Transactions of the Institute of Measurement and Control

This paper presents an overview of the latest advancement of Internet-enabled cloud-based cyber manufacturing and new approaches for hardware-in-the-loop real-time applications, including cloud-based remote monitoring and control of industrial robots, remote assembly in a cyber-physical environment, and a cloud robotic system for energy-efficient operations. Altogether, they form an integrated cloud-based cyber manufacturing system. In terms of enabling technologies, they are the unique combination of virtual 3D models driven by real sensors, and image-to-model based representation of dynamic environment to guide cyber users. The objective of this research is to significantly reduce network traffic over the Internet for cyber manufacturing. This paper includes case studies, the results of which show that the integrated cyber-physical system consumes less than 1% of network bandwidth of traditional camera-based systems with a 30 msec latency of real-time operations. They are feasible and practical as cyber manufacturing solutions.

Flexible and stretchable electronics technologies have been attracting increasing attention owing to their potential applications in personal consumed electronics, wearable human–machine interfaces (HMI) and the Internet of Things (IoTs). This paper proposes an HMI based on a polyvinylidene difluoride (PVDF) sensor and laminated it onto the surface of the skin for signal classification and controlling the motion of a mobile robot. The PVDF sensor with ultra-thin stretchable substrate can make conformal contact with the surface of the skin for more accurate measurement of the electrophysiological signal and to provide more accurate control of the actuators. Microelectro-mechanical system (MEMS) technologies and transfer printing processes are adopted for fabrication of the epidermal PVDF sensor. Sensors placed on two wrists would generate two different signals with the fist clenched and loosened. It can be classified into four signals with a combination of the signals from both wrists, i.e. four control modes. Experiments demonstrated that PVDF sensors may be used as an HMI to control the motion of a mobile robot remotely.

The term of Internet of Things (IoT) is an emerging concept that has already made an impact on many research domains by providing new solutions and ideas. However, we noticed that many IoT applications are more focused on the ‘communication’ part and are still relatively weak in ‘intelligent’ aspects. Consequently, in this paper we proposed an approach for a self-adaptive distributed decision support model to provide more intelligent support for IoT applications. The model is designed with three major approaches: an artificial neural network (ANN) for environment recognition, knowledge merging to create a local knowledge base and expert systems technology for decision making. In addition, a self-adaption feature is introduced to fix any possible improper usage of knowledge that may be caused by inaccuracies in the environment recognition. This strategy was confirmed in an experiment with a local Chinese medical clinical trials centre, in which the results indicate it may improve the accuracy of decisions from 42% to about 85%.

In application systems of the Internet of Things (IoT), real-time monitoring and control of the systems need to be realized with wireless communications from machine to machine (M2M), especially under harsh application scenarios. The data link of the M2M communication has also a critical effect on the system in the application. In this paper, a wireless bi-directional data link for smart temperature recording is reported. The radio frequency hardware employed consists of NRF24L01 wireless transceivers, RFX2401 amplifiers and matching network sand antennas. The microcontroller unit (MCU) is based on the STM32F103VET6 chip, which controls the NRF24L01 by a four-pin serial peripheral interface (SPI) to realize the bi-directional data link, and the sensor data are sampled with 12-bit analogue-to-digital converters (ADCs). The communication is realized as the interrupt request (IRQ) signal of the NRF24L01 changes periodically. A look-up table and a linear optimization method are also implemented to improve the accuracy of ADC data. The bi-directional data link is then applied to a wired heating control system. The results show that temperature data can be transmitted and received over a distance up to 320 m in an open environment and 52 m in an indoor complex environment with the hardware implemented. The real-time temperature data can be displayed on a computer or a handheld device. Wireless M2M communication and control are thus demonstrated.

The reluctance or incapability of further increasing production resources has made many enterprises suffering from high resource-workload situation. Production dynamics thus cannot be resolved by single resource independently, yet have to make an integral use of the adjustable capability of multiple resources of the whole system in a synchronized way. This paper considers a dynamic production logistics (PL) process comprising multiple independently operated PL stages, which adopts Internet of Things to capture real-time execution dynamics and rely on plan (re)scheduling and cloud-based resources re-configuration to cope with dynamics. A generic dynamic production logistics synchronization (PLS) solution is proposed. Qualitatively, a multi-phase multi-stage multi-degree synchronization control mechanism is put forward toward a PL system with generic structure and typical execution dynamics. Quantitatively, collaborative optimization is applied to assist the PLS to obtain the synchronization results. With a real-life case study, the effectiveness of the proposed mechanism and method has been verified. A set of sensitivity analysis is also conducted toward dynamics of different degree and different time, which provides significant managerial implication to PL managers to better deal with dynamics.

