Periodic and event-based impulse control for linear stochastic systems with multiplicative noise

This paper studies the performance comparison of periodic and event-based sampling for a class of linear stochastic systems with multiplicative noise, where the impulse control is adopted. By solving boundary value problems, we obtain the analytic expressions of the mean sampling time and the average state variance under the event-based sampling. It is shown that the event-based impulse control has substantially smaller average state variance than the periodic control under the same sampling frequency. Particularly, for the integrator case, the performance ratio of the two sampling methods is given explicitly. By simulation, it is demonstrated that the advantage of event-based sampling over periodic sampling is most obvious for unstable systems, followed by critical stable systems, and least obvious for stable systems.     

Feedback control of robot Arm with discrete nonlinear model

This article looks at the problem of controlling a robot arm from a discrete time perspective. We develop the theories and obtain results that feedback linearizes the discrete nonlinear system representing a PUMA 560 robot arm. The effect of sampling on the performance of the arm has been studied and shown through experiments. The results presented here, where the nonlinear robot model has been linearized and controlled completely in discrete time, are new to the robotics literature. We shown certain restrictions necessary on the sampling time of the system. The results obtained have been experimentally verified at the Center for Robotics and Automation at Washington University. Because a number of researchers have addressed the problem of loss of feedback linearizability under sampling, it was important to develop theories and obtain results in discrete time that take into consideration the effects due to sampling. This problem has been addressed here. Analysis of the control law under the assumptions of bounded input is performed and a recursive sensitivity function is derived. The results we obtain in discrete time show the dependency of the performance of the arm on the sampling time. It has been seen that with a higher sampling frequency the performance of the arm substantially improves and it is expected that close to 1000 Hz sampling rate, the peak performance of the PUMA 560 robot arm, will be reached.     

Beyond equidistant sampling for performance and cost: A loop‐shaping approach applied to a motion system

Nonequidistant sampling potentially enhances the performance/cost trade‐off that is present in traditional equidistant sampling schemes. The aim of this paper is to develop a systematic feedback control design approach for systems that go beyond equidistant sampling. A loop‐shaping design framework for such nonequidistantly sampled systems is developed that addresses both stability and performance. The framework only requires frequency response function measurements of the LTI system, whereas it appropriately addresses the linear periodically time‐varying behavior introduced by the nonequidistant sampling. Experimental validation on a motion system demonstrates the superiority of the design framework for nonequidistantly sampled systems compared to traditional designs that rely on equidistant sampling.     

Control of sampled-data systems with variable sampling rate

This paper addresses stability and performance of sampled-data systems with variable sampling rate, where the change between sampling rates is decided by a scheduler. A motivational example is presented, where a stable continuous time system is controlled with two sampling rates. It is shown that the resulting system could be unstable when the sampling changes between these two rates, although each individual closed-loop system is stable under the designed controller that minimizes the same continuous loss function. Two solutions are presented in this paper. The first solution is to impose restrictions on switching sequences such that only stable sequences are chosen. The second solution presented is more general, where a piecewise constant state feedback control law is designed which guarantees stability for all possible variations of sampling rate. Furthermore, the performance defined by a continuous time quadratic cost function for the sampled-data system with variable sampling rate can be optimized using the proposed synthesis method.     

A note on truss topology optimization under self-weight load: mixed-integer second-order cone programming approach

