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Efficient and accurate real-time estimation of power system synchronization is quite important for its safety control and operation. However, signal sensing failure, electromagnetic interference, system delay, etc., will cause the sensor gain degradation. To furnish a dependable method for dynamic estimation in power grid synchronization amid sensor gain degradation, this research presents a robust estimation system capable of monitoring and tracking the frequency, voltage phase angles, and magnitudes. Firstly, the random degradation of measurement data is characterized by a discrete distribution within the range [0,1]. Secondly, the state space model of sensor gain degradation is established. Subsequently, a novel modified fault-tolerant extended Kalman filter (MFTEKF) is developed under the recursive estimator framework. Finally, extensive experimental results definitively demonstrate that the proposed MFTEKF can accurately monitor the dynamic characteristics of the power grid.

Under unknown operating conditions, the domain generalization approach based on domain metrics is commonly used for rolling bearing fault diagnostics. Nevertheless, in the event of equipment failure under unknown operating conditions, focusing solely on the transferable characteristics across domains may result in the unintentional neglect of domain-specific features. To address the problems mentioned, the present study introduces a feature decomposition learning method that simultaneously extracts inter-domain transferable and domain-specific features. This method aims to obtain richer feature information by constructing different feature extractors. For the extraction of transferable features, a joint metric method based on central moment differences is devised. A difference maximization method is employed to extract domain-specific features. The experimental findings demonstrate that the proposed technique exhibits greater defect detection capacity across two datasets.

This paper proposes a set of Nash equilibrium tracking control strategies based on mixed zero-sum (MZS) game for the continuous-time nonlinear multi-player systems with deferred asymmetric time-varying (DATV) full-state constraints and unknown initial state. Firstly, an improved shift transformation is used to modify the original constrained system with an unknown initial state into a barrier transformable constrained system. Then, based on the barrier transformable constrained system and predefined reference trajectory, an unconstrained augmented system is formed through the application of the barrier function (BF) transformation. Furthermore, the MZS game Nash equilibrium tracking control strategies are derived by establishing the tracking error related quadratic cost functions and corresponding HJ functions for different players. On this basis, a critic-only structure is established to approximate the control strategy of every player online. By employing Lyapunov theory, it is proven that the neural network weights and tracking error are uniformly ultimately bounded (UUB) within DATV full-state constraints. Simulation experiments of a three-player nonlinear system demonstrate that our algorithm successfully handles deferred state constraints and unknown initial conditions, ensuring that the system states follow the desired reference trajectories. Simulation results further validate the uniform ultimate boundedness of neural network weights and tracking errors.

Control of the family of systems that can be represented in the Euler Lagrange (EL) form is both challenging from a theoretical perspective and applicable to a broad spectrum of real systems. For this type of control problem, given that any parameter estimation error and disturbances are not directly addressed, the system performance deteriorates, and stability cannot be deduced in advance. Considering these issues, this work presents the design and the corresponding analysis of a saturation function based, model-free, continuous robust controller for mechanical systems represented in the EL form. In order to avoid chattering in controller input, which is a common problem in most robust and high-gain control designs, the proposed method makes use of continuously differentiable terms. The stability of the closed-loop system is ensured via rigorous Lyapunov-based arguments. To ease the tuning of the controller gain, an adaptive gain-tuning algorithm is proposed to be applied as an add-on. The effectiveness of the controller is demonstrated by a simulation study on a twin rotor multi-input-multi-output system (TRMS) model Furthermore, the feasibility of the proposed method is then tested on an in-house built, inherently unstable, and therefore extremely sensitive mobile robotic platform. In the experimental study, satisfactory performances are attained for both the controller and the gain-tuning algorithm where less than 0.5° error is obtained in roll and pitch angles and less than 1° error is achieved in the yaw direction.

The article presents a framework for fault detection and classification to monitor the condition of motor bearings under multiple operating conditions. The condition monitoring of motor bearings is crucial for failure prevention, as bearings are prone to failure in challenging working environments. Intelligent fault diagnosis methods driven by deep learning and model-based approaches have been widely adopted to address these concerns. However, accurately diagnosing bearing faults across varying conditions and identifying multiple fault types remains challenging. The article proposes a multitask fault detection and classification approach for health monitoring using the HUST motor bearings dataset. The evaluation using HUST motor bearing datasets demonstrates robust performance across diverse operating conditions and in the presence of multiple faults. The HUST dataset is valuable for bearing fault diagnosis due to its diverse operating conditions and inclusion of multiple fault types, offering a realistic representation of fault scenarios derived from real bearing experiments. This methodology enhances the safety and reliability of mechanical equipment, with adaptability to various rotating scenarios.

Melting defects in Laser Powder Bed Fusion (LPBF) processes, such as lack of fusion (LOF) or over-melting (OM), can cause significant deterioration in mechanical properties and surface roughness of printed parts, potentially leading to process failure. Previous attempts to utilize local melt pool-related information for LPBF process control have faced limitations due to the high requirements on sensors and data processing, as well as the lack of representativeness of local melt pool information. This study focuses on the surface quality of the parts and proposes an image based LPBF control to mitigate melting defect. A quality identification module utilizing convolutional neural networks (CNN) to perform layer-by-layer evaluation of the melting quality of the part. The CNN achieved an accuracy of up to 98.2% in identifying melting quality. Furthermore, based on the surface melting quality extracted by CNN, a fuzzy control strategy (FIC) integrated with a historical state consistency check mechanism (HSCCM) is introduced to determine the optimal control actions for subsequent layers. Experimental results affirm that the FIC integrated with HSCCM effectively alleviates surface melting defects, thereby enhancing surface roughness and manufacturing quality of components. This research offers a novel approach for online quality monitoring and improvement in LPBF processes.

This paper investigates the unified tracking control problem for a class of high-order nonlinear systems with 7 kinds of irregular state constraints and input saturation based on the dynamic event-triggered mechanism. The irregular state constraints exist in practical systems, including time-varying constraints, alternation between positive and negative bounds, adding/removing constraints during system operation, and the state of the system being constrained only by the upper/lower boundaries. Auxiliary constraint boundaries are introduced to deal with these irregular state constraints. This unified method allows different auxiliary constrained boundaries in response to specific circumstances, without affecting the controller's structure. Nonlinear transformed functions (NTFs) are used to eliminate the feasibility condition of barrier Lyapunov functions (BLFs) methods. Subsequently, based on the dynamic event-triggered mechanism and adding a power integrator technique, an event-triggered controller is designed to effectively reduce communication burden and energy consumption between the controller and the actuator. Finally, a simulation example and a practical example are given to verify the effectiveness of the proposed unified control method.

A suitable controller for the Continuous Stirred Tank Reactor (CSTR) plays a crucial role in the reactor's performance. This paper presents a novel observer-based sliding mode controller designed to control the CSTR with high accuracy and a fast response. The stability of the proposed method is analyzed using Lyapunov theory. Five other methods, with fixed-time, finite-time (terminal), and asymptotic stability, are used for comparison. Two scenarios are defined: one with bounded control signals and one with unbounded control signals. A real-time simulator is employed to evaluate the control methods. Four different indices are used to compare the methods. In all indices, the proposed control method demonstrates the best performance, with fast response, accurate tracking and estimation, chatter-free operation, and robust features.

A controller based on the paradigm of virtual structure, considering two quadrotors transporting a cable-suspended load, is proposed, aiming at controlling the load movement through controlling the triangle quadrotor-quadrotor-load. The null space-based behavioral control technique is used, prioritizing preserving the formation shape and orientation. The objectives are to keep a safe distance between the vehicles, to prevent collision, and to reduce the load swing. Results of experiments run using the proposed controller are also shown, which validate the proposed approach. From the theoretical analysis and experimental results, the conclusion is that combining the virtual structure control paradigm with the null space-based behavioral control provides an effective solution for the problem of transporting a cable-suspended load using two aircraft, which is the main contribution of the paper.

As the last subprocess of copper flotation processes, the column cleaning process plays a decisive role in the tailing copper grade (TCG) and concentrate copper grade (CCG), which are the important factors in determining comprehensive economic indicators. Therefore, the problem of setpoints tracking control of TCG and CCG is particularly important. However, the unknown parameters in the column cleaning process bring great challenges to the problem of setpoints tracking. To overcome this problem, a state space model is constructed based on the two phase model of flotation. Due to the complexity of the column cleaning process, the state-space model matrices cannot be detected or calculated directly. Therefore, a deep autoencoder-based subspace identification method (SIM-DAE) is proposed to identify the state-space model matrices. Next, a Lyapunov-Krasovskii function is proposed to verify the stability and anti-interference performance of the identified system. Meanwhile, the state feedback controller is designed that the TCG and CCG can track with the setpoints. Finally, the effectiveness and feasibility of the proposed methods are verified by the data experiments and an industrial field platform.