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In the quest for human-robot interaction (HRI), leading to the development of emotion recognition, learning, and analysis capabilities, robotics plays a significant role in human perception, attention, decision-making, and social communication. However, the accurate recognition of emotions in HRI remains a challenge. This is due to the coexistence of multiple sources of information in utilizing multimodal facial expressions and head poses as multiple convolutional neural networks (CNN) and deep learning are combined. This research analyzes and improves the robustness of emotion recognition, and proposes a novel approach that optimizes traditional deep neural networks that fall into poor local optima when optimizing the weightings of the deep neural network using standard methods. The proposed approach adaptively finds the better weightings of the network, resulting in a hybrid genetic algorithm with stochastic gradient descent (HGASGD). This hybrid algorithm combines the inherent, implicit parallelism of the genetic algorithm with the better global optimization of stochastic gradient descent (SGD). An experiment shows the effectiveness of our proposed approach in providing complete emotion recognition through a combination of multimodal data, CNNs, and HGASGD, indicating that it represents a powerful tool in achieving interactions between humans and robotics. To validate and test the effectiveness of our proposed approach through experiments, the performance and reliability of our approach and two variants of HGASGD FER are compared using a large dataset of facial images. Our approach integrates multimodal information from facial expressions and head poses, enabling the system to recognize emotions better. The results show that CNN-HGASGD outperforms CNNs-SGD and other existing state-of-the-art methods in terms of FER. [ABSTRACT FROM AUTHOR]

In this article, K-meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human–robot interaction (HRI). The multimodal features (gray pixels; time and frequency domain) extracted from facial expression and speech are fused based on Kernel canonical correlation analysis. K-means clustering is used to select features from multiple modalities and reduce dimensionality. The proposed approach can improve the heterogenicity among different modalities and make multiple modalities complementary to promote multimodal emotion recognition. Experiments on two datasets, namely SAVEE and eNTERFACE‘05, are conducted to evaluate the accuracy of the proposed method. The results show that the proposed method produces good recognition rates that are higher than the ones produced by the methods without K-means clustering; more specifically, they are 2.77% higher in SAVEE and 4.7% higher in eNTERFACE‘05. [ABSTRACT FROM AUTHOR]

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human–robot interaction. It aims to understand students' intentions in the university teaching scene. Initially, we employ convolution and maximum pooling for feature extraction. Subsequently, we apply the ridge regression algorithm for emotional behavior recognition, which effectively mitigates the impact of complex network structures and slow network updates often associated with deep learning. Moreover, we utilize multivariate analysis of variance to identify the key personal information factors influencing intentions and calculate their influence coefficients. Finally, a fuzzy inference method is employed to gain a comprehensive understanding of intentions. Our experimental results demonstrate the effectiveness of the BDFEI model. When compared to existing models, namely FDNNSA, ResNet-101+GFK, and HCFS, the BDFEI model achieved superior accuracy on the FABO database, surpassing them by 12.21%, 1.89%, and 0.78%, respectively. Furthermore, our self-built database experiments yielded an impressive 82.00% accuracy in intention understanding, confirming the efficacy of our emotional intention inference model. [ABSTRACT FROM AUTHOR]

Two-layer fuzzy support vector regression-Takagi–Sugeno (TLFSVR-TS) model is proposed for emotion understanding in human–robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression, and emotional intention understanding is obtained mainly based on human emotions and identification information. It aims to make robots capable of recognizing and understanding human emotions, in such a way that make HRI run smoothly. TLFSVR-TS considers about the priori knowledge inferred from human personal preference to reduce the uncertainty of various people, and multiple support vector regression (SVR) corresponding to different genders/provinces/ages of human to guarantee the local learning ability. Preliminary application experiments are performed in the developing emotional social robot system, where 30 volunteers experience the scenario of “drinking in the bar.” Results show that the proposal receives higher understanding accuracy than that of TLFSVR, kernel fuzzy ${c}$ -means clustering is fused with SVR, and SVR. [ABSTRACT FROM AUTHOR]

