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Beam pumping units play a key role in oil extraction. There is an increasing demand for optimal oil-extracting performance as operational environments are becoming more challenging and complex. Therefore, it is vital to create an acceptable mathematical model and develop robust control mechanisms. In the past decades, researchers have achieved fruitful results regarding modeling and control methods in this field. This article provides a comprehensive review of the research on modeling and control methods for beam pumping units, including mathematical models based on differential equations and boundary conditions, as well as the control strategies designed. Finally, future perspectives and recommendations are presented. [ABSTRACT FROM AUTHOR]

Fault data injection attacks may lead to a decrease in system performance and even a malfunction in system operation for an automatic feedback control system, which has motive to develop an effective method for rapidly detecting such attacks so that appropriate measures can be taken correspondingly. In this study, a secure descriptor estimation technique is proposed for continuous-time Lipschitz nonlinear cyber physical systems affected by actuator attacks, sensor attacks, and unknown process uncertainties. Specifically, by forming a new state vector composed of original system states and sensor faults, an equivalent descriptor dynamic system is built. A proportional and derivate sliding-mode observer is presented so that the system states, sensor attack, and actuator attack can be reconstructed successfully. The observer gains are obtained by using linear matrix inequality to secure robustly stable estimation error dynamics. Moreover, a robust descriptor fast adaptive observer estimator is presented as a complement. Finally, the efficacy levels of the proposed design approaches are validated using a vertical take-off and landing aircraft system. Comparison studies are also carried out to assess the tracking performances of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies. [ABSTRACT FROM AUTHOR]

This paper addresses video error concealment techniques, focusing on motion vector recovery of P- and B-frames, to improve the decoded quality of videos when bit stream data incurs transmission errors. First, we propose a dynamic programming (DP) technique to optimize the path cost in a multistage topology and evaluate the goodness of boundary matching and side smoothness of recovered macroblocks. However, due to the high computational complexity of DP, a suboptimal alternative enhanced with an adaptive Kalman filtering algorithm is adopted instead. Experiments show that by considering the side smoothness between adjacent recovered MBs, the proposed algorithm improves the reconstructed video quality by about 0.4-0.9 dB with a packet loss rate up to 15%, compared to a traditional boundary matching algorithm. In addition, subjective image inspection demonstrates the efficiency of the proposed algorithm in retaining the continuity of lines and image details. Index Terms--Boundary matching algorithm (BMA), dynamic programming (DP), greedy algorithm, Kalman filtering, video error concealment, video error resilience.

This note first points out that the main results by Wang and Balas regarding the doubly coprime fractional representations for generalized dynamical systems have severe limitation in their applications, that is, the doubly coprime factorization obtained by Wang and Balas cannot characterize the parameterization of all properly stabilizing controllers when a system is singular, therefore, truly generalized. To remedy those results, two new doubly coprime factorizations have been developed here that will parameterize all properly stabilizing controllers for single-input or single-output cases. In addition, the new results can characterize the parameterization of all corresponding causal properly stabilizing controllers. Finally, the extension to the multiple-input--multiple-output case is presented. Index Terms--Controller parameterization, coprime factorizations, generalized dynamical systems, proportional and derivative feedback.

This article builds a stress–strain prediction model based on production data from the steel industry by using machine learning algorithms. Based on the stress–strain data of 9Ni steel hot deformation behavior, the prediction model of flow stress constitutive equation of 9Ni steel is established. Four models, including Arrhenius-type model considering strain compensation, Arrhenius-type model of Stochastic Configuration Networks (SCNs) neural network, Arrhenius-type model of Multi-objective Particle Swarm Optimization (AMPSO) and Support Vector Machine (SVM) model, are adopted in this research. The results show that the Arrhenius-type model considering strain compensation can predict the stress trend under different deformation conditions, but its prediction accuracy has some deviation. The prediction model based on SVM algorithm has the best prediction accuracy. The square of Correlation Coefficient (R2), the Average Absolute Relative Error (AARE), and Mean Square Error (MSE) are 0.99996, 0.002455, and 0.1998, respectively. Based on the data of 9Ni steel hot deformation behavior, the prediction models of machine learning algorithm have good application prospects in steel industry. [ABSTRACT FROM AUTHOR]