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Abstract This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

Abstract In order to achieve more efficient and optimised resource scheduling, this research carried out a multi‐objective task resource allocation method for low‐voltage station edge computing based on hierarchical Bayesian adaptive sparsity. Based on hierarchical Bayesian adaptive sparsity, the multi‐objective task resource allocation technical framework for edge computing in low‐voltage stations is established, which is composed of end pipe edge cloud; After collecting real‐time operation data of power distribution equipment, substation terminals, transmission terminals, etc. in the architecture end, it is transmitted to the data middle platform and service middle platform of the Internet of Things management platform in the cloud through the edge Internet of Things agent; Set and solve the constraint conditions, and build a multi type flexible load hierarchical optimal allocation model; The abnormal area topology identification sub module of multi‐objective task resource of low‐voltage station area edge computing is used to identify the abnormal area topology of the current low‐voltage station area; Taking it as input, the multi‐objective task resources of edge computing are allocated, and the multi‐objective task resources allocation method of edge computing in low pressure platform area is realized under the differential evolution algorithm. The experimental results show that the proposed method has good convergence effect, strong distribution ability, relatively gentle increase in energy consumption, and the calculated results are basically consistent with the actual values, with good effectiveness.

This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system's adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT. [ABSTRACT FROM AUTHOR]

Abstract Payload delivery is one of the many Unmanned Aerial Vehicle (UAV) applications that save time, energy, and human resources. The present simulation study compares rotary‐wing quadrotors (RW‐Quadrotors) and tail‐sitter biplane quadrotors (Bi‐Quadrotors) under payload delivery conditions that help to choose the best UAV for the particular mission. A comparative study is performed based on (i) time taken to accomplish the given mission, (ii) trajectory tracking performance, (iii) motor speed throughout the entire flight envelope, and (iv) remains SoC (State of Charge) after the mission. Furthermore, a Backstepping Controller (BSC) and an Adaptive Backstepping Controller (ABSC) are developed for both UAVs to handle mass changes during flight while Genetic Algorithm (GA) is used for the gain optimization. MATLAB Simulink‐based numerical simulation reveals that the Bi‐Quadrotor has a lower average rotor speed than the RW‐Quadrotor. However, the Bi‐Quadrotor's SoC after the mission is higher than the RW‐Quadrotor, and it took less time to accomplish the mission. The ABSC designed for RW‐Quadrotor and Bi‐Quadrotor effectively handles mass change during the mission. However, the BSC controller increases energy consumption for the Bi‐Quadrotor in the presence of wind gusts.

Abstract Flexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions.

Abstract 3D meshes simplification plays an important role in many industrial domains. The two goals of Delaunay mesh simplification are maintaining high geometric fidelity and reducing mesh complexity. However, they are conflicting and cannot solved by gradient. Such limitation prevents existing Delaunay mesh simplification to obtain a small enough number of vertices and promising fidelity at the same time. To address these issues, this paper proposes an evolutionary multi‐objective approach for Delaunay mesh simplification. Firstly, the authors replace the previous fixed error‐bound threshold by the designed adaptive segment‐specific thresholds. Secondly, a constrained simplification is performed through a series of edge collapses that satisfy both Delaunay and error constraints. Next, the non‐dominated sorting genetic algorithm II (NSGA‐II) is employed to solve the multi‐objective problem to search for the optimal trade‐off threshold sequences. Finally, a fine‐tuning method is designed to further enhance the geometric fidelity of the simplified mesh. Experimental results demonstrate that the authors’ method consistently achieves a satisfactory balance between the approximation error and number of vertices, outperforming existing state‐of‐the‐art methods.

Payload delivery is one of the many Unmanned Aerial Vehicle (UAV) applications that save time, energy, and human resources. The present simulation study compares rotary‐wing quadrotors (RW‐Quadrotors) and tail‐sitter biplane quadrotors (Bi‐Quadrotors) under payload delivery conditions that help to choose the best UAV for the particular mission. A comparative study is performed based on (i) time taken to accomplish the given mission, (ii) trajectory tracking performance, (iii) motor speed throughout the entire flight envelope, and (iv) remains SoC (State of Charge) after the mission. Furthermore, a Backstepping Controller (BSC) and an Adaptive Backstepping Controller (ABSC) are developed for both UAVs to handle mass changes during flight while Genetic Algorithm (GA) is used for the gain optimization. MATLAB Simulink‐based numerical simulation reveals that the Bi‐Quadrotor has a lower average rotor speed than the RW‐Quadrotor. However, the Bi‐Quadrotor's SoC after the mission is higher than the RW‐Quadrotor, and it took less time to accomplish the mission. The ABSC designed for RW‐Quadrotor and Bi‐Quadrotor effectively handles mass change during the mission. However, the BSC controller increases energy consumption for the Bi‐Quadrotor in the presence of wind gusts. [ABSTRACT FROM AUTHOR]

Flexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions. [ABSTRACT FROM AUTHOR]

Abstract A multi‐agent iterative optimisation method based on deep reinforcement learning is proposed for the balancing and sequencing problem in mixed model assembly lines. Based on the Markov decision process model for balancing and sequencing, a balancing agent using a deep deterministic policy gradient algorithm, a sequencing agent using an Actor–Critic algorithm, as well as an iterative interaction mechanism between these agents' output solutions are designed for realising the global optimisation of mixed model assembly lines. The exchange of solution information including assembly time and station workload in the iterative interaction realises the coordination of the worker assignment policy at the balancing stage and the production arrangement policy at the sequencing stage for the minimisation of work overload and idle time at stations. Through the comparative experiments with heuristic rules, genetic algorithms, and the original deep reinforcement learning algorithm, the effectiveness of the proposed method is demonstrated and discussed for small‐scale instances as well as large‐scale ones.

Abstract The proper sizing of the electronic target to implement complex controllers is barely tackled at engineering schools. An incorrect selection leads, at best, to inefficient use of resources and unnecessarily high costs. In this work, the criteria to implement the electronic Lyapunov‐based controller for non‐linear trajectory tracking of a P3‐DX robot is focused on. After comparing pros and cons of the four alternatives, test results are shown with two of them: NUC8i5INH microcomputer and STM32F767 single chip board.

Abstract As communication networks are implemented for information exchange between the master and slave sides of bilateral teleoperation systems, they are exposed to cyber‐attack threats. This paper aims to analyse the performance of bilateral teleoperation systems in the presence of random denial‐of‐service (DoS) attacks and constant transmission delays and propose a mode‐dependent switching controller to mitigate the influence of DoS attacks. The characteristics of DoS attacks and networks are thoroughly incorporated in the design; also considered is the case of both communication directions behaving independently. Specifically, the model of a teleoperation system under a DoS attack is integrated as a stochastic jump system. A mode‐dependent control approach is proposed for a teleoperation system to mitigate the influence of random DoS attacks. In case studies, vulnerability analysis and time‐domain simulation results show that teleoperation system performance can be degraded under continuous random DoS attacks. When the proposed mode‐based switching controllers are installed, the trajectory tracking performance and authenticity of interaction force feedback are significantly improved during the attacking period.

The proper sizing of the electronic target to implement complex controllers is barely tackled at engineering schools. An incorrect selection leads, at best, to inefficient use of resources and unnecessarily high costs. In this work, the criteria to implement the electronic Lyapunov‐based controller for non‐linear trajectory tracking of a P3‐DX robot is focused on. After comparing pros and cons of the four alternatives, test results are shown with two of them: NUC8i5INH microcomputer and STM32F767 single chip board.In this work, the criteria to implement the electronic Lyapunov‐based controller for non‐linear trajectory tracking of a P3‐DX robot is focused on. Incorrect sizing of the electronic target leads, at best, to inefficient use of resources and unnecessarily high costs. [ABSTRACT FROM AUTHOR]

Abstract This letter presents a new intelligent control scheme for the accurate trajectory tracking of flexible link manipulators. The proposed approach is mainly based on a sliding mode controller for underactuated systems with an embedded artificial neural network to deal with modelling inaccuracies. The adopted neural network only needs a single input and one hidden layer, which drastically reduces the computational complexity of the control law and allows its implementation in low‐power microcontrollers. Online learning, rather than supervised offline training, is chosen to allow the weights of the neural network to be adjusted in real time during the tracking. Therefore, the resulting controller is able to cope with the underactuating issues and to adapt itself by learning from experience, which grants the capacity to deal with plant dynamics properly. The boundedness and convergence properties of the tracking error are proved by evoking Barbalat's lemma in a Lyapunov‐like stability analysis. Experimental results obtained with a small single‐link flexible manipulator show the efficacy of the proposed control scheme, even in the presence of a high level of uncertainty and noisy signals.

