In practical UAV with slung load systems, the suspension point often deviates from the UAV’s center of gravity due to structural limitations or operation requirement. This offset configuration disrupts the vehicle’s original structural symmetry and creates nonlinear coupling dynamics which leads to a complicated motion control problem. Conventionally, UAV systems are usually modeled and controlled from the perspective with its reference frame originating at the UAV’s center of gravity. Unlike existing approaches, this work introduces a new modeling and control framework by shifting the reference point from the UAV’s center of gravity to the connection point. The proposed dynamic model reveals that the load’s motion is directly driven by the acceleration of the UAV’s suspension point. A model-based control system is developed, comprising a decoupler, a mixer, and immersion-and-invariance adaptive control laws. An acceleration control law of the suspension point is proposed to actively control the velocity and swing angle of the slung load. Simultaneously, an inner-loop control torque and a mixer are developed based on the off-center frame to solve the coupling between the UAV and slung load attitude dynamics. The immersion-and-invariance adaptive laws are designed to estimate and compensate for external disturbances. Furthermore, a flexible gain function is introduced to improve the flexibility in shaping the control law, thereby achieving the potential for enhanced robustness and faster convergence of both the system states and parameter estimates compared with conventional control laws with linear gains. Finally, real-world flight experiments involving velocity tracking and disturbance rejection are conducted to demonstrate the advantages of the proposed control strategy.

Load transportation using unmanned aerial vehicles (UAVs) presents both intriguing possibilities and significant challenges in research and practical applications. This study aims to present a comprehensive literature review of recent progress in the development of multirotor UAVs transporting cable-suspended loads. A secondary objective is to assist researchers and engineers in the design and development of flight control systems for UAV-slung-load applications. To this end, the survey begins by providing a historical overview of flight control hardware platforms and load swing measurement systems used in UAV-slung-load systems. Subsequently, representative modeling approaches for UAV-slung-load systems are introduced. The survey then reviews a range of existing flight control strategies, highlighting their key characteristics and advantages. Finally, general challenges and potential future research directions for UAV-slung-load systems are discussed.

This article intends to demonstrate a real-time sensorless predictive control for a Line-Start Permanent Magnet Synchronous Machine (LSPMSM) with a speed observer based on the Model Reference Adaptive System (MRAS). The investigated control approach is evaluated under diverse scenarios in simulation and experimental studies to give an in-depth analysis. The LSPMSM’s dynamic and steady-state performance characteristics are thoroughly examined. A step speed case was investigated. Similarly, the low and high-speed responses were assessed. The LSPMSM’s capacity to handle sudden load increases in synchronous mode was also evaluated. When compared to sensor-equipped predictive control, the real-time implementation via the dSPACE dS1103 digital controller applied to a 1.5$ kW $LSPMSM drive system indicates that the proposed design strategy achieves sensorless control without degrading machine performance in terms of operational speed, dynamic torque and flux responses, steady-state torque and flux ripples, current total harmonic distortion, and converter switching frequency.Note to Practitioners—LSPMSM usually are connected directly to the 3-phase supply. But it is important to note that they have both the qualities of IM and PMSM. The motivation for this research is to investigate how we can utilize the LSPMSM for the variable speed drive operation. Since, PMSM requires sophisticated startup mechanism, but produces similar steady-state performance to LSPMSM, this research helps evaluate the performance of the predictive control for LSPMSM without the speed sensor.

Developing a trustworthy framework for intelligent fault diagnosis (IFD) of machines has two major challenges: confidently recognizing known faults and precisely detecting novel faults. However, current IFD frameworks are typically based on the closed-world assumption and tackle the two issues independently, making it hard to meet the expectations of reliable diagnosis in complicated working situations. In this paper, aProbabilistic framework forMechanical fault diagnosis (ProMo) is proposed to integrate misidentification and out-of-distribution (OOD) detection for high-speed train bogie. ProMo uses variational parameters to capture uncertainties at both the feature and prediction levels, in this way the misclassified and OOD samples with high predictive uncertainty are differentiated. To begin, a Bayesian deep neural network with a hierarchical classifier is built, offering diverse predictions and enabling ensemble uncertainty estimate. Then, a probabilistic null space analysis technique for post hoc OOD detection is presented, in which the magnitude of feature projections indicates the OOD-ness. Additionally, a novel metric assessing classification correctness and prediction reliability simultaneously is proposed. Extensive experiments revealed that ProMo outperforms state-of-the-art methods and achieves reliable fault diagnosis, even in the presence of covariate shift in monitoring data.

