Background: A few models have been proposed for the prediction of the risk of post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP), but many include factors that are not assessed routinely. Herein, we intend to develop and validate a predictive model for the occurrence of PEP. Methods: Data of patients who underwent endoscopic retrograde cholangiopancreatography (ERCP) from January 01, 2016 to May 16, 2019 (training set and internal test set) and from May 17, 2019 to December 25, 2019 (external test set) were retrospectively collected. The performance of the model was validated in the two validation cohorts. Results: A total of 342 patients were included for the external test set, and 47 (13.7%) developed PEP. The variables included in the scoring system were gastrectomy history, high direct bilirubin (DBIL), high albumin (ALB), villous type of papillary orifice, nodular type of papillary orifice, pancreatic guidewire passages, precut sphincterotomy, and high operator experience. A total score >5 indicated high risk. In the external test set, the area under the curve (AUC) was 0.718, the sensitivity was 0.723, and the specificity was 0.676. In the external test set, the probability of PEP was 6.1%, 17.0%, and 37.5% in patients with low (5) risk scores, respectively. Conclusions: This study established a scoring system for predicting the risk of PEP using routinely measured clinical variables. Its application in routine work warrants further investigation.

This article presents a sensorless control strategy for permanent magnet synchronous motors (PMSMs) with low chattering based on an adaptive sliding mode observer (SMO). The strategy incorporates an improved SMO with a novel adaptive fast power reaching law (NAFPRL-SMO) and a higher-order phase-locked loop based on an extended state observer (ESO-HOPLL). First, the NAFPRL-SMO is designed with adaptive gains to address the issue of slow reaching velocity that is commonly associated with traditional power reaching laws when far from the sliding mode surface. This is achieved by utilizing system state variables for enhancement. Then, to effectively suppress the chattering phenomenon, a sigmoid function is employed as a switching function. Finally, the ESO-HOPLL structure is utilised to reduce the effects of differential noise and improve the performance of the system, allowing for precise estimation of rotor position and speed information. Simulations and experiments are conducted to validate the steady-state and dynamic performance of the proposed sensorless control strategy, demonstrating its effectiveness and potential for enhancing performance in PMSM applications.

Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques.

So far, the inspection of power line corridors is mainly carried out by manual or helicopters. However, manual inspection is very labour-intensive and inefficient. On the other hand, the cost of the inspection using helicopters is very high. Also the safety of helicopters cannot be guaranteed. In recent years, some researchers have started to use unmanned helicopters to inspect power lines. Nevertheless, their focuses are the development of autonomous control systems. In this paper, we present an applied inspection robotic system based on an unmanned autonomous helicopter (UAH) for power line corridor inspection. Our interest is on the combination of the autonomous helicopter and the inspection system for the practical application in the field. This paper describes the whole architecture of the robotic system and presents some design issues. The experimental results are presented as well.

The significant role for a contemporary control algorithm in the position control of a permanent magnet linear motor (PMLM) system is highlighted by the rigorous standards for accuracy in many modern industrial and robotics applications. A robust predefined time convergent sliding mode controller (PreDSMC) is designed for the high precision position tracking of a permanent magnet linear motor (PMLM) system with external disturbance, and its convergence time is independent of the system’s initial value and model parameters. We verified theoretically that the performance function conditions are satisfied, the motor speed is steady and constrained, and the motor position tracking error converges to zero within the prescribed time. First, we designed a sliding mode (SM) surface with predetermined time convergence, which mathematically demonstrates that the tracking error converges to zero within the predefined time and shows that the position tracking accuracy is higher. Secondly, we developed a PreDSMC law that is independent of initial state and based on the predefined time convergence Lyapunov stability criterion. Finally, to prove the accuracy and higher precision of the proposed PreDSMC, comparative numerical simulations are performed for PMLM with compound disturbances. Simulation findings show that the suggested robust predefined control method considerably reduces the impacts of friction and external disturbances; consequently, it may increase the control performance when compared to the typical proportional integral derivative (PID) controller, the nonsingular fast terminal SMC, and the linear SMC.

Achieving accurate position tracking for robotics and industrial servo systems is an extremely challenging task, particularly when dealing with control saturation, parameter perturbation, and external disturbance. To address these challenges, a predefined time convergent sliding mode adaptive controller (PTCSMAC) has been proposed for a permanent magnet linear motor (PMLM). A novel sliding mode surface (SMS) with predefined time convergence PDTC has been constructed, which ensures that the error converges to zero within the prescribed time. The system not only meets the expected performance standards but also has a uniformly bounded motor speed. The trajectory tracking error in SMS is proven to converge to zero within the predefined time. This predefined time stability of the closed-loop system has been demonstrated by using the Lyapunov stability criterion with PDTC. The convergence time (CT) can be arbitrarily set, and the upper bound of it is not affected by the initial value and control parameters of the system. A new updated version of extreme learning machine (ELM) is introduced to approximate the uncertain part of the system based on PDTC. The ELM is also provided with the hyperbolic tangent function to estimate the saturation constraint. This is done by converting the function into a linear function concerning the unconstrained control input variable. Then, based on established stability, a novel sliding mode adaptive controller (PTCSMAC) with predefined time convergence is designed. The convergence time (CT) of the controller is unaffected by the initial conditions as well as the control parameters. The rigorous numerical simulations on the PMLM model with complex disturbances verify the strong robustness and high-precision tracking characteristic of the proposed control law.