A low-pass filter is essential for stabilizing strictly proper repetitive-control systems, but it inevitably degrades steady-state tracking accuracy due to gain attenuation and phase lag. This article presents a new filter design and optimization method that improves both transient response and steady-state accuracy in continuous-time repetitive-control systems. First, the gain and phase characteristics of conventional filter-based repetitive controllers are rigorously analyzed to reveal the relationship between filter parameters and tracking performance. Based on this analysis, a new filter structure is designed to precisely compensate for gain attenuation and phase delay, specifically at the fundamental frequency, by minimizing the error term without increasing the filter bandwidth. A guideline for selecting the filter parameters for varying periodic trajectories is also provided. In addition, according to the one-to-one mapping between control and learning behaviors and their respective gains, dual performance indices are constructed to account for tracking error and control effort across multiple learning cycles. A multiobjective optimization framework is then developed to directly tune these gains subject to stability constraints, achieving an optimal balance between rapid transient convergence and control energy efficiency. Experimental results validate the effectiveness and superiority of the design.

During drilling process, variations in formation hardness and frictional resistance often cause a mismatch between drilling operating parameters and actual conditions, reducing efficiency. A fuzzy decision-making strategy for drilling operating parameters is developed that enables feed speed and rotational speed to adapt to actual conditions. The formation hardness is accurately characterized using only drilling data through the integration of fuzzy C-means clustering method and defuzzification method. Meanwhile, the computational model for frictional resistance is established. They serve as inputs for subsequent decision-making models. The decision-making models for feed speed and rotational speed are developed based on the Mamdani fuzzy inference method, respectively. The effectiveness of the approach is demonstrated through an industrial case study based on actual drilling data.

In coal seam drilling, wall friction along the borehole introduces complex, spatially distributed disturbances that degrade the dynamic performance of the drill string. Due to its slender and flexible structure, the drill string is highly sensitive to such multi-point excitations, leading to frequent velocity fluctuations and reduced tracking accuracy. To address this, a robust H-infinity control strategy is proposed. A disturbance weighting function is designed to give the controller notch-filter characteristics, enabling targeted suppression of resonance-induced vibrations. Relying only on inlet measurements, the controller ensures accurate tracking of the reference feeding speed while effectively mitigating wall friction effects. Simulation results show significant improvements in steady-state accuracy, disturbance rejection, and robustness compared to conventional methods, confirming the effectiveness of the proposed approach.

Measurement of ocean current velocity profiles is critical for marine operations, yet traditional methods such as Acoustic Doppler Current Profilers face limitations in cost, resolution, and penetration depth. Existing inversion techniques often rely on idealized assumptions limiting their practical applicability. To address these challenges, this paper proposes stratified hybrid convolutional network, a novel deep learning-based soft sensing method for reconstructing ocean current velocity profiles. The proposed method leverages a physically guided stratification of ocean currents into distinct layers based on temperature-salinity-density correlations and their driving mechanisms. An adaptive feature selection algorithm mitigates overfitting by filtering redundant inputs and aligning features with layer-specific dynamics. A hybrid neural network architecture combines two-dimensional convolutions along depth and feature dimensions to capture nonlinear relationships, followed by multi-layer perceptrons for high-dimensional nonlinear mapping. Experimental results demonstrate superior performance. This method provides a practical solution for the inversion of ocean current velocity profiles.

This paper addresses the passivity issue of neural networks with a time-varying delay. We introduce an allowable delay set flexible fragmentation approach for constructing a novel Lyapunov–Krasovskii functional (LKF). Unlike some existing methods, this LKF is more flexible as it allows for different Lyapunov matrices in different allowable delay subsets. Based on the proposed LKF, and utilizing integral inequality approaches and zero equation techniques, several passivity criteria are derived for neural networks with a time-varying delay. Two numerical examples are finally provided to demonstrate the advantages of the proposed method.