In a Wireless Sensor Networks (WSN) based fluid pipeline leak monitoring system, numerous sensors are deployed along the pipeline networks. A great amount of measurements are continuously transmitted from the sensor nodes to their corresponding sink nodes. The energy consumed on data transmission dominates the power depletion of a WSN system. To reduce the amount of data transmission and prolong the lifetime of WSN, in this paper, a Combined Dual-Prediction based Data Fusion (CDPDF) method is proposed. Transmissions are only triggered if the measurement is substantially different from the predicted value. Furthermore, unlike existing methods which establish the predictor by merely considering the measurements from a single sensor, the proposed CDPDF learns and updates the predictor by integrating measurements from multiple neighboring sensors, hence the spatial cross-correlation is taken into account and the prediction accuracy is significantly improved. In this paper, an Enhanced Leak Detection and Isolation (EnLDI) method is also proposed in which several important parameters, such as the friction factor and the pressure wave propagation speed, can be online updated, resulting in improvement of the leak localization accuracy. Experimental case studies are conducted. By employing the proposed CDPDF and EnLDI methods in pipeline networks monitoring, the accuracy of leak isolation is significantly increased with reduced data transmission demands. Note to Practitioners—This work delivers a hybrid scheme that combines machine learning based data fusion and transmission, with model-based leak detection and isolation. The work is motivated by the problem of high energy consumption on data transmission and poor leak diagnosis accuracy in WSN based pipeline networks monitoring system. To reduce the energy consumed during frequent transmissions among sensor nodes, in this paper, a machine learning based data fusion method is proposed which can eliminate most of the redundant transmissions. Among the investigated schemes, the Extreme Learning Machine (ELM) based predictor can not only achieve satisfactory prediction accuracy but also has low computational cost, hence it can be easily implemented in most of the embedded micro-controller systems in practice. At the base station of a WSN, in the leak diagnosis phase, traditional model-based methods employ the fixed model parameters which should be adjustable in different pressure and flow conditions etc. In this paper, an online model parameter estimation procedure is introduced and incorporated in the scheme designed to estimate the leak size and location, thus, the leak localization accuracy is significantly improved. Moreover, the algorithmic procedures, mathematical expressions, evaluation process and results are also provided for practical implementation.

This article presents a novel robust estimator-based dual-mode predictive fault-tolerant control (FTC) design scheme for linear parameter varying systems with state/input constraints. The overall FTC is composed of an unconstrained robust fixed FTC component and a constrained robust predictive FTC component. The former is determined by solving an offline integrated design of generalized unknown input observer and fault compensation control law. It is mainly used to compensate the influence of faults and stabilize the tracking error system. The robust predictive FTC is formulated based on the tightened invariant set constraint and quadratic programming. It is used to guarantee the recursive feasibility of the overall FTC under constraints, so its optimal value should be determined by solving the programming problem in real time. An algorithm is also provided to summarize all the involved steps of offline design and online implementation. Finally, the effectiveness of the proposed results is verified in a practical dc-dc buck converter.

In block‐mode transmission, single carrier frequency domain equalisation‐based ultra‐wide‐band technique is a promising physical‐layer candidate scheme. Here, the authors address the noise variance estimation problem for signal‐to‐noise ratio estimation in such systems. Our investigations focus on the estimation schemes both robust to the pilot sequence and the channel type. First, the authors, respectively, define the correlated noise samples and the uncorrelated ones, where the difference signals or the sum signals of two adjacent received pilot signals are included. Then, with the help of the Cramer–Rao lower bound (CRLB) theorem, the authors either use the correlated or the uncorrelated samples to estimate the noise variance based on the difference signals and the sum signals, respectively, regardless of the pilot sequences. The corresponding CRLBs are also given and analysed. Finally, under either the correlated or the uncorrelated noise samples, the authors propose to linearly combine the estimator of the difference signals with that of the sum signals where the weight coefficients are optimised. Since the proposed two combined estimators are not only robust to the pilot sequence but also automatically adapt the weights to the channel condition, they can significantly outperform the existing estimators.

This paper investigates the fault-tolerant shape control (FTSC) problem for stochastic distribution systems. For this problem, in addition to measurable input signals, it is assumed that the distribution function of the system output can be evaluated so that it is available. It is also assumed that the system is subject to actuator faults. In this case, the main control objective is for the output of the stochastic distribution system to track a given target distribution even in the presence of actuator faults. By estimating these actuator faults, an effective FTSC strategy is proposed, which consists of a normal control law and an adaptive compensation control law. The former can track the given output distribution with an optimized performance index in the fault-free case, while the latter can automatically reduce (or even eliminate) the adverse effects caused by the actuator faults. The proposed method can be applied to tracking control of output probability density functions. To demonstrate the effectiveness of the proposed scheme, simulation is performed on one numerical example with satisfactory results obtained. In addition, a practical example of soil particle gradation control in geotechnical applications is given in this paper, and the results show that the proposed fault-tolerant scheme is applicable to practical particle size distribution control.

In this paper, a real-time fault detection and isolation (FDI) scheme for dynamical systems is developed, by integrating the signal processing technique with neural network design. Wavelet analysis is applied to capture the fault-induced transients of the measured signals in real-time, and the decomposed signals are pre-processed to extract details about a fault. A Regional Self-Organizing feature Map (R-SOM) neural network is synthesized to classify the fault types. The R-SOM neural network adopts two regions adjustment in the learning algorithm, thus it has high precision in clustering and matching, especially when the noise, disturbance and other uncertainties exist in the systems. As a result, the proposed FDI scheme is robust and accurate. The design is implemented on a stirred tank system and satisfactory online testing results are obtained.