During the drilling process, horizontal drill strings are often subject to stick-slip and back-pressure phenomena, which undermine drilling safety and affect drilling costs. However, most of the models proposed in the existing literature apply only to the analysis of the drill string motion state and are irrelevant to the rotary drilling process in coal mines. This paper presents a lumped-mass model of a drill string in a horizontal coal mine well. First, based on reasonable assumptions, the drill-string system was considered as four lumped-mass units. The lateral-torsional dynamics model was established based on the Lagrange dynamics theory. Further, combined with lateral movement, the intermittent contact between the drill string and borehole wall is described, and the friction torque due to this intermittent contact is calculated. Combined with the Karnopp bit-rock interaction, a drill-string dynamics model of horizontal wells that considers intermittent contact friction was developed. The proposed horizontal well drill-string model is more suitable for rotary drilling in a coal mine, and the nonlinear influencing factors are considered more comprehensive. [ABSTRACT FROM AUTHOR]

Normal and stable operations of hydraulic systems are of great importance to the safety and efficiency of industrial production processes. Accurate and prompt diagnosis of fault types and degrees can ensure hydraulic systems return to normal in the early stage of faults and thus can prevent serious accidents. However, the structure of hydraulic systems is complex, and some faults may occur simultaneously. In addition, many hydraulic systems have multi-rate data collected from different sensors. Such problems cause great challenges to fault diagnosis in hydraulic systems. Motivated by the above issues, this paper proposes a deep learning method to diagnose faults and identify fault degrees in hydraulic systems using fully convolutional networks (FCNs) and deep feature fusion. The main contributions are twofold: (1) A new fault diagnosis framework is designed to identify both the fault types and degrees in the presence of multiple faults; and (2) deep feature extractors composed of multiple superimposed convolutional blocks are designed to extract deep features from multi-rate time series, and such features are then fused via flattening and concatenating and fed into the fault diagnosis model. Case studies based on a hydraulic system test bed are provided to demonstrate the effectiveness and superiority of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

The pre-chamber pressure is an important control parameter that affects the coke dry quenching process. It often fluctuates violently and is detrimental for the safe operation of the coke dry quenching process. This study proposes an intelligent control method for the pre-chamber pressure based on working condition identification for the coke dry quenching process to realize stable control of the pre-chamber pressure. First, by describing the coke dry quenching process and analyzing the factors affecting the pre-chamber pressure, an intelligent control strategy was developed. Then, the K-means clustering algorithm was used to identify the working conditions of pre-chamber, and the working conditions were divided into two categories: stable and fluctuating. For stable conditions, a fuzzy proportional-integral-derivative controller was designed to improve the pressure control accuracy. For fluctuating conditions, an expert controller was designed to rapidly adjust the pressure. Finally, experiments based on actual data were performed and the results showed that the proposed method can effectively improve the control accuracy of pressure under different conditions. This satisfies the requirements for a continuous coke dry quenching process. [ABSTRACT FROM AUTHOR]

The heating furnace is a sizeable energy-consuming device in iron and steel industry. In compact strip production (CSP), the size of the heating furnace is large, and the working conditions are complicated. Furthermore, the large fluctuation of furnace temperature leads to large loss of billet combustion and energy consumption. This article deals with the design of hybrid intelligent control based on condition identification for the combustion process of the CSP heating furnace. By analyzing the process of the heating furnace and existing problems, the structure of a hybrid intelligent control system is proposed, and the working conditions are divided into two categories: stable and fluctuating. A fuzzy controller is designed to improve the control accuracy of furnace temperature under the stable working condition, and an expert controller is used to adjust the temperature rapidly under the fluctuating working condition. Besides, calorific value compensation is introduced to reduce the influence of calorific value of gas fluctuation on temperature. After verifying the effectiveness, the proposed method has been used in a steel plant and achieved a good control result. The intelligent control system improves the control precision of the furnace temperature and reduces energy consumption compared with the traditional control system. [ABSTRACT FROM AUTHOR]

