计控制算法的稳定性和有效性。     

小卫星模型独立大角度姿态机动半实物仿真

提出了仅用反作用轮的小卫星姿态大角度机动控制算法，针对反作用轮输出力矩受限和速率饱和的为束，采用递阶饱和思想使星体沿欧拉轴逐步逼近目标位置．文中设计的快速机动控制器，无需知道系统精确模型，即可实现卫星任意时刻的姿态捕获和机动控制，并采用dSPACE实时仿真系统、反作用飞轮实物、先纤陀螺实物和星载机，在单轴气浮台上进行了半实物仿真验证．仿真结果表明，在系统模型不确定时，设计的控制算法能够实现快速姿态机动任务，有良好的鲁棒性。     

装载机限滑差速器的自适应模糊控制和仿真分析

为提高装载机限滑差速器的限滑性能和对不同路面状况的自适应能力,针对装载机结构设计了一种新型的电控液压限滑差速器,以最佳滑移率为控制目标,建立了自适应模糊PID控制器,通过调节差速器液压控制系统的输出压力来控制差速器的输出限滑转矩,实现转矩在驱动轮上的合理分配,有效抑制车轮打滑。作者建立了装载机仿真模型,在非对称路面直线行驶工况下进行了仿真实验。仿真结果表明,该电控液压限滑差速器限滑功能良好,与普通差速器相比设计的自适应模糊PID控制器具有良好的自适应能力。     

航天器转动惯量在线辨识

可靠和有效的高精度姿态控制需要已知卫星变化的质量特性的精确信息,在线辨识航天器的转动惯量显得尤为重要。利用飞轮作为执行机构,分别提出了一种基于递推最小二乘法的航天器转动惯量在线开环辨识和闭环辨识的算法。当飞轮输出合适的控制力矩时,能够快速辨识出处于开环状态的航天器转动惯量,并且保证航天器的姿态变化在可接受的范围内。提出了一种闭环辨识方法,基于变结构控制器,在航天器完成姿态机动任务的同时,能够快速辨识出航天器的转动惯量,辨识精度在2%之内。