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Considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.

The authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.

This paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.

An augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

This paper aims to determine the optimal preview length for the preview state feedback controller and to design the static preview gains for the controller.

The design methodology is based on LMI formulations and Bisection Approach to determine the optimal preview length for the preview state feedback controller that achieves robust H∞ performance. The determination of the optimal preview length is such that the performance of the optimized controller is similar (within bounds) to the maximum length controller, in terms of transient and frequency response along with the H∞ performance.

The proposed algorithm is implemented for two benchmark design examples that verify its ability and applicability. The results show that the optimal preview length controller designed using the proposed method performs similar to the maximum length preview controller (within bounds) and better than reported in literature.

The paper discusses the novel algorithm formulation and its proof as no such method is available to determine the optimal preview length.

This paper considers the experimental results for tracking behaviors of human operators in preview control systems and, on the basis of the experimental results, proposes a model which can be viewed as the cascade connection of the convolution integral that processes the future input and the closed‐loop system modelled by the crossover model. The measured closed‐loop frequency characteristics and the analytical model demonstrate the following facts. 1) The human operator mainly improves the closed‐loop phase characteristics by using preview input information, while the closed‐loop amplitude characteristics are hardly improved. 2) The preview weighting function in processing the future input has a peak near 0.25 sec ahead and comes to very small values in the range beyond 0.5 sec ahead. 3) The future value beyond 0.5 sec ahead is of little value for improving the tracking characteristics when the controlled elements are K and Kc/s. But when the controlled element is Kc/s2, the required preview time is 0.75 sec.

Barge-type offshore floating wind turbine (OFWT) commonly exhibits an under-actuated phenomenon in an offshore environment, which leads to a potential vibration-damping hazard. This article aims to provide a new robust output feedback anti-vibrational control scheme for the novel translational oscillator with rotational actuator (TORA) based five-degrees of freedom (5-DOF) barge-type OFWT in the presence of unwanted disturbances and modeling uncertainties.

In this paper, an active control technique called TORA has been used to design a 5-DOF barge-type OFWT model, where the mathematical model of the proposed system is derived by using Euler–Lagrange's equations. The robust hierarchical backstepping integral nonsingular terminal sliding mode control (HBINTSMC) with an adaptive gain is used in conjunction with extended order high gain observer (EHGO) to achieve system stabilization in the presence of unwanted disturbances and modeling uncertainties. The numerical simulations based on MATLAB/SIMULINK have been performed to demonstrate the feasibility and effectiveness of the proposed model and control law.

The numerical simulation results affirm the accuracy and efficiency of the proposed control law for the TORA based OFWT system. The results demonstrate that the proposed control law is robust against unwanted disturbances and uncertainties. The unknown states are accurately estimated by EHGO which enables the controller to exhibit improved stabilization performance.

A new mathematical model of the 5-DOF barge-type OFWT system based on TORA is the major contribution of this research paper. Furthermore, it provides a new adaptive anti-vibration control scheme by incorporating the EHGO for the proposed model.

Accurate glide path tracking is vital to the automatic carrier landing task of unmanned aerial vehicle (UAV). The purpose of this paper is to develop a reliable flight controller that can simultaneously deal with external disturbance, structure fault and actuator fault.

The automatic carrier landing task is resolved into the glide path tracking problem and attitude tracking problem. The disturbance observer-based adaptive sliding mode control scheme is proposed for system stabilization, disturbance rejection and fault tolerance.

Both the Lyapunov method and exemplary simulations can prove that the disturbance estimation error and the attitude tracking error converge in finite time in the presence of external disturbances and various faults.

The presented algorithm is testified by a UAV automatic carrier landing simulation, which shows the potential of practical usage.

The barrier function is introduced to adaptively update both the sliding mode observer gain and sliding mode controller gain, so that the sliding mode surface could converge to a predefined region without overestimation. The proposed flight controller ensures a secure carrier landing task.

The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic background.

A dynamic target detection method based on the fusion of optical flow and neural network is proposed.

Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target.

It provides a powerful safeguard for target detection and targets the tracking application.

The proposed method represents the fusion of optical flow and neural network to detect the moving object, and it can be used in new-generation intelligent monitoring systems.

FOSTER Wheeler Energy and Air Products, in a joint venture, have been awarded a contract for the Nitrogen and Dry Air systems for the European Transonic Windtunnel (ETW), now under construction in Cologne. The total cost of the Nitrogen and Dry Air systems is anticipated to be around DM.55 million (£18.9m.).

The purpose of this paper is to design an innovative autonomous carrier landing system (ACLS) using novel robust adaptive preview control (RAPC) method, which can assure safe and successful autonomous carrier landing under the influence of airwake disturbance and irregular deck motion. To design a deck motion predictor based on an unscented Kalman filter (UKF), which predicts the touchdown point, very precisely.

An ACLS is comprising a UKF based deck motion predictor, a previewable glide path module and a control system. The previewable information is augmented with the system and then latitude and longitudinal controllers are designed based on the preview control scheme, in which the robust adaptive feedback and feedforward gain’s laws are obtained through Lyapunov stability theorem and linear matrix inequality approach, guarantying the closed-loop system’s asymptotic stability.

The autonomous carrier landing problem is solved by proposing robust ACLS, which is validated through numerical simulation in presence of sea disturbance and time-varying external disturbances.

The ACLS is designed considering the practical aspects of the application, presenting superior performance with extended robustness.

The novel RAPC, relative motion-based guidance system and deck motion compensation mechanism are developed and presented, never been implemented for autonomous carrier landing operations.

This study aims to examine the effects of the proposed mobile Web library application (MWLA) on the search experience and its impact on learners’ engagement, interaction and overall learning outcomes within an institutional repository. Furthermore, the study investigates learners’ acceptance of the MWLA system.

The study suggests implementing an MWLA with Algolia’s search service to improve the institutional repository and enhance learners’ access to reliable information. It involved an experiment with 85 undergraduate students divided into experimental and control groups (CGs), where the experimental group (EG) used MWLA for search tasks, and the CG used the traditional library website. The study evaluated the acceptance and learning behaviours of the EG towards MWLA, considering factors such as usefulness, ease of use, mobility, accessibility, satisfaction and intention to use.

The findings of this study provide empirical evidence that the EG, which used the MWLA, demonstrated superior performance compared to the CG across all institutional repository collections, resulting in improved learning outcomes. Participants were highly satisfied with MWLA and found it user-friendly and beneficial for improving search skills. MWLA’s portability and accessibility motivated active learner engagement.

The powerful search bar of MWLA significantly enhanced learners’ search efficiency, resulting in more effective retrieval of relevant materials. Moreover, learners who actively engaged with previews and full-text content, using appropriate keywords and syntax, achieved higher scores and were more likely to access previews, abstracts and full texts of documents using the sorting-by-year or by-advisor feature.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.