Accurate prediction of carbon efficiency is a prerequisite for achieving energy saving and consumption reduction in an iron ore sintering process, and is the key to guaranteeing the quality and yield of sintered ore. This paper proposes an original real-time dynamic prediction model for carbon efficiency prediction in the process. A Savitzky–Golay filter is used to eliminate noise of the actual production data collected from a cooperative sintering plant, and the correlation between carbon efficiency and process parameters is determined by mutual information. A modified version of maximum entropy clustering algorithm is presented for identifying working conditions to accurately discriminate between anomalies and normal working conditions. Then, the real-time dynamic prediction model of carbon efficiency based on broad learning is established by taking into account the process characteristics and using the prediction error information under normal working conditions. The proposed model is demonstrated to be valid by carrying out some experiments with actual production data. The experimental comparative analysis show that this model has good generalization capabilities and high real-time prediction accuracy, and is superior to other advanced methods in dynamic prediction of carbon efficiency. • An improved maximum entropy clustering is presented to identify working conditions. • A real-time dynamic prediction model of CCR based on broad learning is established. • A suitable modeling framework is proposed to reflect the sintering process dynamics. [ABSTRACT FROM AUTHOR]

This article is concerned with the trajectory optimization problem of a drilling process, which is of significance to drilling efficiency and safety. Due to the difference between an actual trajectory and a planned trajectory, this problem is a multiobjective optimization problem (MOP) with parameter uncertainties. To solve the problem, this study devises a new approach named nondominated sorting genetic Algorithm II using outlier removal (OR-NSGA-II). First, the optimization problem with parameter uncertainties is formulated, including two objective functions: 1) a trajectory length and 2) an expected value of drill-string torque. Then, an outlier-removal mechanism is devised in the sorting process of NSGA-II to reduce the negative effects of parameter uncertainties. Next, the crowding distance calculation in NSGA-II is improved to ensure population diversity. Comparison of simulation results show that our method is effective on the MOP with parameter uncertainties. [ABSTRACT FROM AUTHOR]

The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network’s (CNN’s) potential performance in avoiding local optima and speeding up convergence by the hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human–robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using a principal component analysis. Finally, the high-level expression semantic features are extracted and recognized by WACNN which is optimized by HGA. In order to evaluate the effectiveness of WACNN, experiments on JAFFE, CK+, and static facial expressions in the wild 2.0 databases are carried out by using ${k}$ -fold cross validation, and experimental results show the recognition accuracies of the proposal are superior to that of the state-of-the-art, such as local directional ternary pattern and weighted mixture deep neural network (DNN), which aim to extract discriminative and are the DNN-based methods. Moreover, recognition accuracies of the proposal are also higher than the deep CNN without HGA, which indicates that the proposal has better global optimization ability. Meanwhile, preliminary application experiments are also carried out by using the proposed algorithm on the emotional social robot system, where nine volunteers and two-wheeled robots experience the scenario of emotion understanding. Application results indicate that the wheeled robots can recognize basic expressions, such as happy, surprise, and so on. [ABSTRACT FROM AUTHOR]

The abnormal operating mode of the iron ore sintering process will produce sinter ore with low yield and poor quality. It is of high economic value to ensure that the sintering process runs under normal operating mode. An intelligent decision-making strategy based on the forecast of the abnormal operating mode for the iron ore sintering process is presented in this paper. First, a fuzzy rule-based model is used to construct the forecast model of operating mode, of which inputs are selected by the one-way analysis of variance. Then, an intelligent decision-making strategy for operating parameters is proposed based on the priority. Finally, experiments are performed by using the actual running data collecting from the industrial site. The originality of this study comes with establishing a fuzzy rule-based model to forecast the operating mode and designing an intelligent decision-making strategy based on priority to improve the abnormal operating mode. The result shows that the constructed forecast model of operating mode forecasts well in abnormal operating mode. The proposed intelligent decision-making strategy can effectively improve abnormal operating mode, which has a good application prospect in the iron ore sintering process. • An intelligent decision-making framework based on the forecast of abnormal operating mode is designed. • A fuzzy rule-based model is used to build the forecast model of operating mode. • An intelligent decision-making strategy of operating parameters based on priority is proposed. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of designing a two-dimensional (2D) modified repetitive-control system based on a Takagi–Sugeno (T-S) fuzzy model to achieve high tracking performance for a nonlinear plant. First, a nonlinear plant is represented by a T-S fuzzy model, and a modified repetitive controller with two repetitive loops is used to increases design flexibility. Next, a continuous-discrete 2D model is established to make use of the 2D characteristics in the modified repetitive-control system. Then, a sufficient stability condition is derived in terms of linear matrix inequalities. Three parameters are used to balance continuous control and discrete learning actions: one in a repetitive loop and two in a Lyapunov–Krasovskii functional. A particle swarm optimisation algorithm yields optimal parameters and the gains of the modified repetitive and state-feedback controllers. Finally, simulation and comparison results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The disturbance rejection problem of T–S fuzzy systems is concerned. Since the T–S fuzzy system is characterised by its membership function, less conservative stabilisation conditions can be derived from membership function-dependent Lyapunov function which contributes to the improvement of disturbance rejection performance. Specifically, we utilise a configuration composed of a membership function-dependent state observer for the state estimation, a membership function-dependent equivalent-input-disturbance estimator for the estimation and compensation of disturbance and an internal model for the reference tracking. It is revealed that this membership function-dependent Lyapunov function naturally leads to control gains switching in accordance with the derivative signs of the normalised premise variables. The switching rules and the design conditions for all control gains are obtained explicitly. In particular, the free-weighting-matrix approach is used to lessen the conservatism in the stability condition. Moreover, a concrete procedure for the controller design including the switching rule is given. Finally, the developed method is tested via simulations. The advantage of the membership function-dependent equivalent-input-disturbance method is validated by comparing with conventional methods. [ABSTRACT FROM AUTHOR]