With larger-scale of railway construction in China, there are more and more larger-scale tunnel projects in process. Tunnel engineering constructions are very difficult, high risk, and there are many unpredictable factors that may cause safety issues, such as landslides, roof caving and water bursts, threatening the construction personnel’s safety. The personnel positioning tracking systems have been studied and applied preliminarily in railway tunnel construction. It is important to know how many people are underground, and workers must sign in/out one by one at tunnel entrances when they come in or leave; a process that is time-consuming and sometimes irritating to those who line up and must exit one-by-one to sign in. Active Radio Frequency Identification (RFID) systems can respond to transponders on the personnel’s helmets or jackets to quickly identify workers coming and going without stopping to sign in and out. It can also alert management when a person enters without a transponder. Indoor moving target recognition and tracking in Internet of Things is a popular research and application topic in recent years. This paper proposes an indoor moving target recognition and tracking method based on RFID and Charge Coupled Device (CCD) collaborative information fusion. First, RFID technique and the proposed extended virtual reference elimination (extended virtual reference elimination) approach are used to recognize and to coarsely locate the target. Second, based on the coarse localization results, the monitoring/sleeping control of different CCDs will be realized. Subsequently, the background-difference method is used to detect the target in CCD monitoring image and realize precise localization with multiple angle of view fusion. Finally, with weighted average of the two localization results, the moving target location is obtained. The method combines the advantages of RFID fast recognition and localization and CCD precise localization. The experiment results indicate that the proposed collaborative information fusion method can effectively improve the accuracy and real-time performance of indoor moving target tracking.

This paper introduces the principle of a radio frequency identification (RFID) middleware, including its characteristics, problems and implementation in relation to Electronic Product Code (EPC). The targeted users are small and medium-sized domestic enterprises (SMEs), who need flexible and convenient solutions. This paper provides a framework and construction solutions of this simple RFID middleware, and discusses EPC concepts, solutions and advantages. It has been found that the best solution is to construct a simple and convenient platform and continuously add new common reader adapters based on the actual needs and extend special applications, which will become the basic functions of the RFID middleware, so that SMEs can conveniently use and extend these functions according to their needs. This study shows that the simple RFID middleware is suitable for SMEs to use and the open source can be easily applied to make a platform based on the RFID middleware.

To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.

This paper presents a new system in which automatic Vision Alignment was applied to the Internet of Things (IoT) production line to solve the problem of automatic assembling of screws and iron plates with holes. The position of screw holes are searched for by Machine Vision and transmitted to the robotic arm so that it could insert screws into holes. During this process, a swarm intelligence algorithm – Adaptive Multi-elite Wolf Pack Algorithm (AMWPA) – was used to recognize the holes. The experimental results show that the algorithm succeed in recognizing holes and has better global convergence and computational robustness.

The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sources of evidence. When the sources of evidence are reliable, the conjunctive rule plays a predominant role, whereas if the sources of evidence are in high conflict the disjunctive rule is critical. Our ART approach was compared with existing representative evidence theory methods through two examples, and was further applied in the prediction of floor water inrush in coal mines. The ART approach presented in this paper was demonstrated to behave better than the existing methods.

This paper presents a new method for control of linear networked systems by combining the predictive control and the variable sampling period approaches. In this way, event-driven sensors are implemented, i.e. by construction the sensors are triggered to sample the outputs of the plant, when new control input signals are received by the actuators. In light of this formulation, at each sampling instant, total control loop delay will be equal to the sampling period which is unknown. In order to deal with the network effects associated with a range of pre-specified time delays, appropriate step invariant discrete-time models of the networked plant are calculated offline. Based on these, some stabilizing control signals are constructed online. The control signals are then packed in the control-side packet, transmitted back to the plant side and received by a time delay compensator module. A less conservative class of Lyapunov functions, called switched quadratic Lyapunov, is used here for stability analysis and stabilizing controller design. Simulation studies on well-known benchmark problems demonstrate the effectiveness of the proposed method.

