Adaptive output-feedback control of torsional vibration in off-shore rotary oil drilling systems

Wang, Ji; Krstić, Miroslav

doi:10.1016/j.automatica.2019.108640

Cited by 47 publications

(19 citation statements)

References 32 publications

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“…In this study, we just consider the matched disturbance in order to simplify the control design. When the disturbance or uncertainty appears at the uncontrolled end or the governing equations, the control scheme can be designed based on PDE backstepping methods [38,39].…”

Section: Dynamics Analysismentioning

confidence: 99%

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Liu

Han

Zhao

et al. 2021

Sci. China Inf. Sci.

184

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In this paper, we address simultaneous control of a flexible spacecraft's attitude and vibrations in a three-dimensional space under input disturbances and unknown actuator failures. Using Hamilton's principle, the system dynamics is modeled as an infinite dimensional system captured using partial differential equations. Moreover, a novel adaptive fault tolerant control strategy is developed to suppress the vibrations of the flexible panel in the course of the attitude stabilization. To determine whether the system energies, angular velocities and transverse deflections, remain bounded and asymptotically decay to zero in the case wherein the number of actuator failures is infinite, a Lyapunov-based stability analysis is conducted. Finally, extensive numerical simulations are performed to demonstrate the performance of the proposed adaptive control strategy.

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Section: Dynamics Analysismentioning

confidence: 99%

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Liu

Han

Zhao

et al. 2021

Sci. China Inf. Sci.

184

View full text Add to dashboard Cite

show abstract

“…Remark Assumption 1 is a basic hypothesis in robust control of DPSs 49‐51 . Both the distributed load

f (x, t)

and boundary disturbance

d (t)

are generated by the ocean wave, wind, and current.…”

Section: Problem Formulationmentioning

confidence: 99%

Stabilization control of a flexible marine riser with failed and bounded actuator and time‐varying boundary constraints

Han

Liu

2021

Intl J Robust & Nonlinear

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In this article, we investigate the stabilization control problem of a flexible marine riser system with a failed and bounded control action and time‐varying boundary constraints. The considered system is modeled as a distributed parameter system dominated by a partial differential equation. The involved problems are extended to some more general conditions. The saturation and failures of the actuator make the input and output of the actuator a nonlinear relationship, which leads to the normally designed control input invalid. By applying the Nussbaum gain technique, we report a synthetical control method to tackle the input nonlinearity. A piecewise barrier Lyapunov function (BLF) is introduced to reduce the boundary displacement in an asymmetric open set. Compared with the traditional BLFs, the initial boundary displacement is not forced to retain within the given region by integrating a shifting function into the BLF design. In addition, the adaptive method is combined with the hyperbolic tangent function to provide a smooth control solution to the unknown parameters from nonlinear input and external perturbation. Relying on a Lyapunov‐based analysis and a numerical simulation, it is proven that the actuated riser system is uniformly bounded and the boundary displacement always keeps in the limited set even subject to actuator failures and limitations.

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“…Compared to existing stuck pipe prediction approaches, the new proposed approach can distinguish the well-cleanup-related stuck pipe risk and other reasons for stuck pipes [20]. Recently, research on an oil drilling system with a stick-slip phenomenon based on wave Partial Differential Equation (PDE) models was carried out to design an adaptive controller, which is used in torsional vibration suppression for an oil drilling system with uncertain stick-slip instability and disturbances at the drilling bit [21]. A newly developed BN model is capable of probability adapting and probability updating including predictive analysis and diagnostic analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

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Adaptive output-feedback control of torsional vibration in off-shore rotary oil drilling systems

Cited by 47 publications

References 32 publications

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures

Stabilization control of a flexible marine riser with failed and bounded actuator and time‐varying boundary constraints

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

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