Controlling weight-on-bit is essential in the drilling process, which determines the rock-breaking performance, drill bit wear, and wellbore quality. This paper aims to develop a weight-on-bit control system for field implementation, using a variable-gain integrated controller and a multiple-model parameter estimator. First, a weight-on-bit dynamic model for controller synthesis is derived according to the real drilling scenario. Then, the integrated controller is designed with two parts: a variable-gain proportional–integral controller and a dynamic output feedback controller, which separates the design of the system's high-order from low-order parts. The controller gain is tuned through a multiple-model parameter estimator, which estimates uncertain parameters related to rock strength. The established control system is implemented in a typical electrical drilling rig. It meets the requirement of adapting to different formations, considering the flexible drill string and the variable feed motor speed threshold. Numerical and field application results confirm the effectiveness of the developed system. [ABSTRACT FROM AUTHOR]

Plate shape recognition in the roller quenching process provides effective feedback signals for plate shape control. Its accuracy directly affects the control effect. Most studies have obtained the high recognition accuracy of the plate shape defects by neural networks or parameter identification methods, but cannot provide a sufficiently detailed feedback signal with controllable accuracy for plate shape control, limiting the further improvement of the control effect. For these problems, a plate shape recognition method for roller quenching process is proposed. Firstly, the roller quenching process and the representation of the plate shape are introduced, and the structure of the plate shape recognition method is designed. Secondly, the Gaussian function is used to describe the plate shape defects, and the physical significance of each parameter in the Gaussian function is analyzed. Based on the Gaussian function, a plate shape fitting method with iterative structure and controllable fitting accuracy is designed to extract the detailed information about each defect. Thirdly, according to the actual production experience, the expert rules are designed for plate shape classification and to evaluate the quality. Finally, the method is verified by industrial production data. The results verify the correctness and effectiveness of our method, indicating that the method can provide important guidance for the improvement of plate shape quality and lays an important foundation for plate shape control. • An plate shape fitting framework is proposed with controllable fitting accuracy. • Higher classification accuracy is obtained by integrating the expert rules. • The algorithm process is simple and easy to implement in the industrial field. [ABSTRACT FROM AUTHOR]

The build-up rate of the bottom hole assembly (BHA) fluctuates violently during the deviation correction in the vertical drilling process due to the mutation of stratum, low operation accuracy of the BHA, etc. As a result, the uncertainty of the build-up rate of the BHA has great influence on the performance of correction control and the quality of the drilling trajectory. To deal with this control problem, a tube-based adaptive model predictive control (MPC) method is developed in this article. By dividing the trajectory extension model into two lower order models, the calculability of the disturbance invariant set of the tube-based MPC controller is ensured. Meanwhile, the bootstrap sampling method with a sliding window is used to estimate the range of the uncertainty online to reduce the conservativeness of the controller based on practical drilling data. Moreover, rules and a small interim deviation are introduced to adjust the parameters adaptively to decrease the curvature of the drilling trajectory and the calculation time. Simulations and hardware-in-the-loop experiment tests are carried out to illustrate the effectiveness of the control method. The results show that the proposed method completes the drilling task with a higher quality of trajectory and needs less time for calculation. [ABSTRACT FROM AUTHOR]