This article presents a two-degree-of-freedom structure of a fuzzy repetitive-control system that accurately tracks periodic signals and effectively suppresses aperiodic disturbances based on an enhanced equivalent-input-disturbance (EEID) method. To make full use of the learning characteristic, a 2-D model of the fuzzy repetitive-control system is established to independently regulate the control and learning behaviors. The EEID approach decouples the design of the observer and the estimator by adding a flexible controller to the estimator. This makes it possible to simultaneously reduce the estimated and filtered errors, which is a contradictory problem in the conventional EID method. The two errors cannot be eliminated due to the lack of an internal model of the external disturbance. To actively cancel the negative influence of the two errors, a new observation-error compensator is added to the observer input to improve the disturbance-suppression performance. Two low-conservative linear-matrix-inequality stability conditions are derived using the Lyapunov–Krasovskii functionals and zero equations with free-weighting matrices. The nondominated sorting genetic algorithm II and the particle swarm optimization algorithm are used to optimize the parameters of the controllers for the tracking and disturbance-suppression systems based on the stability conditions and performance indexes. The effectiveness and superiority of the design are verified by numerical simulation and comparison results.

In practice, repetitive control (RC) is a type of learning control that exhibits good tracking performance. However, existing nonlinear RC methods lack analysis and design of the learning property, which results in limited performance. This study addresses the learning-enhanced issue. First, a new fuzzy Lyapunov candidate is constructed for stability analysis, which contains an integral term associated with the membership function and a double integral term. The design of the learning-dependent term integrates the nonlinear membership function information, which enhances the learning ability. Second, an additional first-order low-pass filter is incorporated into the conventional equivalent input disturbance estimator. The new filter acts as an integrator that adjusts the bandwidth of the disturbance compensation and gradually eliminates the disturbances in the output error. Third, a recursive optimization algorithm is used to design the controllers. Experimental comparisons on motor drive systems demonstrate the effectiveness and superiority of the method.

Severe drill-string vibration is a significant source of drilling problems. Most existing methods for vibration recognition rely on downhole data, facing great limitations in practice. In addition, complex and changeable formations result in limited ability of single-scale features to characterize the vibration. To resolve these issues, an end-to-end vibration recognition model using only surface drilling data is proposed based on multiscale features extraction. A multiscale temporal convolutional network is developed to extract multiscale temporal features of multisensor data, enhancing the vibration representation capability to adapt to complex formations. To further improve recognition capability, the bidirectional long short-term memory network is utilized to obtain contextual linkage of multiscale features. Experiments conducted with field data have verified the efficiency of proposed method. It has achieved 97% accuracy, and outperforms existing methods. Furthermore, compared with the recognition result based on single-scale features, it has improved by 6% in accuracy. The proposed method provides automated diagnostics of drill-string vibration.

This article is concerned with the balance between improving rate-of-penetration and suppressing drill-string vibrations by optimizing weight-on-bit and rotational speed. The main contribution of this article lies in three points. First, due to the lack of downhole data, the problem of suppressing drill-string vibrations is transformed into reducing state fluctuation of drill-string system. Meanwhile, a multiobjective optimization scheme is established. Second, the temporal relations are revealed, including modeling time, optimization time, resampling time, controller response time, buffer time, and optimization interval. They act as constraints for subsequent modeling and optimization. Third, the improved extreme learning machine methods are developed to build the calculation models of rate-of-penetration and state fluctuation of drill-string system, separately. The nondominated sorting genetic algorithm II, technique for order preference by similarity to an ideal solution method, and moving window are used to obtain the optimal weight-on-bit and rotational speed. The effectiveness of the approach is demonstrated through an industrial case study based on the actual drilling data.

It is inevitable to encounter through different formations in the drilling process for deep exploration, and the penetration resistance coefficient (PRC) is an uncertain parameter related to lithology. In this article, a parameter-estimation-based gain-scheduling controller is developed to eliminate undesired system performance deterioration due to the uncertain parameter. First, a drill-string axial finite element model with the uncertain PRC is established, and a control-oriented low-order model is derived via mode selection. A gain-scheduling controller is computed based on the quadratic stability condition of the closed-loop system, which can cope with the system's parameter uncertainty using variable low-frequency gain. An adaptive observer is designed to estimate the unmeasurable scheduling variable. Field data from a geothermal drilling well is obtained to validate our model. According to this drilling well scenario, both numerical and experimental results illustrate the effectiveness of our method. The explicit relationship between the controller gain and the uncertain parameter is presented. It is found that the performance of the closed-loop system is more sensitive to the controller gain when drilling in soft formations, requiring more attention in such scenarios.

