A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

基于星座图对射频指纹识别方法的研究

针对星座图的射频(RF)指纹识别方案中,低信噪比环境下识别准确率低的问题,提出一种基于欧式距离与幅度距离的二维识别算法来进行RF指纹识别。该方案通过对星座图进行优化处理,可从优化后的星座图中提取识别性能更好的RF指纹,再通过二维识别算法来提高识别准确率。仿真结果表明:与仅用欧式距离作为判断依据的方法相比,所提出的二维识别算法的识别准确率最高可提升8%,在设备容量为50组的情况下识别准确率为77.8%,并且从优化后的星座图中所提取的RF指纹具有更好的唯一性和稳定性。     

Anti‐Sway Crane Control Considering Wind Disturbance and Container Mass

A method for estimating the sway angle using an observer has already been proposed. The state observer estimates the sway angle accurately and must use the detected sway angle value. However, the estimated sway angle has an error owing to rope length error, friction force, and wind. Moreover, the container mass cannot be determined, and therefore the observer parameter is not suitable. We already proposed robust antisway control for overcoming rope length error without adding a new sensor. Further, we designed a friction disturbance observer to cancel out the influence of the friction force. In this paper, we first propose a container mass estimation method when a crane system performs rolling up control. The observer parameter can be selected using the estimated mass value. Second, in crane parallel shift control, we propose a robust antisway control even when there is a wind disturbance. We design a wind disturbance observer and propose a wind disturbance estimator to separate the friction observer output from the wind disturbance observer output. We confirm through experiments that the proposed method can reduce vibration.     

A two-layered cross coupling control scheme for a three-dimensional motion control system

A two-layered modeling and compensation scheme is proposed to reduce the contouring error of a three-dimensional motion control system. In the proposed scheme, the contouring error model of the three-dimensional motion control system is divided into two layers: the top layer and the bottom layer. The proposed multi-layered structure of the contouring error model presents more flexibility in the control system design because the cross coupling controllers in different layers can be designed separately. In this paper, a nonlinear PI controller and a position error compensator are designed in the bottom layer in order to achieve high contouring accuracy in the XY plane, while a unilateral compensator is designed in the top layer to further reduce contouring error in the three dimensional space. Finally, experiments are performed to verify the performance of the proposed two-layered modeling and compensation scheme. Experiment results show that the designed two-layered cross coupling controller can obtain higher contouring accuracy than traditional cross coupling controller both in the XY plane and in the XYZ space.     

一种广义不可分支持向量机算法

针对标准的C-SVM(C-support vector machine)算法在处理很多实际分类问题时，对识别错误代价损失差异很大的极端情况表现出的局限性，提出一种通用的 广义支持向量机算法。根据识别错误后所付出的代价，可以把最优分类面向代价损失低的一方进行推移，留给代价损失高的一方更大的空间，提高其识别率，从而减小识别错误后带来的代价损失。该方法进一步提高了标准C SVM的适用性以及样本的正确识别率，将新算法应用到高分辨雷达距离像的识别中，实验证明,广义C-SVM能取得比传统C-SVM更好的识别效果。     

Precise contour following for biaxial systems via an A-type iterative learning cross-coupled control algorithm

This paper proposes an A-type iterative learning cross-coupled control (CCC) algorithm for biaxial systems. An algebraic equation based contour error model is used as the CCC input. This model has the advantage that it is zero if and only if the real value vanishes. The iterative learning CCC is designed to make its input converge to zero. Hence, it is expected to that the contour error will converge to zero as well. After analyzing the control algorithm convergence condition in the frequency domain, the proposed method is implemented on a motion stage. Experimental results show that the algorithm perfectly follows contours as the cycles approach infinity regardless of whether tracking errors are small or large.     

Unreliable numbers: error and harm induced by bad design can be reduced by better design

Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that many common, widely used systems are defective, and induce unnecessary human error. We show that Monte Carlo methods enable designers to explore the implications of normal and unexpected operator behaviour, and to design systems to be more resilient to use error. We demonstrate novel designs with improved resilience, implying that the common problems identified and the errors they induce are avoidable.     

Quantitative analysis of semiconductor supply chain contracts with order flexibility under demand uncertainty: A case study

The evidence base for the configuration of rolling horizon flexibility (RHF) contracts (a type of quantity flexibility contract) used in the semiconductor industry to coordinate production and demand remains meagre, more art than science. Informed by the characteristics of actual clauses and demand behaviors drawn from a company’s experience, a discrete-event simulation model is developed to represent the company’s supply chain. It comprises of three parties: a customer, a supplier (semiconductor manufacturer), and a capacity provider. Through analysis of customer forecasted demand the paper characterizes forecast demand as being under, over or unbiased. Models of these forecasted demands drives both long and short term planning. In long term planning, which is given twelve months before an order is delivered, capacity at the capacity provider is booked. Short term planning is also driven by this forecast which, within a binding period, is governed by an RHF contract. Results from the model report inventory levels, and delivery compliance, namely Delivery Performance (DP) and Delivery Reliability (DR), measures widely used in this sector. It is concluded from this work that on the balance of performance measures RHF contracts with asymmetrical flexibility bounds are substantially better than those with symmetrical boundaries, and that this conclusion is robust with regard to both over-planning and under-planning behaviors. This robustness is a critical attribute with respect to the endemic medium-term vacillation between both states experienced in practice in this sector.     

Quantitative relationship between time margin and human reliability

Operator reliability in complex systems is influenced by various performance shaping factors (PSFs). Time is a particularly important PSF; however, empirical studies of human reliability analysis (HRA) are rarely focused on modeling the effect of time PSF on human error probability (HEP). This study contributes to HRA literature by investigating the empirical relationship between time margin and HEP. Time margin is defined as the difference between the time available to complete a task and the time required to successfully complete the task, divided by the required time. We investigate and compare two models (logistic and linear) to explain HEP based on time margin. The empirical HEP data for model testing were extracted from a microworld simulator (Study 1) and a full-scope simulator (Study 2) in two existing studies relevant to procedural tasks in nuclear power plants. For Study 1, both models exhibited an acceptable, equivalent explanatory power; for Study 2, although both models exhibited an acceptable explanatory ability, the logistic model explained more variance in HEP. Our findings indicate the potential of the logistic model in explaining and predicting HEP based on time margin in time-critical tasks.     

大气次声源定位算法及误差分析

逐步多通道相关方法是定位次声源的主要方法。该方法会忽略阵元的海拔高度差,从而引入误差。文章分析了基于广义互相关的时间延迟估计算法,讨论了基于时延的平面波入射角定位方法。着重研究了逐步多通道相关方法的误差来源,确定了基于时延的定位方法是产生误差的主要原因,明确了大气次声源定位误差来源于忽略了阵元间的海拔高度差。基于最小二乘法推导了在不考虑阵元高度的情况下计算次声波入射角的方法。对存在高度差的4元中心三角阵型进行了误差仿真实验,在忽略阵元海拔高度差的条件下,各方向入射的次声波角度定位误差最大达到4°,特定阵型的阵元最大海拔高度差与入射角度计算误差成线性关系,并探讨了入射角度计算误差对主要参数和后续定位的影响。