Accurate prediction of rate of penetration is a prerequisite for optimization of drilling parameters. However, characteristics such as multiple drilling conditions, inconsistency in data length and various drilling variables can lead to inaccurate prediction of rate of penetration in actual drilling process. Therefore, it is important to use appropriate methods to predict rate of penetration for the above characteristics. This paper proposes an online prediction method of rate of penetration based on drilling conditions identification. First, data preprocessing and correlation analysis are employed to handle various drilling variables to obtain optimal model inputs. Then, dynamic time warping method is used to solve inconsistency in data length. Further, for multiple drilling conditions, the fuzzy c-means and dynamic time warping are combined to identify drilling conditions. Next, different extreme learning machine models optimized by genetic algorithm are built to predict rate of penetration for different drilling conditions. Finally, the results involving actual data illustrate that the prediction error of the proposed hybrid method of fuzzy c-means, dynamic time warping, extreme learning machine and genetic algorithm meets the requirements. The proposed hybrid method outperforms the comparative methods in four performance metrics, which verify the rationality and necessity of considering drilling conditions when predicting rate of penetration. [ABSTRACT FROM AUTHOR]

The design of a trajectory is significant to the efficiency and safety of a drilling process. In this paper, wellbore stability, indicated by the safe mud weight, is introduced as constraints to formulate a drilling trajectory optimisation problem. The upper and lower bounds of the safe mud weight are analysed according to the real data of a well. To solve the optimisation problem with strict constraints, we devise a new multi-objective optimisation algorithm. The devised algorithm takes advantage of inverted generational distance and knee points, and improves the convergence and diversity of the obtained solutions by modifying the selection process. Simulation results show that the devised algorithm is effective on the drilling trajectory optimisation problem constrained to wellbore stability. [ABSTRACT FROM AUTHOR]

Burn-through point (BTP) is an essential parameter in the iron ore sintering process. Operators usually judge whether the current production is stable by monitoring the BTP. It comes with significant application prospects to predict the BTP accurately. A prediction model of the BTP with fuzzy time series is designed in this paper. First, the fuzzy time series prediction method with the Fuzzy C-Means clustering is presented as the core modeling method. A prediction model of the response is constructed to obtain a timely response to the current BTP. The prediction model of the difference is established to estimate the present unmeasurable disturbance on the BTP. Then, a hybrid prediction model is built, which realizes the composition of these two models by an adjustment factor. Finally, a series of experiments is carried out using the raw time series data from an iron and steel plant. The experimental result shows that the designed model has better prediction performance for the BTP than existing models, which is an advantage resulting from the hybrid structure and the fuzzy time series prediction model with the Fuzzy C-Means clustering. This prediction model of the BTP implies the foundation for the stable control of the iron ore sintering process. • A fuzzy time series prediction model with the Fuzzy C-Means clustering is designed. • A hybrid prediction model of the burn-through point is developed. • The proposed model has a higher prediction accuracy than the existed models. [ABSTRACT FROM AUTHOR]

With the increase of drilling depth, complicated geological environments lead to a high risk of loss and kick. Fault diagnosis plays an essential role in minimizing the financial and environmental losses of the drilling process. On account of the temporal correlation of drilling parameters, a fault diagnosis method based on feature clustering of time series data for loss and kick of the drilling process is presented in this paper. Distance correlation is conducted for parameter combination to retain the whole information of drilling process. Global trend, local trends, and approximate entropy features are extracted to illustrate the characteristic of the time series. Density-based clustering method is performed for each combination to mine the local similarity among drilling parameters. Based on the clustering results of each combination as the inputs, the Bayesian classifier is further utilized to obtain the final fault diagnosis result. Experiments are executed with the actual data collected from a practical drilling process. The results indicate that the proposed method has both low false alarm rate and low miss alarm rate. • Distance correlation is applied to discovery the nonlinear relationships of drilling parameters. • Local trend features are introduced to illustrate the internal characteristic of the time series. • Density-based clustering is conducted to mine the local similarity among drilling parameters. [ABSTRACT FROM AUTHOR]

