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This article presents an active disturbance rejection adaptive precision pointing controller for all-electric tank bidirectional stability system in high speed driving environment. First, a comprehensive dynamic model for mechatronics and control integration of bidirectional stability system is modeled. Second, the backstepping idea is introduced to perfectly incorporate the parameter adaptive technique and the extended state observer to design a targeted control strategy. The matched and mismatched uncertainties of the system can be estimated separately with the construction of two extended state observers, while a novel adaptive function updated simultaneously via tracking and observation errors is synthesized to deal with the parametric uncertainties. Finally, theoretical analysis and extensive comparative results verify that the proposed controller is effective., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

In this article, the problem of high precision motion control for motor servo systems with modeling uncertainties and unknown actuator backlash is addressed. The combination of synthesized adaptive laws and continuous nonlinear robust term handles parameter uncertainties and system disturbances. The adaptive technique updates the unknown parameters of actuator backlash in real time and the backlash inverse function eliminates the backlash effect. Meanwhile, the designed controller without knowing the range of the disturbance upper bound but automatically estimates through the adaptive law, which improves the engineering practicability. Finally, the theoretical analysis proves the perfect asymptotic stability of the presented controller even with unmodeled disturbances and unknown actuator backlash. Extensive comparative experiments reveal the superiority of the presented method., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

This article proposes an asymptotic adaptive command filtered control approach for uncertain nonlinear systems with parametric uncertainties, mismatched and matched disturbances. To accomplish the task, a disturbance observer (DO) only with one tuning parameter is firstly used to attain the disturbance compensation. The parameter uncertainties can be addressed via composite updated laws. Then, by judiciously combining DO, adaptive control and command filter technique, a novel command filtered controller with adaptive-gain auxiliary systems is developed to attain asymptotic tracking and shun "explosion of complexity". The system stability is proved by utilizing the Lyapunov function. Extensive experimental results uncover the preponderance of the exhibited strategy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

Due to the nonlinearity and various uncertainties, the controller design for hydraulic servo systems with input constraint is more complicated and challenging. This paper first proposes an asymptotic tracking controller for electrohydraulic servomechanisms considering input constraint, parametric uncertainties, and unmodeled disturbances. The core innovation of this controller is to decouple the control input and the input nonlinearity while guaranteeing the nonlinear decoupling term and its derivative to be available and bounded. Meanwhile, the decoupling operation could be skillfully integrated to realize accurate adaptive model-based compensation while remaining the unique feature of asymptotic control for residual disturbances. For this purpose, based on the desired trajectory and the estimated disturbance via an extended state observer (ESO), a desired load pressure signal is constructed to replace the actual load pressure and accomplish the required decoupling operation. In this case, a desired adaptive feedforward compensation in combination with a robust integral of the sign of the error (RISE) feedback is proposed to attenuate parametric uncertainties and residual unmodeled disturbances, respectively. Subsequently, a smooth hyperbolic tangent function is integrated into the controller to handle the input constraint. Theoretical analysis proves that the developed control strategy can achieve semi-global asymptotic tracking performance. Besides, numerical simulations and experimental tests demonstrate that the proposed control scheme can ensure high-precision tracking performance and simultaneously satisfy the preset control input range when encountering the input constraint and modeling uncertainties., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Objectives: Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy., Methods: Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05., Results: Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04mm and 1.50mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89., Conclusions: This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images., Clinical Significance: Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard., (Copyright © 2022. Published by Elsevier Ltd.)

This paper presents an output feedback adaptive super-twisting sliding mode controller (SSMC) for hydraulic systems with unmodeled disturbances via utilizing an extended state observer (ESO). Both unmeasured system states and unmodeled disturbances are estimated by ESO based on output position signal, which avoids using noise-polluted signals and eliminates most of the disturbance effects on control performance simultaneously. Moreover, a SSMC is developed to further suppress the residual error of disturbance compensation, in which feedback gains are adapted online to further reduce the high-gain feedback. In addition, this proposed controller is continuous and chattering-free, which is beneficial to practical applications. Theoretical analysis indicates that the proposed controller ensures an asymptotic stability when existing constant disturbances, and ultimately bounded tracking performance for the time-variant disturbance case. Comparative experimental results reveal the validity of the developed approach., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

It is well known that hydraulic systems typically suffer from heavy disturbances including parametric uncertainties and unknown disturbances. In order to attain high performance tracking control, this paper proposes a composite design of nonlinear neural-networks (NN) and continuous robust integral of the sign of the error (RISE) feedback controller. The control development incorporates a NN feedforward component to have a compensation for unknown state-dependent disturbances and to further improve the accuracy of feedforward compensation, meanwhile input parameter is updated online. To achieve asymptotic stability, a novel RISE term with NN-based feedforward component is developed for the first time to enable the incorporation of model reference adaptive control structure where acceleration signal is not employed. The proposed controller guarantees controlled hydraulic system a semi-global asymptotic stability. For the experimental results, the prescribed transient performance is tested under rectangular trajectory and the steady state performance is tested under sinusoidal trajectory., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme., (Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper proposes a continuous adaptive integral robust control with robust integral of the sign of the error (RISE) feedback for a class of uncertain nonlinear systems, in which the RISE feedback gain is adapted online to ensure the robustness against disturbances without the prior bound knowledge of the additive disturbances. In addition, an adaptive compensation integrated with the proposed adaptive RISE feedback term is also constructed to further reduce design conservatism when the system also exists parametric uncertainties. Lyapunov analysis reveals the proposed controllers could guarantee the tracking errors are asymptotically converging to zero with continuous control efforts. To illustrate the high performance nature of the developed controllers, numerical simulations are provided. At the end, an application case of an actual electromechanical servo system driven by motor is also studied, with some specific design consideration, and comparative experimental results are obtained to verify the effectiveness of the proposed controllers., (Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.)