This study presents a two-dimensional (2-D) repetitive control method to address the issues of periodic tracking and disturbance suppression in uncertain Takagi–Sugeno systems. The disturbance and uncertainty are treated as an equivalent-input-disturbance (EID). However, the conventional EID estimators typically suppress the EID through high gain. Meanwhile, the low-pass filter associated with EID causes a certain degree of phase lag. A proportional–integral (PI) filter is integrated with an EID estimator to develop a PI-EID structure to improve the estimation accuracy. Based on the self-learning mechanism of repetitive control, the 2-D repetitive controller is used to achieve a high level of tracking. Unlike the conventional nonlinear repetitive control methods, the state observer and the PI-EID estimator are membership function dependent. The gains of both controllers switch in line with the signs of the time derivative of the normalized premise variables, and this framework takes full account of the information of the nonlinear membership functions. The controller design procedures and the stability conditions are detailedly presented. Finally, a rotation speed control experiment is conducted to validate the developed PI-EID method.

Data-driven fault diagnosis methods have been widely applied at present. In actual processes, there usually exist multiple failure modes; the data frequency spectrum varies in different failure modes, which would bring challenges for feature extraction and subsequent fault diagnosis. In this article, a decision fusion scheme based on the mode decomposition and evidence theory is proposed for fault diagnosis during drilling. The raw data are decomposed into multiple series with different center frequencies, the decomposed series are reconstructed to several groups. For each group, the local diagnosis model is established, thus, several local diagnostic results are obtained. Then, all local diagnostic results are fed into the evidence theory-based decision fusion model. Meanwhile, a confidence matrices-based weight adjustment method is designed to enhance the reliability of fused results. An industrial case study based on the actual drilling data verifies that the proposed method is beneficial to improve the diagnostic effect during drilling.

In this article, a coupled axial-torsional drill-string vibration model is established, which considers the influence of bit balling on rock-breaking and drill-string vibration. First, a bit-rock interaction model considering normal drilling and bit balling conditions is established, where the different rock-breaking conditions are classified based on the Gaussian mixture model. The fuzzy method is used to solve clustering inaccuracies problems in the bit balling formation and disappearance phases, and a fuzzy bit-rock interaction model is established in these two phases. The bit-rock interaction modeling method has higher accuracy in describing bit balling than the Real, Ritto, and Karnopp models. Finally, the coupled axial-torsional drill-string vibration model considering bit balling is established based on the established bit-rock interaction model, using the drilling tool parameters of a geothermal well and the bit-rock interaction model parameter identification result. The effectiveness of the modeling method is illustrated by micro-rig experiments and field data verification.

In the geological drilling process, predicting the rate of penetration (ROP) is significantly important for improving drilling efficiency and reducing nondrilling time. However, due to the drilling data pollution and the complex nonlinearity in the geological drilling process, a reliable and highly accurate ROP prediction model is not easy to construct, and the model accuracy is affected by the value of model hyperparameters. In order to overcome the difficulties in modeling, a novel ROP model is developed to handle abnormal data and achieve nonlinear fitting. First, a local outlier factor is introduced to automatically detect the abnormal data, and then, replace it with the nearest normal data. Then, the support vector regression (SVR) method is applied to construct nonlinear prediction model for ROP, and a modified bat algorithm (MBA) is developed to solve the non-convex problem in determining optimal value of hyperparameters for SVR-based ROP model. The MBA has six modifications to improve the global search ability, which achieves better performance in global search ability compared with other nine algorithms based on the experiments of IEEE 2005 benchmark functions. The developed ROP prediction model that combines SVR and MBA methods is tested based on actual drilling data and a semiphysical system, and the simulation and application results show that the developed modeling method has higher ROP prediction accuracy compared with the other modeling methods.

The safe and efficient operation of geological drilling systems is critically dependent on proper process monitoring. A monitoring model based on probabilistic principal component analysis is presented to deeply exploit performance feature information hidden among the process data in this paper. First, the offline monitoring model is established with historical process data collected from different sources to reveal different characteristics, and the monitoring statistics as a benchmark are obtained. Then, actual operating data are introduced to the established model to realize online monitoring. Finally, the monitoring effect is discussed, and the causes of inefficient drilling are tracked. The experimental results indicate that the proposed method can effectively monitor the operating performance of the drilling process.

