Optimal experimental design and some related control problems

This paper traces the strong relations between experimental design and control, such as the use of optimal inputs to obtain precise parameter estimation in dynamical systems and the introduction of suitably designed perturbations in adaptive control. The mathematical background of optimal experimental design is briefly presented, and the role of experimental design in the asymptotic properties of estimators is emphasized. Although most of the paper concerns parametric models, some results are also presented for statistical learning and prediction with nonparametric models.     

Supervisory control of modular systems with global specification languages

The paper presents sufficient conditions for modular (supervisory) control synthesis to equal global control synthesis. In modular control synthesis a supervisory control is synthesized for each module separately and the supervisory control consists of the parallel composition of the modular supervisory controls. The general case of the specification that is indecomposable and not necessarily contained in the plant language, which is often the case in practice, is considered. The usual assumption that all shared events are controllable is relaxed by introducing two new structural conditions relying on the global mutual controllability condition. The novel concept used as a sufficient structural condition is strong global mutual controllability. The main result uses a weaker condition called global mutual controllability together with local consistency of the specification. An example illustrates the approach.     

Control of frictional dynamics of a one-dimensional particle array

Control of frictional forces is required in many applications of tribology. While the problem is approached by chemical means traditionally, a recent approach was proposed to control the system mechanically to tune frictional responses. We design feedback control laws for a one-dimensional particle array sliding on a surface subject to friction. The Frenkel-Kontorova model describing the dynamics is a nonlinear interconnected system and the accessible control elements are average quantities only. We prove local stability of equilibrium points of the un-controlled system in the presence of linear and nonlinear particle interactions, respectively. We then formulate a tracking control problem, whose control objective is for the average system to reach a designated targeted velocity using accessible elements. Sufficient stabilization conditions are explicitly derived for the closed-loop error systems using the Lyapunov theory based methods. Simulation results show satisfactory performances. The results can be applied to other physical systems whose dynamics is described by the Frenkel-Kontorova model.     

Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles

Rather severe parametric uncertainties and uncertain nonlinearities exist in the dynamic modeling of a parallel manipulator driven by pneumatic muscles. Those uncertainties not only come from the time-varying friction forces and the static force modeling errors of pneumatic muscles but also from the inherent complex nonlinearities and unknown disturbances of the parallel manipulator. In this paper, a discontinuous projection-based adaptive robust control strategy is adopted to compensate for both the parametric uncertainties and uncertain nonlinearities of a three-pneumatic-muscles-driven parallel manipulator to achieve precise posture trajectory tracking control. The resulting controller effectively handles the effects of various parameter variations and the hard-to-model nonlinearities such as the friction forces of the pneumatic muscles. Simulation and experimental results are obtained to illustrate the effectiveness of the proposed adaptive robust controller.     

Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

Decomposed-distance weighted optimal transport for unsupervised domain adaptation

Applied Intelligence - Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain with a different but related distribution....     

基于数字兆欧表自动切换档位的研究与实现

随着电力、电气设备的日益复杂化,对于其安全性能也有了更高的要求,数字兆欧表作为测量绝缘阻值的重要工具,已在工业领域中广泛应用;为了满足测量精度以及兆欧表的可靠性,通过基于S3C2440的嵌入式平台,提出了一种基于数字兆欧表的自动切换档位的实现方案,以达到更加精确的测量;此方案通过判断采集到的电压值选择相应的采样电阻来实现,并结合硬件平台和软件设计,分别介绍了驱动程序和应用程序的编写与测试,并很好地应用到了实际电路中;经测试,能够达到预期的效果,测量精度高,测量误差控制在±2%。     

Stability analysis for multi-agent systems using the incidence matrix: Quantized communication and formation control

The spectral properties of the incidence matrix of the communication graph are exploited to provide solutions to two multi-agent control problems. In particular, we consider the problem of state agreement with quantized communication and the problem of distance-based formation control. In both cases, stabilizing control laws are provided when the communication graph is a tree. It is shown how the relation between tree graphs and the null space of the corresponding incidence matrix encode fundamental properties for these two multi-agent control problems.     

Multi-level hierarchical interface-based supervisory control

Hierarchical Interface-Based Supervisory Control employs interfaces that allow properties of a monolithic system to be verified through local analysis. By avoiding the need to verify properties globally, significant computational savings can be achieved. In this paper we provide local requirements for a multi-level architecture employing command-pair type interfaces. This multi-level architecture allows for a greater reduction in complexity and improved reconfigurability over the two-level case that has been previously studied since it allows the global system to be partitioned into smaller modules. This paper also provides results for synthesizing supervisors in the multi-level architecture that are locally maximally permissive with respect to a given specification and set of interfaces.     

A level set method based on the Bayesian risk for medical image segmentation

This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation.