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For practical mechanical systems, uncertainties/disturbances, such as unmodeled dynamics and frictions, are nonignorable factors. For existing control methods, these factors are usually neglected or addressed by a robust way. As a consequence, the nominal control performance of these methods is sacrificed. Moreover, there exists the chattering problem for some existing robust methods, such as sliding

Dissimilarity analysis (DISSIM) has been widely used to monitor the Gaussian processes. However, its further application is hindered due to its unavailability to non-Gaussian processes whose data do not satisfy the hypothesis of the Gaussian distributions. To sensitively detect faults and enhance understanding of the non-Gaussian processes, a Gaussian feature analytics-based DISSIM (GDISSIM) method

The IEEE Transactions on Automation Science and Engineering (T-ASE) wishes to thank the 1110 reviewers over the past year who have performed an essential role in maintaining the quality of this publication. T-ASE strives for the 90-90 standard, i.e., 90% of articles should be reviewed within 90 days of submission. The review process starts when the Editor-in-Chief assigns the paper to an Editor, who

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We are very pleased to present this Special Section of these Transactions, including six extended articles selected from the technical program of the 2017 International Conference on Automation Science and Engineering (CASE 2017), held in Xi’an, China, August 20–23, 2017. CASE is an offspring of the Transactions on Automation Science and Engineering and is the flagship automation conference of the

The 14th Annual IEEE International Conference on Automation Science and Engineering (CASE 2018) sponsored by the IEEE Robotics and Automation Society (RAS) was held on August 20–24, 2018, in Munich, Germany. IEEE CASE is the flagship automation conference of the IEEE RAS and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal was to provide a broad

Cyber–physical systems (CPSs) in the manufacturing domain can be deployed to support monitoring and analysis of production systems of a factory in order to improve, support, or automate processes, such as maintenance or scheduling. When a network of CPS is subject to frequent changes, the semantic interoperability between the CPSs is of special interest in order to avoid manual, tedious, and error-prone

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This article analyzes the backward sequence for single-armed cluster tools with processing time variations. The backward sequence is popularly used to operate a single-armed cluster tool in practice, but its performance has not been analyzed when processing time variations are introduced. To address the problem, we first define a fundamental cycle and derive a formula for cycle time analysis considering

A previously developed pneumatic muscles’ (PMs) actuated gait exoskeleton (with only knee joint) has been demonstrated in achieving appropriate actuation torque, range of motion (ROM), and control bandwidth for task-specific gait training. While the adopted multi-input–multi-output (MIMO) sliding mode (SM) strategy has preliminarily implemented simultaneous control of the exoskeleton’s angular trajectory

This article proposes a two-finger-based micrograsping system with high compliant borosilicate 3.3 glass probes and the corresponding sensing and control algorithms, which enables the orientation manipulation of microparts in three-dimensional (3-D) space. Compared with the existing research, the novelty of this article relies on three aspects: 1) the end-effector of the microgripper is designed to

Precise 3-D surface reconstruction plays an important role in automated manipulation, industrial inspection, robotics, and so on. In this article, we present a novel 3-D surface reconstruction framework for stereo vision systems assisted with structured light projection. In the framework, a multistep matching scheme is proposed to establish a reliable correspondence between image pairs with high computation

This article considers the problem of assigning mobile robots to goals on transport networks with uncertain and potentially correlated information about travel times. Our aim is to produce optimal assignments such that the average waiting time at destinations is minimized. Since noisy travel time estimates result in suboptimal assignments, we propose a method that offers robustness to uncertainty by

Metadata in most existing building automation systems (BASs) is inconsistent, incomplete, and nondescriptive. This situation is a major obstacle to the widespread use of data analytics to improve the operation of buildings. In this article, we put forward a method to infer zone-level metadata from features derived from BAS data. The method includes two steps: 1) classification of BAS points into different

Nowadays, the entire world is facing challenges in energy and environment. To resolve these problems, the power systems are interconnected to promote the development of renewable energy sources (RESs). However, the economic dispatch (ED) problem for the global energy interconnection (GEI) should tackle two issues: 1) handle the uncertainty from RES and allocate the responsibility among the interconnected

Ambient assisted living and smart home technologies are a good way to take care of dependent people whose number will increase in the future. They allow the discovery and the recognition of human’s activities of daily living (ADLs) in order to take care of people by keeping them in their home. In order to consider the human behavior nondeterminism, probabilistic approaches are used despite difficulties

The optical tracking system (OTS) plays a vital role in the computer-assisted surgical navigation process, whereas the performance of the commonly used binocular stereo vision is affected by the line-of-sight problem and limited workspace. Thus, this article proposed a prior knowledge-based multicamera reconstruction model (PKRM) to both expand the tracking workspace and improve the tracking robust

Somatic cell nuclear transfer (SCNT), which is an important procedure in cloning, has been conducted manually for decades. The operating efficiency drops sharply in batch SCNT because of the long-time observation under microscopy and the time-wasting traditional process. Though the operating time was reduced by robotic SCNT in previous studies, the traditional operating process was still used. In this

The development of autonomous or semiautonomous surgical robots stands to improve the performance of existing teleoperated equipment but requires fine hand-eye calibration between the free-moving endoscopic camera and patient-side manipulator arms (PSMs). A novel method of solving this problem for the da Vinci robotic surgical system and kinematically similar systems is presented. First, a series of

Deadlock resolution has been an important research topic in the field of automated manufacturing systems (AMSs). Researchers generally assume that AMS resources never break down whereas only a few resolve the issues of resource failures in the discrete-event supervision of AMSs. In fact, an AMS consists of a number of numerically controlled machines interacting with each other. The failure of resources

In a human–robot coexisting environment, it is pivotal for a mobile service robot to arrive at the goal position safely and efficiently. In this article, an elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic

This article studies a steelmaking–continuous casting (SCC) scheduling problem by considering ladle allocation. It takes technological rules in steel manufacturing and ladle-related constraints into account. A scheduling problem is formulated to determine allocation equipment for jobs, production sequence for jobs processed by the same equipment, and modification operations for empty ladles after their

In the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader’s objective function, while controlling the reaction function of the followers, simultaneously. We consider an aggregator

Fault prognosis of the air-handling systems, which are the key subsystems of heating, ventilation, and air conditioning systems, allows system operators to know the remaining useful life (RUL), thus preventing unexpected breakdowns and reducing the operational and maintenance costs. In this article, a new hidden semi-Markov model-based method is developed. In the method, only relevant state-transition

Smart customization service is an important element for smart manufacturing. The success of smart customization requires that designers, manufacturers, and customers with differences in context, semantics, and other cognitive aspects be engaged in a collaborative process. With product configurators reported to have positive impacts on product quality to meet customers’ needs, this article attempts

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The steelmaking process in the iron and steel industry involves complicated physicochemical reactions. The main aim of steelmaking is to adjust the quality of molten steel. During the steel-tapping process, the temperature and carbon content are the most essential quality indices for end-point prediction. This article presents a novel machine learning framework for the end-point prediction problems

In this article, we introduce a translational force control method with disturbance observer (DOB)-based force disturbance cancellation for precise 3-D acceleration control of a multi-rotor unmanned aerial vehicle (UAV). The acceleration control of the multi-rotor requires conversion of the desired acceleration signal to the desired roll, pitch, and total thrust. However, because the attitude dynamics

Automatic polyp recognition in endoscopic images is challenging because of the low contrast between polyps and the surrounding area, the fuzzy and irregular polyp borders, and varying imaging light conditions. In this article, we propose a novel densely connected convolutional network with “unbalanced discriminant (UD)” loss and “category sensitive (CS)” loss (DenseNet-UDCS) for the task. We first

A significant challenge in comprehensive geometric accuracy control of an additive manufacturing (AM) system is the specification of shape deviation models for different computer-aided design products manufactured on its constituent AM processes. Current deviation modeling techniques do not satisfactorily address this challenge because they can require substantial user inputs and efforts to implement

We present a method for fabricating general models with multi-directional 3-D printing systems by printing different model regions along with different directions. The core of our method is a support-effective volume decomposition algorithm that minimizes the area of the regions with large overhangs. A beam-guided searching algorithm with manufacturing constraints determines the optimal volume decomposition

In this article, we propose an augmented reality semiautomatic labeling (ARS), a semiautomatic method which leverages on moving a 2-D camera by means of a robot, proving precise camera tracking, and an augmented reality pen (ARP) to define initial object bounding box, to create large labeled data sets with minimal human intervention. By removing the burden of generating annotated data from humans,

Considering time-varying transition probability (TVTP), this article combines Markov regime switching with a dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model to construct a new hedging model and study a state-dependent minimum variance hedging ratio. A two-stage maximum likelihood method is constructed to estimate the model parameters. A filtering

Pulp is the most important raw material in paper industries, whose fiber length stochastic distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the FLSD shaping, which displays non-Gaussian distributional properties. Therefore, the traditional control method based

Accurate modeling of a thermal field is one of the fundamental requirements in engineering thermal management in numerous industries. Existing studies have shown that using differential equations to model a thermal field delivers good performance when the parameters are predetermined through physical or experimental analysis. However, due to variations of the inner medium affected by certain latent

Visual localization has been well studied in recent decades and applied in many fields as a fundamental capability in robotics. However, the success of the state of the arts usually builds on the assumption that the environment is static. In dynamic scenarios where moving objects are present, the performance of the existing visual localization systems degrades a lot due to the disturbance of the dynamic

The contribution of this article is to propose an approach that solves the motion planning problem for an extensible elastic rod using contacts with the environment. We first show how motion planning for a deformable rod can be done by coupling both quasi-static and dynamic rod models with sampling-based methods. Sampling directly in the submanifold of static equilibrium and contact-free configurations

Learning the relationship between a response variable (e.g., a quality characteristic) and a set of predictors (e.g., process variables) is of special importance in process modeling, prediction, and optimization. In many applications, not only is the number of these variables large but these variables are also high-dimensional (HD) (e.g., they are represented by waveform signals). This high dimensionality

To more actively perform fine manipulation tasks in the real world, intelligent robots should be able to understand and communicate the physical attributes of the material during interaction with an object. Tactile and vision are two important sensing modalities in robotic perception system. In this article, we propose a cross-modal material perception framework for recognizing novel objects. Concretely

3-D dynamic field modeling using data acquired from sensor networks is typically complex due to the data sparsity and missing problem. In this article, we consider the ubiquitous missing data problem in current sensor networks and aim to take complete advantage of the existing sensor data for thermal field modeling. In the common scenario, data from the target network are not always obtainable, but

We propose a novel subsurface pipeline mapping and 3D reconstruction method by fusing ground-penetrating radar (GPR) scans and camera images. To facilitate the simultaneous detection of multiple pipelines, we model the GPR sensing process and prove hyperbola response for general scanning with nonperpendicular angles. Furthermore, we fuse visual simultaneous localization and mapping outputs, encoder

Convolutional neural networks (CNNs) for 3-D data analyses require a large size of memory and fast computation power, making real-time applications difficult. This article proposes a novel OctreeNet (a sparse 3-D CNN) to analyze the sparse 3-D laser scanning data gathered from outdoor environments. It uses a collection of shallow octrees for 3-D scene representation to reduce the memory footprint of

Pose estimation with state or measurement constraints has been frequent in autonomous vehicle navigation. Aiming at incorporating constraints inherently, this article proposes a dynamic potential field (DPF)-based formulation to represent states, measurements, and constraints on connected Riemannian manifolds. The state equation and the output equation are derived in the DPF forms, which imply the

3-D registration has always been performed invoking singular value decomposition (SVD) or eigenvalue decomposition (EIG) in real engineering practices. However, these numerical algorithms suffer from uncertainty of convergence in many cases. A novel fast symbolic solution is proposed in this article by following our recent publication in this journal. The equivalence analysis shows that our previous

Because of the worldwide aging population, more and more elders suffer from dementia problem. Nowadays, it is an inconvenient and time-consuming process for medical doctors to diagnose elders who live independently with possible dementia because the process imposes a large quantity of diagnostic questions from a checklist that needs to be answered by elders themselves or their caregivers either directly

This article deals with the problem of discovering a Petri net (PN) model of a discrete-event system, starting from the observation of long-event sequences. Precisely, given an interpreted PN (IPN) system modeling the relations between input and output events of the system (i.e., the reactive/observable behavior), the internal state evolutions of the system (i.e., the unobservable behavior) are first

We propose an innovative approach to enhance welding operations by using a cyber-physical system (CPS) with layered architecture and enabling a robot to be effectively operated by its commanding human. This article focuses on the recognition of the commanding human’s intention that should be executed by the robot. To this end, a virtual reality (VR) system based on the HTC Vive is used to create a

Complex logistics support missions require the execution of spatially separated information gathering and situational awareness tasks. Mobile robot teams can play an important role in the automated execution of these tasks to reduce mission completion time. Planning strategies for such missions must take into account the formation of effective coalitions among available robots and assignment of tasks

Controlling magnetic microswimmers toward 3-D manipulation tasks has received considerable attention. Although related studies on manipulating helical microswimmers have been developed, stable closed-loop controls and accuracy swimming models should be still investigated. This article addresses the problem of 3-D path following for magnetically driven helical microswimmers with an adaptive-compensation

Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually

The tracking-by-detection tracking framework usually consists of two stages: drawing samples around the target object and classifying each sample as either the target object or background. Current popular trackers under this framework typically draw many samples from the raw image and feed them into the deep neural networks, resulting in high computational burden and low tracking speed. In this article

A vision-based system presents one of the most reliable methods for achieving an automated robot-assisted manipulation associated with surgical knot tying. However, some challenges in suture thread detection and automated suture thread grasping significantly hinder the realization of a fully automated surgical knot tying. In this article, we propose a novel algorithm that can be used for computing