The aim of this paper is to implement a robust weight on bit control design in an unified experimental system. A laboratory drilling rig is used to simulate the real world bit-rock interaction. Via communication between programmable logic controller (PLC) and OLE for Process Control (OPC) server, the laboratory drilling rig can exchange data with a finite element drill-string model and a hoisting system model which are realized in MATLAB/Simulink. Thus, an unified experimental system is established to reproduce the actual drilling process. The penetration resistance coefficient of the experiment rock sample is tested to obtain the bit-rock interaction model for controller design. Considering the high-order mode of the slender drill-string, a reduced order model with multiplicative weighting function is derived. Based on the developed model, a robust integrated controller is obtained by connecting a PI controller and a dynamic output feedback controller. Experiment results are presented to show the effectiveness of our method and the established system.

This article investigates H∞synchronization of delayed neural networks under a receding horizon scheme, where two types of interval time-varying delays are considered according to whether the lower bound of the delay derivative is known or not. Note that a receding horizon synchronization law can be regarded as an optimization solution at each timeslot to a minimaxization problem related closely with a certain cost functional. In this article, two cost functionals with some delay-dependent matrices are introduced, respectively, for the two types of time delays. In order to obtain less conservative conditions, a novel inequality on quadratic polynomial functions is established, which includes some existing ones as its special cases. Based on the novel inequality, two sufficient conditions are derived to design the terminal weighting matrices of the cost functionals such that the resulting synchronization error system can be stabilized with a prescribed infinite horizon H∞performance level. Finally, three numerical examples are used to demonstrate the validity of the proposed results.

This paper is concerned with the stubborn state estimation of delayed neural networks that subject to a general class of disturbances in measurements, including outliers and impulsive disturbances as its special cases. This class of disturbances may be unbounded, irregular, and assorted; therefore, they can hardly be suppressed by existing identification-based estimation approaches. In this paper, a stubborn state estimator is constructed by intentionally devising a saturation scheme on the injection of output estimation error. The embedded saturation can effectively resist the influences from these measurement disturbances by saturating them. Moreover, the saturation threshold in the designed scheme is not constant but governed by a dynamic equation with parameters to be designed. Benefiting from this adaptiveness, the estimator obtains more freedom in dealing with various disturbances. By combining a novel Lyapunov functional, the generalized sector condition and two latest integral inequalities, a delay-dependent criterion is derived in a less conservative way to check whether the estimation error system with this dynamic saturation is globally stable. A sufficient condition with two tuning scalars is further provided to codesign the gain of the state estimator and the evolution law of the saturation threshold. Finally, two numerical examples are used to illustrate the stubbornness of this state estimator in the presence of measurement outliers or impulsive disturbances.

This paper is concerned with energy-to-peak state estimation on static neural networks (SNNs) with interval time-varying delays. The objective is to design suitable delay-dependent state estimators such that the peak value of the estimation error state can be minimized for all disturbances with bounded energy. Note that the Lyapunov-Krasovskii functional (LKF) method plus proper integral inequalities provides a powerful tool in stability analysis and state estimation of delayed NNs. The main contribution of this paper lies in three points: 1) the relationship between two integral inequalities based on orthogonal and nonorthogonal polynomial sequences is disclosed. It is proven that the second-order Bessel-Legendre inequality (BLI), which is based on an orthogonal polynomial sequence, outperforms the second-order integral inequality recently established based on a nonorthogonal polynomial sequence; 2) the LKF method together with the second-order BLI is employed to derive some novel sufficient conditions such that the resulting estimation error system is globally asymptotically stable with desirable energy-to-peak performance, in which two types of time-varying delays are considered, allowing its derivative information is partly known or totally unknown; and 3) a linear-matrix-inequality-based approach is presented to design energy-to-peak state estimators for SNNs with two types of time-varying delays, whose efficiency is demonstrated via two widely studied numerical examples.

Demands for cloud video surveillance systems are growing rapidly. Addressing to the issue of low energy-efficiency in cloud video datacenters, a task scheduling method using a time-clustering-based genetic algorithm is proposed. Firstly, an off-line scheduling model with SLA (service level agreement) time constraint is proposed after the analysis of the constrain relationship between the SLA and surveillance tasks. Then, a time-clustering-based genetic algorithm (TCGA) is proposed to solve the model for an optimal energy-efficient solution. According to the solution, the service quality is guaranteed and the total operating time of virtual machines is minimized. Meanwhile, idle virtual machines are shut down to reduce energy consumption. Simulations of large scale tasks scheduling are conducted. Several comparison experiments verify that the proposed method can improve the resource utilization greatly and achieve energy saving extremely.