This study compares the performance of popular sampling methods for computer experiments using various performance measures to compare them. It is well known that the sample points, in the design space located by a sampling method, determine the quality of the meta-model generated based on expensive computer experiment (or simulation) results obtained at sample (or training) points. Thus, it is very important to locate the sample points using a sampling method suitable for the system of interest to be approximated. However, there is still no clear guideline for selecting an appropriate sampling method for computer experiments. As such, a sampling method, the optimal Latin hypercube design (OLHD), has been popularly used, and quasi-random sequences and the centroidal Voronoi tessellation (CVT) have begun to be noticed recently. Some literature on the CVT asserted that the performance of the CVT was better than that of the LHD, but this assertion seems unfair because those studies only employed space-filling performance measures in favor of the CVT. In this research, we performed the comparison study among the popular sampling methods for computer experiments (CVT, OLHD, and three quasi-random sequences) with employing both space-filling properties and a projective property as performance measures to fairly compare them. We also compared the root mean square error (RMSE) values of Kriging meta-models generated using the five sampling methods to evaluate their prediction performance. From the comparison results, we provided a guideline for selecting appropriate sampling methods for some systems of interest to be approximated.     

Optimal sampling-rates and tracking properties of digital LQ and LQG tracking controllers for systems with an exogenous component and costs associated to sampling

In this paper it is shown that if costs are associated to sampling operations which are added to a performance criterion, the minimization of this new performance criterion results in a controller operated at an optimal sampling rate. This, under the assumptions that the system is periodically sampled, the applied control is kept fixed between every two sampling instances and some technical conditions are met. In case the considered planning horizon in the performance criterion is finite an algorithm is devised which calculates in a finite number of steps the optimal sampling period. It is shown that the technical conditions mentioned above are satisfied by the finite planning horizon time-varying LQG tracking problem. Since stability is a major requirement in controller design we also consider the case of an infinite planning horizon. This analysis is focused on the time-invariant digital LQ tracking problem. Given some mild regularity conditions a numerical algorithm is presented that approximates the optimal solution within any prespecified error norm. It is shown that also in this case an optimal sampling-rate exists. The algorithm for determining the optimal sampling period if the planning horizon is finite is illustrated in an economic example.     

一种改进的谐波电流检测算法

针对基于瞬时无功功率理论的谐波检测算法实时性差、不能直接用于单相谐波检测的问题,提出采用滑窗迭代DFT算法来提高谐波检测的实时性;并针对传统DFT在非同步抽样时存在错误的问题,提出采用自适应抽样算法来自动调整抽样时间,从而减小DFT在非同步抽样时的计算误差。仿真结果表明,基于自适应抽样的滑窗迭代DFT算法能够实时有效地检测出谐波电流,具有很好的目标跟随性和抗干扰性。     

改进型分层抽样技术及性能研究

报文抽样技术是高速网络流量测量和管理中使用的一项关键技术。本文通过引进分层特征、层数L、分层边界、各层样本量分配、层内抽样策略5个分层抽样参数,并对其进行重新配置和简单理论探讨,实现对分层抽样技术的改进。同时文章使用简单线性估计推断原始流数据,并借助于Φ偏差检验方法,对改进的分层抽样技术和其它抽样技术在测量网络报文长度分布方面进行准确性性能比较。结果表明,改进的分层抽样技术在测量报文长度分布方面的准确性性能远高于其它抽样方式,提高了测量的精度。     

Assessment of the sampling rate in control systems

Several approaches to automatic control performance assessment have been proposed in recent years, among which the one by Harris has received much attention in particular. One thing that is not automatically assessed by computing a performance index, is whether the controller uses an appropriate sampling frequency. In this paper, an approach is proposed which allows the evaluation of Harris’ performance index at sampling rates faster than that used for data collection. The new method is illustrated using data from the Swedish pulp and paper industry. When the dead-time is not the main performance limiting factor, the Harris index is known to produce overly pessimistic values. Therefore, a modified index was also presented and evaluated for the examples. The conclusion is that by using the modified index it is possible to get an indication of the benefit of faster sampling.     

A feedback scheduler for real-time controller tasks

The problem studied in this paper is how to distribute computing resources over a set of real-time control loops in order to optimize the total control performance. Two subproblems are investigated: how the control performance depends on the sampling interval, and how a recursive resource allocation optimization routine can be designed. Linear quadratic cost functions are used as performance indicators. Expressions for calculating their dependence on the sampling interval are given. An optimization routine, called a feedback scheduler, that uses these expressions is designed.     

On interval tracking performance evaluation and practical varying sampling ILC

This paperconsiders the evaluation of interval tracking error for sampled control performance and an associated sampling technique to enhance the tracking performance. The upper bounds of the tracking error profile of arbitrary sample interval for both the linear system and nonlinear system are first given. A practical sampled-data iterative learning control with varying sampling rates is proposed to ensure a prior given tolerant tracking error. In this control strategy, the inter-sample behaviour is checked to determine which intervals are not satisfactory when the given tracking performance at-sample time instants is satisfied, and then the sampling frequency for such intervals is increased. Both at-sample and inter-sample tracking performance are satisfied after enough learning iterations. Two examples are simulated to demonstrate the effectiveness of the proposed sampling strategy.     

一种面向低采样率的点云数据处理网络

在直接处理点云的三维神经网络中,采样阶段实现了对原始点云中关键点的筛选,对于整个网络的性能及网络的抗噪能力具有重要作用。目前主流的最远点采样（FPS）方法在处理大规模3D点云数据时计算量大且耗时,并且低采样率时经过FPS采样后模型性能下降明显。针对这两个问题,提出一种面向低采样率的点云数据处理网络AS-Net。设计一个新的采样模块代替原backbone中的FPS,其由两个Layer组成,每个Layer基于长短期记忆网络获取原始点云与采样点云之间的联系权重,从而高效提取关键信息,去除冗余信息。在此基础上,利用注意力机制选择特征值较高的原始点云作为采样点,采样点作为后序任务的关键点输入到网络,进一步提高网络模型性能。基于ModelNet40数据集的实验结果表明,在低采样率条件下,AS-Net仍可达到81.6%的分类准确率,与使用FPS作为采样方法的网络模型相比提高52.7%。此外,其对噪声干扰具有很强的鲁棒性,对于大场景的分割时间效率优于同类采样方法。     

采样点个数对T-S模糊建模精度的影响

在模糊建模中所取的采样点个数会对辨识出的模型精度产生影响,在只给出有限个数据采样点且数据分布不能人为控制的情况下怎样选取最优的采样点个数是模糊辨识中要解决的问题之一.通过采样点个数变化的模糊辨识算法来研究模糊建模中采样点个数对模型描述性能的影响.基于T S模糊模型,采用对称三角形模糊划分和“网格对角线法”提取模糊规则,通过对DISO系统和Mackey Glass 无序时间序列进行建模,给出模糊模型训练性能指标和检验性能指标随采样点个数增加的变化趋势曲线.     

Visibility‐Aware Progressive Farthest Point Sampling on the GPU

In this paper, we present the first algorithm for progressive sampling of 3D surfaces with blue noise characteristics that runs entirely on the GPU. The performance of our algorithm is comparable to state‐of‐the‐art GPU Poisson‐disk sampling methods, while additionally producing ordered sequences of samples where every prefix exhibits good blue noise properties. The basic idea is, to reduce the 3D sampling domain to a set of 2.5D images which we sample in parallel utilizing the rasterization hardware of current GPUs. This allows for simple visibility‐aware sampling that only captures the surface as seen from outside the sampled object, which is especially useful for point‐based level‐of‐detail rendering methods. However, our method can be easily extended for sampling the entire surface without changing the basic algorithm. We provide a statistical analysis of our algorithm and show that it produces good blue noise characteristics for every prefix of the resulting sample sequence and analyze the performance of our method compared to related state‐of‐the‐art sampling methods.     

Asymptotic properties of the best sampled-data control performance

Properties of the best achievable performance of sampled-data control systems are investigated. Contrary to our intuition, the best sampled-data control performance does not always converge to the best continuous-time control performance, even as the sampling period approaches zero. A counterexample is presented and a lifting-based analysis is performed. It is shown that the performance convergence is guaranteed if, and only if, a certain Hankel norm and an aliasing effect computed from a specified antialiasing filter tend to vanish with a decreasing sampling period. It is also shown that this condition is reduced to a simpler form if the specified filter has special forms. These results can be generalized to a system involving a generalized hold     

Non-preemptible last section assignment for reducing feedback latency in real-time control systems

A real-time control system design procedure consists of the controller design stage and the implementation stage. In the controller design stage, various digital control theories are used with assumptions, such as synchronous sampling, no sampling jitter and negligible feedback latency (latency from sensing to actuation). However, in the implementation stage, multiple control tasks are usually scheduled on a processor, which creates a finite sampling period, varying feedback latency and sampling jitter, and therefore the controller's performance is degraded. In this article, we investigate the relationship between control performance and timing parameters. In the course of this investigation, we found that the feedback latency is the dominant factor affecting control performance. In addition, we propose a rate monotonic (RM) scheduler with non-preemptible last section algorithm, which can reduce the feedback latency considerably. The proposed algorithm provides better control performance than a preemptive RM scheduler, in most cases. The effectiveness of the proposed algorithm is shown in illustrative examples.     

Finding needles in haystacks is harder with neutrality

This research presents an extended analysis of the reported successes of the Cartesian Genetic Programming method on a simplified form of the Boolean parity problem. We show the method of sampling used by the CGP is significantly less effective at locating solutions than the solution density of the corresponding formula space would warrant. We present results indicating that the loss of performance is caused by the sampling bias of the CGP, due to the neutrality friendly representation. We implement a simple intron free random sampling algorithm which performs considerably better on the same problem and then explain how such performance is possible.     

Time delay and sampling rate effect on dual-stage servo control performance

Dual-stage servo control loops are modeled for hard disk drives (HDDs) with the consideration of time delay which is due to the time taken for position error signal measurement and controller computing. The time delays are involved in controller design and the closed servo control loop is investigated to show the effects of the time delay on servo control performance. The studied actuation system is a PZT milliactuator based dual-stage servo system. The delay time within and over the sampling interval is separately considered for 62.5 kHz sampling rate. To show the effect of sampling rate on servo control performance, a control system designed with 40 kHz sampling rate is also studied. As a result, significant improvement of servo performance is attained with this increased sampling rate and a reasonable time delay for the dual-stage actuation system.     

Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem

Process planning and scheduling (PPS) is an important and practical topic but very intractable problem in manufacturing systems. Many research studies used multiobjective evolutionary algorithm (MOEA) to solve such problems; however, they cannot achieve satisfactory results in both quality and computational speed. This paper proposes a hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MOEA) to deal with the PPS problem. HSS-MOEA tactfully combines the advantages of vector evaluated genetic algorithm (VEGA) and a sampling strategy according to a new Pareto dominating and dominated relationship-based fitness function (PDDR-FF). The sampling strategy of VEGA prefers the edge region of the Pareto front and PDDR-FF-based sampling strategy has the tendency converging toward the central area of the Pareto front. These two mechanisms preserve both the convergence rate and the distribution performance. The numerical comparisons state that the HSS-MOEA is better than a generalized Pareto-based scale-independent fitness function based genetic algorithm combing with VEGA in efficacy (convergence and distribution) performance, while the efficiency is closely equivalent. Moreover, the efficacy performance of HSS-MOEA is also better than NSGA-II and SPEA2, and the efficiency is obviously better than their performance.     

Evaluation of subgraph searching algorithms detecting network motif in biological networks

Despite several algorithms for searching subgraphs in motif detection presented in the literature, no effort has been done for characterizing their performance till now. This paper presents a methodology to evaluate the performance of three algorithms: edge sampling algorithm (ESA), enumerate subgraphs (ESU) and randomly enumerate subgraphs (RAND-ESU). A series of experiments are performed to test sampling speed and sampling quality. The results show that RAND-ESU is more efficient and has less computational cost than other algorithms for large-size motif detection, and ESU has its own advantage in small-size motif detection.