Proper burden distribution is an important way to improve conditions of a blast furnace, which indicate the stability and energy consumption. However, it is difficult for operators to recognize the stability and energy consumption at the same time and adjust them through burden distribution, due to complex iron-making mechanism. Therefore, this article presents a two-stage decision-making method for the burden distribution parameters based on recognizing the conditions. First, the stability and energy consumption are recognized, respectively, according to the classification of stability of slag crust (SSC) and the gas utilization rate (GUR). And then, two prediction models are presented for the SSC and GUR to predict the conditions. Finally, the burden distribution parameters are determined in two stages, where edge parameters of the burden distribution are determined at first according to the SSC, and then, center parameters are determined according to the GUR. Experimental results based on industrial data show that the proposed method provides an effective decision-making way for improving the conditions. [ABSTRACT FROM AUTHOR]

• The different kinds of outliers in drilling process are all detected, which need to be handled differently. • The causes of anomalies are discriminated intelligently and effectively by the DTW-FCM. • While improving the data quality, the data with drilling accident characteristics is reserved. • The performance of PNN-based monitoring models are improved by the discrimination and correction. During the drilling process, the data quality influences the reliability of condition monitoring results. However, the actual drilling data may encounter various kinds of abnormal data, and different kinds of them need to be handled differently. The abnormal data caused by external factors such as sensor failure, storage errors, etc., should be corrected, while the abnormal data caused by drilling accidents should be protected. Therefore, not only the anomaly detection is needed, but the causes of anomalies should be further discriminated. This paper proposes a method for discrimination and correction of abnormal data in the drilling process. First, the local outlier factor anomaly detection algorithm is developed to detect all kinds of the abnormal data. Then, the dynamic time warping and fuzzy c-means are combined for the discrimination of causes of anomalies. Finally, the discriminated abnormal data caused by external factors are corrected with the k nearest neighbor interpolation. Simulation results involving actual data illustrate that the causes of anomalies can be discriminated effectively, and the monitoring results of monitoring models based on neural network improve after using the proposed method, which verify the necessity of anomaly discrimination. [ABSTRACT FROM AUTHOR]

This study presents a repetitive control method based on a multi-stage particle swarm optimization (PSO) algorithm with variable intervals to enhance the tracking performance of Takagi–Sugeno (T–S) fuzzy systems. First, a T–S fuzzy model is used to describe a nonlinear system. A modified repetitive control structure with two repetitive loops guarantees the tracking accuracy of periodic signals. Taking advantage of the two-dimensional (2D) property with continuous control and discrete learning, a continuous-discrete 2D model is presented to describe the nonlinear repetitive control system. Next, a multi-stage PSO algorithm with variable intervals searches for the best parameter combination in the linear matrix inequality-based stability condition to regulate the control and learning actions, which avoids a suboptimal solution and guarantees high control accuracy. Finally, an application to control the speed of synchronous motor with a permanent magnet demonstrates the validity of the method, and comparisons with related methods show its superiority. [ABSTRACT FROM AUTHOR]

Suppressing torsional vibration of drill-string has great safety and economic importance to drilling process. Note that most existing control methods only measure surface information, their performance are limited, especially for a long drill-string. In this paper, an advanced control system is developed to mitigate torsional vibration of drill-string, using a measurement-while-drilling (MWD) communication tool to collect downhole information. The drill-string is described by a lumped parameter model to include full torsional flexibility modes with less computational complexity. A state observer and a filter are combined to estimate an equivalent control torque to cope with uncertain bit–rock interaction. Considering that the communication and power resources are quite precious for MWD, the downhole state is periodically sampled and an event-triggered mechanism is devised to pick out only some key sampled data for transmission to the surface. Using both the surface continuous measurement and the discrete MWD data with transmission delays, the closed loop of the drill-string and the control system is modeled as a nonlinear time-delay system, based on which sufficient conditions are derived for stability analysis and calculation of gain matrices. A numerical case study is carried out to verify the effectiveness of the approach. • An event-triggered scheme is devised for measurement-while-drilling communication. • A vibration control system is developed using both surface and downhole information. • A state observer and a filter are combined to cope with uncertain rock reaction. • Delay-dependent practical stability of the closed loop system is analyzed. [ABSTRACT FROM AUTHOR]