Abstract This article presents an approach to implement a Nonlinear Model Predictive Controller (NMPC) in real‐time with a non‐standard cost index. The proposed technique's applications are presented to maximize the energy produced by a Wave Energy Converter (WEC) when the cost index is a non‐quadratic piecewise discontinuous functional of some design variables. The presented framework is based on pseudo‐quadratisation and weight scheduling, which is implemented using the ACADO toolkit for MATLAB/Simulink. The proposed strategy features code generation and deployment on the real‐time target machines for industrial applications. The simulations and experiments confirm the success of the proposed approach in achieving the feasible operation of the NMPC and an optimal power capture by the wave energy converters.

Abstract For detection of dense small‐target organisms with indistinct features in complex background, the efficiency and accuracy of traditional target detection methods are low. Multi‐sensor fusion oriented human‐robot interaction (HRI) system has facilitated biologists to process and analyse data. For this, several deep learning models based on convolutional neural network (CNN) are improved and compared to study the species and density of dense organisms in deep‐sea hydrothermal vent, which are fused it with related environmental information given by position sensors and conductivity‐temperature‐depth (CTD) sensors, so as to perfect multi‐sensor fusion oriented HRI system. Firstly, the authors combined different meta‐architectures and different feature extractors, and obtained five object identification algorithms based on CNN. Then, they compared computational cost of feature extractors and weighed the pros and cons of each algorithm from mean detection speed, correlation coefficient and mean class‐specific confidence score to confirm that Faster Region‐based CNN (R‐CNN)_InceptionNet is the best algorithm applicable to hydrothermal vent biological dataset. Finally, they calculated the cognitive accuracy of rimicaris exoculata in dense and sparse areas, which were 88.3% and 95.9% respectively, to analyse the performance of the Faster R‐CNN_InceptionNet. Results show that the proposed method can be used in the multi‐sensor fusion oriented HRI system for the statistics of dense organisms in complex environments.

Abstract Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon‐YOLO (L‐YOLO) is proposed to improve the accuracy and real‐time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L‐YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L‐YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits.

Abstract Recently, the global concern for protection coordination is growing with the impact of distributed generators connected to medium voltage distribution system. Effective protection strategies need to be developed in order to avoid undesirable tripping when distributed generators based on Voltage Source Inverters are connected to the medium voltage grid. According to Spanish grid code requirements, the inverter controller response in this paper is assessed under grid faults integrating low voltage ride through capabilities. This paper presents a novel use of a communication‐based directional relay system with artificial neural network, an appropriate option for smart grids protection. A protection strategy is proposed using two algorithms. The first algorithm is based on gathering data of all the protective devices in the grid and send it to a centralized controller. The second algorithm is based on a zone controller using the communication between the peer protection devices in the same line. One of the main advantages of the zone controller is that no need to modify the protection devices setting in case of temporary grid reconfigurations. The behaviour of the protection algorithms is validated through both simulations in MATLAB‐Simulink and experimental results.

Abstract Manipulation skill learning and generalisation have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realise the manipulation skill learning and generalisation. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalisation to deal with complex manipulation tasks. To this end, the key technologies, for example, manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics.

Abstract As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems. Among these transitions—in a bid to reduce flight risk—is the active research domain of autonomous navigation for intelligent UAVs. The autonomous trail‐following flight system that this letter introduces can safely consolidate flight control and mission control within the latest commercial hardware platform. The resource usage and degradation of pass‐through delay in vision‐based convolutional neural network workloads show that virtualisation overhead is not significantly negative, and the overall performance of the introduced system is acceptable. Real‐time cooperation is also verified as achievable—in that the workloads incur minimal communication delay—between the controls. Finally, the actual field test analysis demonstrates the applicability of our autonomous UAV system, whereby our system controls the UAV to follow the centre of a set trail.

Abstract This paper proposes a distributed control scheme for multi‐agent systems to achieve resilient flocking behaviour via event‐based communication. The control scheme can provide the required connectivity conditions to form a flock in the presence of cyberattacks in the network. This method is presented in a fully distributed manner to avoid the use of global data. The developed event‐triggered update rules can mitigate the influence of the non‐cooperative agents with the weighted mean subsequence‐reduced algorithm and reduce unnecessary communication among them. It is proved that under the proposed method, convergence is guaranteed and there is no Zeno behaviour exhibited inherently. To relax the requirement of continuous monitoring of self‐state, a self‐triggered mechanism is proposed further. Simulation results are given to illustrate the theoretical analysis and show the advantages of the event‐ and self‐triggered controllers.