In an electro-hydraulic system (EHS), the throttling phenomenon of the hydraulic valve leads to the problem of low utilization efficiency of hydraulic energy and severe increases in temperature. To alleviate this problem, this paper presents a type of one-chamber-controlled hydraulic circuit. In some applications where elastic load is dominant, this hydraulic circuit can achieve a significant energy-saving effect. For a valve-controlled system, the orifice non-linearity and the slowly varying parameter significantly influence the control performance of the electro-hydraulic system. With this aim in mind, the orifice compensation method is proposed to deal with the orifice non-linearity. Based on the compensation, the fractional order proportional–integral (FOPI) controller is adopted to deal with the problem of fluid parameter variation. In the controller designing process, this paper proposes a controller parameter tuning method based on system frequency characteristic data. Simulation and experiment results show that the strategy presented in this paper can reduce the energy losses dramatically and, at the same time, the control performance of electro-hydraulic system can be guaranteed.

In this paper, by using a distributed control system (DCS) device, a robust tracking control system is proposed based on robust right coprime factorization for a heat exchanger actuated by a water level process with coupling effects and uncertainties. Firstly, nonlinear models of water level and temperature processes with coupling and uncertainties are given. Secondly, nonlinear feedback tracking control systems corresponding to a multi-input multi-output (MIMO) process are realized by using operator-based robust right coprime factorization. Meanwhile, stability of the control systems is guaranteed by using robust stability conditions compatible with the MIMO process including coupling effects, and to improve the output tracking performance, tracking controllers are designed. Finally, the effectiveness of the proposed design scheme is confirmed by simulation and experimental results.

This paper deals with developing a robust iterative algorithm to find the least-squares (P, Q)-orthogonal symmetric and skew-symmetric solution sets of the generalized coupled matrix equations. To this end, first, some properties of these type of matrices are established. Furthermore, an approach is offered to determine the optimal approximate (P, Q)-orthogonal (skew-)symmetric solution pair corresponding to a given arbitrary matrix pair. Some numerical experiments are reported to confirm the validity of the theoretical results and to illustrate the effectiveness of the proposed algorithm.

This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is evaluated in comparison with two robust algorithms like non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective cuckoo search (MOCS). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. Furthermore, the technique of order preference by similarity to ideal solution (TOPSIS) is used to rank the non-dominated solutions and determine the best compromise solution among them. A factorial analysis develops the linear regression expressions of optimal cost, service level measures, lot size and safety stock factor for practitioners. Lastly, the results of the regression equations are compared using a MOPSO–TOPSIS approach and the validity of the developed equations are checked.

In this paper, a new intelligent control scheme based on multiple models and neural networks is proposed to adaptively control a class of Hammerstein nonlinear systems with arbitrary deadzone input. This approach consists of a linear robust adaptive controller, multiple neural networks-based nonlinear adaptive controllers and a switching mechanism. Since the control input is derived from a modified certainty equivalent principle, the manner in which the closed-loop stability is established forms the main contribution. To show the usefulness of the developed results, three simulation examples, including a direct current motor subject to a nonlinear friction, are studied.

In this paper, a theoretical comparison between existing the sigma-point information filter (SPIF) framework and the unscented information filter (UIF) framework is presented. It is shown that the SPIF framework is identical to the sigma-point Kalman filter (SPKF). However, the UIF framework is not identical to the classical SPKF due to the neglect of one-step prediction errors of measurements in the calculation of state estimation error covariance matrix. Thus SPIF framework is more reasonable as compared with UIF framework. According to the theoretical comparison, an improved cubature information filter (CIF) is derived based on the superior SPIF framework. Square-root CIF (SRCIF) is also developed to improve the numerical accuracy and stability of the proposed CIF. The proposed SRCIF is applied to a target tracking problem with large sampling interval and high turn rate, and its performance is compared with the existing SRCIF. The results show that the proposed SRCIF is more reliable and stable as compared with the existing SRCIF. Note that it is impractical for information filters in large-scale applications due to the enormous computational complexity of large-scale matrix inversion, and advanced techniques need to be further considered.