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To develop a new predictive control scheme based on neural networks for linear and non‐linear dynamical systems.

The approach relies on three different multilayer neural networks using input‐output information with delays. One NN is used to identify the process under control, the other is used to predict the future values of the control error and finally the third one is used to compute the magnitude of the control input to be applied to the plant.

This scheme has been tested by controlling discrete‐time SISO and MIMO processes already known in the control literature and the results have been compared with other control approaches with no predictive effects. Transient behavior of the new algorithm, as well as the steady state one, are observed and analyzed in each case studied. Also, online and offline neural network training are compared for the proposed scheme.

The theoretical proof of stability of the proposed scheme still remains to be studied. Conditions under which non‐linear plants together with the proposed controller present a stable behavior have to be derived.

The main advantage of the proposed method is that the predictive effect allows to suitable control complex non‐linear process, eliminating oscillations during the transient response. This will be useful for control engineers to control complex industrial plants.

This general approach is based on predicting the future control errors through a predictive neural network, taking advantage of the NN characteristics to approximate any kind of relationship. The advantage of this predictive scheme is that the knowledge of the future reference values is not needed, since the information used to train the predictive NN is based on present and past values of the control error. Since the plant parameters are unknown, the identification NN is used to back‐propagate the control error from the output of the plant to the output of the controller. The weights of the controller NN are adjusted so that the present and future values of the control error are minimized.

This paper aims to propose a roadmap to control the robot–subassembly (R–S) coordination errors in movable robotic drilling. Fastener hole drilling for multi-station aircraft assembly demands a robotic drilling system with expanded working volume and high positioning accuracy. However, coordination errors often exist between the robot and the subassembly to be drilled because of disturbances.

Mechanical pre-locating and vision-based robot base frame calibration are consecutively implemented to achieve in-process robot relocation after station transfer. Thus, coordination errors induced by robotic platform movements, inconsistent thermal effects, etc. are eliminated. The two-dimensional (2D) vision system is applied to measure the remainder of the R–S coordination errors, which is used to enhance the positioning accuracy of the robot. Accurate estimation of measured positioning errors is of great significance for evaluating the positioning accuracy. For well estimation of the positioning errors with small samples, a bootstrap approach is put forward.

A roadmap for R–S coordination error control using a 2D vision system, composed of in-process relocation, coordination error measurement and drilled position correction, is developed for the movable robotic drilling.

The proposed roadmap has been integrated into a drilling system for the assembly of flight control surfaces of a transport aircraft in Aviation Industry Corporation of China. The position accuracy of the drilled fastener holes is well ensured.

A complete roadmap for controlling coordination errors and improving positioning accuracy is proposed, which makes the high accuracy and efficiency available in movable robotic drilling for aircraft manufacturing.

The reliability of accounting internal control systems (AICS) is often viewed as a primary concern of auditors. Over the past three decades, several reliability models have been proposed for internal control. The main goal of these models is to provide an objective approach to evaluate the reliability of internal control systems. In addition, the models seek to assess the degree of audit reliance that can be placed on internal controls. This paper has a two‐fold objective: (1) to present an overview of the descriptive and prescriptive reliability models developed for the design and evaluation of internal control systems, and (2) to discuss the effects of various factors on the reliability assessment. Furthermore, two methods to estimate process reliabilities are presented and several numerical examples are provided to show the detailed calculations of the reliability and economic efficiency of accounting internal control systems.

The purpose of this paper is to focus on the accurate and steady control on trajectory tracking for wafer transfer robot, suppress the vibration and reduce the contour error.

The wafer transfer robot dynamic model is modeled. Through analyzing the characteristics of wafer transfer robot, cross‐coupled synchronized control is proposed based on the contour error model in task space to improve synchronization of the joints; the shaping for the joints by input shaper in task space is applied to suppress the vibration of the end effector during trajectory tracking. Then combining the cross‐coupled synchronized control with input shaping is proposed to improve accuracy and suppress the vibration.

The combination of cross‐coupled synchronized control and input shaping control method can improve the contour accuracy and reduce the vibration simultaneously during trajectory tracking. And the control method can be used to control the trajectory of wafer transfer robot.

The transfer station is in the center of the robot body. When the transfer station may deviate from the center of the robot body, the synchronizing performance of three axes on the same plane must be considered.

The proposed method can be used to solve the vibration and synchronizing performance problems on similar SCARA robots in semi‐conductor and liquid crystal display industry.

The proposed control method takes advantage of the cross‐coupled synchronized control and input shaping control method. This combination has improved contour accuracy and reduced vibration than applying other methods, and it has achieved better performance than using single one control method only.

This paper aims to solve the problems of the synchronization between branches and the uncertainties such as joint friction, load variation and external interference of a hybrid mechanism. The controller is used to improve the synchronization and robustness of the hybrid mechanism system and achieve both finite time convergence and chattering-free sliding mode.

First, the dynamic model of hybrid mechanism containing lumped uncertainties is formulated by the Lagrange method, and a composite error based on coupling synchronization error and the end-effector tracking error is set up in the task space. Then, by combining the finite time super twisting sliding mode control algorithm, a composite error-based finite time super twisting sliding mode synchronous control law is designed to make the end-effector tracking error and coupling synchronization error achieve better tracking performance and convergence performance. Finally, the Lyapunov stability of the control law and the finite-time convergence of the composite error are proved theoretically.

