Extended state observer-based finite-region control for 2-D Markov jump systems

In this article, an extended state observer-based finite-region control scheme is presented for two-dimensional Markov jump systems with unknown mismatched disturbances. The mathematical model of the two-dimensional Markov jump systems is built on the well-known Roesser model. By establishing special recursive formulas and utilizing the 2-D Lyapunov function theory, sufficient conditions are obtained, which prove that the resultant system is finite-region bounded, if some linear matrix inequalities are achieved. Then, we provide an algorithm to solve the extended state observer-based controller gains. With the proposed control scheme, the external disturbances can be actively rejected from the system outputs. To conclude, a numerical example based on the Darboux equation is provided to demonstrate the validity and effectiveness of the devised control scheme.     

A polarization modulated directional backlight autostereoscopic display

A polarization modulated directional backlight autostereoscopic display is proposed and demonstrated. The system consists of the orthogonally polarized backlight, the Fresnel lens array, a light shaping diffuser film, and a liquid crystal display (LCD) with a microphase retardation film. The autostereoscopic image pair carried by the directional light with different polarization directions is simultaneously projected to different spatial directions. The simulation and experimental results show that the directional projection of parallax images is realized for a high-quality autostereoscopic display with large viewing angle and continuous viewing volume, hence making it suitable for practical applications.     

融合先验信息和特征约束的杆塔螺栓缺陷检测

目的 螺栓是输电线路中数量最多的紧固件,一旦出现缺陷就会影响电力系统的稳定运行。针对螺栓缺陷自动检测中存在的类内多样性和类间相似性挑战,提出了一种融合先验信息和特征约束的Faster R-CNN（fasterregions with convolutional neural network）模型训练方法。方法 在航拍巡检图像预处理阶段,设计了基于先验信息的感兴趣区域提取算法,能够提取被识别目标的上下文区域,从而减少模型训练阶段的数据量,帮助模型在训练阶段关注重点区域,提高其特征提取能力。在模型训练阶段,首先通过费舍尔损失约束Faster R-CNN模型的输出特征生成,使样本特征具有较小的类内距离和较大的类间间隔;然后采用K近邻算法处理样本特征得到K近邻概率,将其作为难易样本的指示以引导模型后续更加关注难样本。结果 在真实航拍巡检图像构建的螺栓数据集上进行测试,与基线模型相比,本文模型使螺栓识别的平均精度均值（mean average precision,mAP）提高了6.4%,其中正常螺栓识别的平均精度（average precision,AP）提高了0.9%,缺陷螺栓识别的平均精度提高了12%。结论 提出的融合先验信息和特征约束的输电杆塔螺栓缺陷检测方法在缺陷螺栓识别上获得了良好的效果,为实现输电线路螺栓缺陷的自动检测奠定了良好的基础。     

Control allocation technology based on fault diagnosis for the unmanned aerial vehicle system subject to physical constraints and fault reconfiguration mismatch

The problem of the system robustness subject to physical constraints and mismatched fault reconstruction is discussed in this paper. In order to facilitate the design, a four-rotor unmanned aerial vehicle (UAV) system model was selected for research. First, the control allocation model of the nonlinear UAV system with disturbances is shown in the paper. Secondly, a weighted pseudo-inverse method based on adaptive weights is proposed, which reduces the impact of physical constraints on the system. After that, a dynamic weight control allocation method based on the fault efficiency matrix is designed. The weight matrix can dynamically adjust the control distribution law according to the fault estimation value provided by the observer. Then, a dynamic adaptive control allocation method for faults and physical constraints is carried out by combining adaptive weights and dynamic weights. Finally, a simulation example is presented to further illustrate the effectiveness of the algorithm proposed in this paper.     

Nonlinear cascade control of an electro-hydraulic actuator with large payload variation

Electro-hydraulic actuators have been widely used in industrial production, but the unknown variable payload seriously affects its position control accuracy. Therefore, a radial basis function neural network disturbance observer is designed to estimate the lumped disturbance force through strong online learning ability in the absence of force sensor. Besides, a nonlinear cascade controller with double loop structure is proposed in this paper. A global fast terminal sliding mode control method is firstly applied in the outer loop position system, which can eliminate chattering and improve convergence speed comparing to traditional sliding mode control. The inner loop force system adopts a backstepping control method to calculate the actual input of the whole system. Theoretical analysis indicates that the proposed controller is stable even if existing time-variant disturbance. Moreover, three comparative controllers are designed and tested in both simulations and experiments. Comparative results show that the developed method has absolute average errors of 1.14 and 0.49 mm in different position tracking, which means more satisfactory tracking performance compared to the contrast controllers.     

Vapor Phase Growth of Centimeter-Sized Band Gap Engineered Cesium Lead Halide Perovskite Single-Crystal Thin Films with Color-tunable Stimulated Emission

Single crystal metal halide perovskites thin films are considered to be a promising optical, optoelectronic materials with extraordinary performance due to their low defect densities. However, it is still difficult to achieve large-scale perovskite single-crystal thin films (SCTFs) with tunable bandgap by vapor-phase deposition method. Herein, the synthesis of CsPbCl_3(1–x)Br_3x SCTFs with centimeter size (1 cm × 1 cm) via vapor-phase deposition is reported. The Br composition of CsPbCl_3(1–x)Br_3x SCTFs can be gradually tuned from x = 0 to x = 1, leading the corresponding bandgap to change from 2.29 to 2.91 eV. Additionally, an low-threshold (≈23.9 µJ cm⁻²) amplified spontaneous emission is achieved based on CsPbCl_3(1–x)Br_3x SCTFs at room temperature, and the wavelength is tuned from 432 to 547 nm by varying the Cl/Br ratio. Importantly, the high-quality CsPbCl_3(1–x)Br_3x SCTFs are ideal optical gain medium with high gain up to 1369.8 ± 101.2 cm⁻¹. This study not only provides a versatile method to fabricate high quality CsPbCl_3(1–x)Br_3x SCTFs with different Cl/Br ratio, but also paves the way for further research of color-tunable perovskite lasing.     

基于区块链的物联网任务协作信任管理方案

在物联网智能设备任务协作场景中,为解决设备交互不受信以及存在恶意设备破坏协作的问题,提出一种基于区块链的信任管理方案。半分布式的架构克服了集中式和分布式架构的不足,任务协作过程中兼顾了双方的信任,并设计了审查机制确保任务评价的真实可靠。信任的衡量中使用狄利克雷分布模拟任务评价的多样性,构建信任为数个状态,并利用马尔可夫链评估信任状态和全局信任。仿真结果表明,提出的方案可以客观衡量设备的信任,同时在检测恶意设备、提高协作任务成功率及避免误判方面具有优势,因此可以有效确保任务交互的可信和协作环境的安全。     

An adaptive reaching law of chattering-free discrete-time sliding mode control for systems with external disturbance

An adaptive reaching law of chattering-free discrete-time sliding mode control is proposed for the systems with external disturbance. An adaptive function is proposed in the reaching law which can increase reaching speed as sliding variable is far away from zero and avoid undesired chattering when sliding variable is close to origin. Moreover, a third-order backward difference of disturbance is added to obtain a narrower quasi-sliding mode band (QSMB). Both the closed-loop system performance and finite reaching steps to achieve the desired QSMB are theoretically analyzed. The simulation results verify the effectiveness of the proposed control strategy.     

Combustion timing control of HCCI engine based on NNPC and Elman-NN model under complex conditions

Homogeneous Charge Compression Ignition (HCCI) combines the characteristics of gasoline engine and diesel engine with high thermal efficiency and low emissions. However, since there is no direct initiator of combustion, it is difficult to control the combustion timing in HCCI engines under complex working conditions. In this paper, Neural Network Predictive Control (NNPC) for combustion timing of the HCCI engine is designed and implemented. First, the black box model based on Elman neural network is designed and developed to estimate the combustion timing. The fuel equivalence ratio, intake valve closing timing, intake manifold temperature, intake manifold gas pressure, and engine speed are chosen as the system inputs. Then, a NNPC controller is designed to control combustion timing by controlling the intake valve closing timing. Simulation results show that the Elman neural network black box model is capable of estimating the HCCI engine combustion timing. In addition, regardless of whether the HCCI engine is in constant or complex condition, the designed NNPC controller is capable of keeping the combustion timing within the ideal range. In particular, under New European Driving Cycle (NEDC) working conditions, the maximum overshoot of the controller is 28.95% and the average error is 1.03 crank angle degree. It is concluded that the controller has good adaptability and robustness.     

Quasi-min-max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

This paper proposes a new visual servoing quasi-min-max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state-dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state-dependent linear error system is covered as linear parameters-varying models which is used to design the visual servoing quasi-min-max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results.