Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.     

Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

基于多自由度气动机械手位置伺服系统稳定性控制研究

基于传统易碎薄板机械手位置伺服控制系统稳定性低、自动化分拣效率低等不足,设计了一种基于笛卡尔坐标式的气动码垛机器手和位置伺服稳定性控制系统。首先,设计并介绍笛卡尔坐标式码垛机械手的基本组成结构,对机械手末端吸盘气动回路控制系统进行设计和分析;然后分别对机械手X、Y、Z三个方向的伺服电机控制原理进行分析并设计了一种位置伺服系统的前馈自适应控制算法;最后,将传统位置闭环PID算法和前馈自适应控制算法进行位置跟踪稳定性对比试验。实验结果表明该笛卡尔坐标式的气动码垛机器手和位置伺服稳定性控制系统设计合理,满足实际生产要求。     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

模块化微反应系统内溴化间甲基苯甲醚连续合成

根据微化工技术发展的主要趋势，针对4-溴-3-甲基苯甲醚间歇非均相合成技术存在的问题，以微筛孔反应器与玻璃微珠填充床为核心功能微设备单元构建了模块化微反应系统，并在此模块化微反应系统内对液-液非均相连续溴化合成4-溴-3-甲基苯甲醚开展研究。通过优化操作条件，在溴浓度(x_{\mathrm{B}{\mathrm{r}}_{\mathrm{2}}})为17.5%（质量分数）、溴与间甲基苯甲醚摩尔比(n_{\mathrm{B}{\mathrm{r}}_{\mathrm{2}}}/n_M)为1.01、反应起始温度(T)为 0℃、停留时间为0.78 min条件下，4-溴-3-甲基苯甲醚的收率大于98%，多溴代副产物的含量仅为1%。与传统间歇溴化反应相比，模块化微反应系统内连续溴化反应具有十分明显的优势：可将间歇过程连续化，在保证安全的基础上极大地提升了反应的效率（时空收率为6.5×10⁴ kg/(m³·h)）；另外，该过程是由传质控制的，微反应器的传质性能优异，可极大地改善产品的选择性（多溴代副产物的量减少50%）。该研究为4-溴-3-甲基苯甲醚的连续高效安全合成提供了技术和设备依据。     

PC2425锤式破碎机的篦子改造

PC2425破碎机是较为常用的锤式破碎机规格之一，主要用于石灰石的破碎，台时产量800~1 000 t，出料粒度≤70 mm，筛余5%，配套4 500~5 000 t水泥生产线。PC2425破碎机篦条为易损件，由于物料的冲击会导致篦条变形，但其原有的弓形架与篦条型式，更换时很难将篦条从弓形架中抽出，导致更换非常不便。此次改造，用户最初适量降低破碎机的出料粒度，以提高生料磨的产量及降低生料粉磨的电耗，虽然通过调整篦条与锤头的间距及篦条间隙可以达到降低出料粒度的要求。但是为了彻底解决篦条更换困难问题，重新设计了篦架结构。     

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd–Bullough–Mikhailov and Tzitzéica–Dodd–Bullough equations, are obtained using the exp_a function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.