Application of neural networks in forecasting engine systems reliability

This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.     

基于PCI总线的ARINC629数据通讯卡设计

经过对三种航空数字总线通讯方式的比较,在提炼出ARINC629规范终端功能的基础上,提出了用于波音等大型商用客机机载设备测试诊断平台的ARINC629数据通讯卡的总体设计方案;基于微机总线的发展趋势,将数据通讯卡定位为PCI板卡的形式,完成了数据通讯卡中协议电路、DSP控制模块和PCI总线接口的硬件设计,以及DSP模块程序、用户界面和驱动程序的软件设计,实现了通信协议,为机载电子设备的测试和故障诊断设备的研制奠定了基础。     

近似动态规划在双容水箱预测控制中的应用

在双容水箱液位优化控制的研究中,由于双容水箱液位控制系统是一个典型的具有大滞后、非线性特征的难控系统,造成系统液位控制不稳定.为解决上述问题,提出一种近似动态规划的预测控制算法,建立反映系统预测时域内参考轨迹与预测输出偏差的性能指标函数J.将函数J的优化问题看作动态规划问题,为了避免“维数灾”,采用近似动态规划求解J的近似值,获得最(次)优控制策略.算法包括评价网络、模型网络和执行网络三部分.评价网络近似性能指标函数,模型网络近似系统输入输出关系,执行网络给出相应控制策略.通过不断在线调整各网络参数,使系统输出逼近参考液位.仿真结果表明,与经典PID算法相比,改进算法表现出更好的控制效果.     

基于人工神经网络的烧结终点预报系统

烧结终点提前预报对于稳定终点,进而提高烧结矿强度和产量、降低能耗有重要意义.为解决烧结过程大滞后环节和烧结终点难以测量的困难,文章介绍了烧结终点的软测量方法;提出了一个新的预报参数——风箱废气温度曲线拐点;将BP神经网络应用于烧结终点的预报,实现了准确地预报烧结终点的状态和生产操作指导.     

Receding horizon control via Bolza-type optimization

This paper deals with the stabilization problem of nonlinear control systems. A variant of the receding horizon control method is proposed which is based on the solution of a certain Bolza problem. The stabilizing property of the method is proved in the case of Lipschitz continuous value function. This version of receding horizon scheme can be considered as a straightforward generalization of the so-called Fake Riccati Equation Technique for nonlinear constrained systems.     

Predictive proportional nonlinear control for stable-target tracking of a mobile robot: an experimental study

Two new types of control method have been developed based on model predictive control for stable-target tracking of a nonholonomic mobile robot. One method (Method 1) is a new nonlinear control method. This was developed based on model predictive control (predictive nonlinear control) to predict the next position of a mobile robot using the current velocities of the right and left wheels. This technique uses a tuning guideline in predictive nonlinear control. The other method (Method 2) is a combination of Method 1 and proportional control (predictive proportional nonlinear control). Method 2 involves a tuning guideline not only in a predictive nonlinear controller, but also in a proportional controller. In this technique, the selection of a tuning guideline in the proportional controller is enhanced, and thereby increases the control action in closed-loop responses. In Method 1, the nonlinear controller is derived from Liapunov stability theory, and is used to control the linear and angular velocities for locomotion control. Tuning parameters in the nonlinear controller (in Method 1) are selected to satisfy various design criteria, such as stability, performance, and robustness. Method 1 has certain limitations that result in a decrease of the performance criteria specified. Strong nonlinearities in the mobile robot system result in accumulated errors. To enhance performance further, we developed Method 2 as the solution for decreasing cumulative errors. Hence, the proportional controller is added to Method 1 in the closed-loop form in order to eliminate errors. The advantage of Method 2 is that it can cope with strong nonlinearities in the mobile vehicle system. The results of the performances of Method 1 and Method 2 are shown to demonstrate the effectiveness of both methods, and also the better performance of Method 2. The two new methods are effective in stable-target tracking, yielding an increase in performance and stability.     

Min–Max MPC based on a network problem

In general, min–max model predictive controllers have a high computational burden. In this work, an efficient implementation of this class of controllers that can be applied to linear plants with additive uncertainties and quadratic cost functions is presented. The new approach relies on the equivalence of the maximization problem with a network problem. If a given condition is satisfied, the computational burden of the proposed implementation grows polynomially with the prediction horizon. In particular, the resulting optimization problem can be posed as a quadratic programming problem with a number of constraints and variables that grows in a quadratic manner with the prediction horizon. An alternative controller has been proposed for those systems that do not satisfy this condition. This alternative controller approximates the original one with a given bound on the error.     

Hierarchical model-based predictive control of a power plant portfolio

One of the main difficulties in large-scale implementation of renewable energy in existing power systems is that the production from renewable sources is difficult to predict and control. For this reason, fast and efficient control of controllable power producing units – so-called “portfolio control” – becomes increasingly important as the ratio of renewable energy in a power system grows. As a consequence, tomorrow's “smart grids” require highly flexible and scalable control systems compared to conventional power systems. This paper proposes a hierarchical model-based predictive control design for power system portfolio control, which aims specifically at meeting these demands.The design involves a two-layer hierarchical structure with clearly defined interfaces that facilitate an object-oriented implementation approach. The same hierarchical structure is reflected in the underlying optimisation problem, which is solved using Dantzig–Wolfe decomposition. This decomposition yields improved computational efficiency and better scalability compared to centralised methods.The proposed control scheme is compared to an existing, state-of-the-art portfolio control system (operated by DONG Energy in Western Denmark) via simulations on a real-world scenario. Despite limited tuning, the new controller shows improvements in terms of ability to track reference production as well as economic performance.     

Application of an explicit min-max MPC to a scaled laboratory process

Min-max model predictive control (MMMPC) requires the on-line solution of a min-max problem, which can be computationally demanding. The piecewise affine nature of MMMPC has been proved for linear systems with quadratic performance criterion. This paper shows how to move most computations off-line obtaining the explicit form of this control law by means of a heuristic algorithm. These results are illustrated with an application to a scaled laboratory process with dynamics fast enough to preclude the use of numerical solvers.     

基于CSMA/CA协议的隐藏终端问题的改进

在无线局域网中,当节点采用竞争方式访问信道时,隐藏终端和其他节点可能同时利用信道发送数据,引起数据碰撞。在CSMA/CA协议中,增加数据收发的控制分组,正确表示数据收发开始和停止的时间,代替网络分配矢量(NAV),使得隐藏终端和其他节点按照先后顺序发送数据,实现可靠的数据传输。在CC2530芯片设计的方法中,实验结果表明降低了隐藏终端干扰造成的误码率;在NS-2仿真器评估的方法中,仿真结果表明控制分组对于网络其他性能影响小。通过两种方法验证了改进MAC协议的有效性。