This paper concerns with trajectory azimuth control in directional drilling. The motion process of the drill bit and a series of stabilizers are described, and a state-space model of the trajectory azimuth is constructed. The scheme of the trajectory azimuth control system is designed based on the equivalent input disturbance approach. An internal model is inserted to track the drill bit to improve the quality of the drilling trajectory. A state observer is combined with a low-pass filter to estimate the trajectory azimuth by measuring the azimuth of the bottom hole assembly (BHA). The control parameters can be obtained by the condition of system stability, which is derived in terms of linear matrix inequalities. A typical case is used to illustrate the validity and robustness of our approach. [ABSTRACT FROM AUTHOR]

With the rapid development of control technology, increasing applications are using model predictive control (MPC) for deviation correction in vertical drilling. However, the accuracy of the predictive model is affected by the uncertainty of the stratum, which results in model mismatch and a reduction in control performance. In this paper, an intelligent compensating method is proposed for MPC-based deviation correction with stratum uncertainty in a vertical drilling process to increase control accuracy. First, a trajectory extension model is introduced as the predictive model for MPC, and the uncertainty of the stratum is discussed. Then, the compensation for the MPC is acquired based on a Gaussian fitting method and hybrid bat algorithm. Finally, based on the actual drilling data, a simulation is performed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Operators often make different control decisions for different operating modes to meet the production requirement of the iron ore sintering process. Recognizing the operating modes is important to improve the quality and quantity of the sinter ore. An operating mode recognition method based on the clustering of time series data for the iron ore sintering process is presented in this paper. First, the Spearman rank correlation analysis and the information entropy analysis are combined to select parameters. Next, the operating mode recognition submodel is built by the fuzzy C-Means clustering method based on dynamic time warping distance and the naive Bayesian classifier method. Then, the outputs of the submodels are fused to obtain the final recognized operating mode. Finally, the productivity and combustion efficiency are regarded as the classification criteria, and the raw data collected from an iron and steel plant are used for the experiment. The experimental results show that the proposed method can effectively recognize the operating mode of the sintering process. • An operating mode recognition method for the sintering process is presented. • The Fuzzy C-Means clustering based on the dynamic time warping distance is used. • The naive Bayesian classification method is used to identify the operating mode. [ABSTRACT FROM AUTHOR]

This paper develops a quick and effective continuous state feedback control strategy based on intelligent optimization for a planar three-link passive–active–active (PAA) underactuated system with first-order nonholonomic characteristic. We analyze the integral characteristic of the system and find the passive link will be stabilized at different angle when the two active links of the system are controlled to different angles. Therefore, we design a set of continuous state feedback controllers based on the control targets of the two active links, and then we optimize the target angles of all links and the design parameters of the above controllers by employing intelligent optimization algorithm. In this way, the system can be stabilized at the target point by using these controllers with optimized parameters. Finally, the simulation results demonstrate that the continuous control strategy based on intelligent optimization may make the planar PAA underactuated system be stabilized at any target point from any initial point. [ABSTRACT FROM AUTHOR]

Stick–slip vibrations are widespread and hazardous phenomena in drilling practice. The objective of this paper is to develop a simple and general control strategy to minimise such vibrations together with unknown disturbances. Taking into account the transmission delay of the torsional energy from the surface to downhole, a neutral-type drill-string model is established and applied rather than a lumped-parameter model that cannot provide satisfactory modelling precision. Based on this neutral-type model, the major contribution consists of the combination of disturbance estimator and smith predictor to develop a robust vibration control scheme for drill-string systems. One conspicuous advantage of this scheme is that the effects of stick–slip vibrations and external disturbances are estimated without the need for any prior knowledge in the control input channel, and are offset correspondingly. Simulations confirm that this control scheme can achieve effective suppression of stick–slip vibrations with good tracking and disturbance rejection performance. [ABSTRACT FROM AUTHOR]