To verify the effectiveness of the proposed control method, simulations and experiments for the prototype system of the hybrid mechanism are conducted. The results show that the proposed control method can achieve better tracking performance and convergence performance.

This is a new innovation for a hybrid mechanism containing lumped uncertainties to improve the robustness, convergence performance, tracking performance and synchronization of the system.

This study aims to fathom the role of nursing governance as a mechanism to activate the chain effect from corporate social responsibility (CSR) through psychological contract to knowledge sharing, which in turn reduces clinical errors in hospitals in the Vietnam context. Clinical errors not merely result from human factors but also from mechanisms which influence human factors.

The clues for the research model were established through structural equation modeling-based analysis of cross-sectional data from 233 nurses of Vietnam-based hospitals.

Research findings unveiled the positive correlation between nursing governance and ethical CSR as well as the negative correlations between nursing governance and legal CSR or economic CSR. Ethical CSR was found to have positive effect on psychological contract, whereas legal or economic CSR was found to have negative effect on psychological contract. The chain effects from psychological contract through knowledge sharing to clinical error control were also attested in this inquiry.

Research results have contributed to literature in some ways, for example, expanding health-care quality and patient safety literature through the chain of antecedents (nursing governance, CSR, psychological contract and knowledge sharing) to clinical error control, underscoring the role of psychological contract in cultivating knowledge sharing and adding organizational outcomes such as knowledge sharing and clinical error control to the nursing governance literature.

This study aims to propose an adaptive fractional-order sliding mode controller to solve the problem of train speed tracking control and position interval control under disturbance environment in moving block system, so as to improve the tracking efficiency and collision avoidance performance.

The mathematical model of information interaction between trains is established based on algebraic graph theory, so that the train can obtain the state information of adjacent trains, and then realize the distributed cooperative control of each train. In the controller design, the sliding mode control and fractional calculus are combined to avoid the discontinuous switching phenomenon, so as to suppress the chattering of sliding mode control, and a parameter adaptive law is constructed to approximate the time-varying operating resistance coefficient.

The simulation results show that compared with proportional integral derivative (PID) control and ordinary sliding mode control, the control accuracy of the proposed algorithm in terms of speed is, respectively, improved by 25% and 75%. The error frequency and fluctuation range of the proposed algorithm are reduced in the position error control, the error value tends to 0, and the operation trend tends to be consistent. Therefore, the control method can improve the control accuracy of the system and prove that it has strong immunity.

The algorithm can reduce the influence of external interference in the actual operating environment, realize efficient and stable tracking of trains, and ensure the safety of train control.

The purpose of this paper is to examine information technology (IT) control deficiencies and their affect on financial reporting.

This study examines 278 companies reporting IT control deficiencies in the first three years of the SOX 404 requirements (2004‐2006). Using quantitative analysis, the study evaluates the impact of IT deficiencies on financial reporting and determines significant differences between companies that report IT deficiencies and companies that do not report IT deficiencies.

Four accounting errors: revenue recognition issues; receivables, investments and cash issues; inventory, vendor and cost of sales issues; and financial statement, footnote, US GAAP, and segment disclosures issues stand out as common financial reporting problems in companies reporting weak IT controls. This study also suggests that companies with IT control deficiencies report more internal control (IC) deficiencies, are smaller, pay higher audit fees, and are typically audited by smaller accounting firms.

This research is limited in scope since only SOX accelerated filers are included in the analysis. As of this study, smaller, non‐accelerated filers are not required to report IC control weaknesses under SOX.

As of this research, no analysis exists to support or refute the relationship of IT controls and accounting errors. This study re‐affirms the widespread impact that deficient IT controls can have on the overall IC structure of the business. Our study reveals some of the important issues associated with IT in the financial reporting process. The role of IT in financial reporting systems is destined to escalate. Studies, like ours, can help managers and auditors identify IT problems that affect financial reporting and take remedial steps to correct these weaknesses.

Automatic guided vehicles (AGVs) are widely used in industrial fields. But most control strategies merely take the lateral force into consideration. This will reduce the accuracy, stability and robustness and will pay additional costs. Therefore, this paper aims to design a control strategy that initially considers lateral force. Thereby, it will improve the accuracy, stability and robustness and reduce the overall cost of AGV.

To achieve the goal of comprehensively improving AGV operating performance, this paper presents a new scheme, combining the dual-wheeled chassis model (DCM) using proportional–integral–differential (PID) control and a supporting quick response (QR) code navigation technology. DCM is the core, which analyzes the deviation caused by lateral force. Then, DCM with PID control by the control law is combined to suppress the errors. Meanwhile, QR code navigation technology provides effective data support for the control strategy.

Most AGV experiments are carried out in a standard environment. However, this study prepares unfavorable scenarios and operating conditions for the experiments that generate detailed data to demonstrate this study’s strategy, which can make an accurate, stable and robust operation process of AGV under various adverse environmental and mechanical factors.

This study proposed DCM, fully considering lateral force and converting the force into velocity. Subsequently, PID controls the speed of two wheels to reduce the error. QR code provides an efficient and low – cost way to obtain information. The three are cleverly combined as a novel industrial AGV control strategy, which can comprehensively improve the operating performance while reducing overall costs.

Large optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.

The ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.

Experimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.

The ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.