Abstract Over the last years, the development and control of Autonomous Underwater Vehicles with attached robotic manipulators, also called Underwater Vehicle Manipulator System (UVMS), has gained significant research attention. In such applications, feedback controllers which guarantee that the end‐effector of the UVMS is fulfilling desired complex tasks should be designed in a way that state and input constraints are taken into consideration. Furthermore, due to their complicated structure, unmodeled dynamics as well as external disturbances may arise. Complex tasks can be conveniently given in the so‐called Linear Temporal Logic (LTL). Motivated by this, the authors develop a combined abstraction and control synthesis framework in which, given the uncertain kinematics/dynamics of the UVMS, a workspace divided into Regions of Interest and a desired LTL task, a sequence of feedback control laws that probably guarantee the LTL formula is provided. The proposed controller falls within the tube‐based non‐linear model predictive control methodology and can handle the rich expressivity of LTL in both safety and reachability specifications. Numerical simulations verify the validity of the proposed framework.

Abstract Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F‐measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state‐of‐art techniques.

Abstract Deep reinforcement learning‐based methods employ an ample amount of computational power that affects the learning process. This paper proposes a novel approach to speed up the training process and improve the performance of autonomous navigation for a tracked robot. The proposed model named “layer normalization dueling double deep Q‐network” has been trained in a virtual environment and then implemented it to a tracked robot for testing in a real‐world scenario. Depth images have been used instead of RGB images to preserve the temporal information. Features are extracted using convolutional neural networks, and actions are derived using the dueling double deep Q‐network. The input data has been normalized before each convolutional layer, which reduces the covariate shift by 69%. This end‐to‐end network architecture of the proposed model provides stability to the network, relieves the burden of computational cost, and converges in much less number of episodes. Compared with three Q‐variant models, the proposed model demonstrates outstanding performance in terms of episodic reward and convergence rate. The proposed model took 12.8% fewer episodes for training compared to other models.

Digital active disturbance rejection controllers (DADRC) have a wide scope as they have inherent advantages such as: easy hardware implementation, the entire system's mathematical model it is not necessary, is a linear controller dealing with uncertainty and is intended to ensure that energy savings are achieved. This document provides a procedure for designing a DADRC for a dc motor drive from the perspective of a Generalized Proportional Integral observer and differential flatness. The angular speed of a dc motor drive is the main purpose of the controller. The proposed DADRC was implemented using floating point operations on a field programmable gate array Artix-7™ using a Nexys 4 board. The system is subject to current demands, parameter variations, dc power supply changes and external load torque variations. The coupling of buck converter and dc motor resulting in external and internal disturbances that are actively counteracts the linear controller, as shown in the results.

This study presents the implementation of a new unsupervised machine learning based system called a buck converter controller using unsupervised machine learning (ABCML) to control the operation of a type of switching voltage regulators, commonly called buck converters. The system uses the voltage and current state variables of buck converters to evaluate four switching-based clustering algorithms, namely a Gaussian mixture model, a self-organising mapping (SOM), a k-means clustering and a hierarchical clustering algorithm. Step response results of the controller implementation with the buck converter show that the SOM controller improves the performance of the voltage regulator system by 99.77% in terms of voltage overshoot and by 63.24% in terms of fall time, whereas the hierarchical clustering algorithm improves the settling time of the output voltage by 3.91%. This finding of optimal machine learning implementation approaches and their comparison could be used to improve the stability and the performance of switching voltage regulation systems which are widely used in electronic systems of today.

This study proposes a control technique for single‐input dual‐output three‐level dc–dc converter (SIDO‐TLC) using feedback (FB) and feedforward (FF) principles. Through the proposed control strategy, SIDO‐TLC can smoothly function in both buck and boost operating conditions with a fast dynamic. Major duties of the designed FB control loops are to noticeably decrease the recovery time of transient responses under the load variations and accurately balance the boost capacitors voltages. To effectively decouple the introduced control inputs, an external FF controller is assigned that can also enhance the buck and boost voltage regulations. Furthermore, a step‐by‐step assessment is contemplated based on the proposed control loops to make an offline adjustment for the FB and FF coefficients. The proposed controller implemented on SIDO‐TLC is highly suitable for portable applications, where efficiency, cost, and speed are of important factors. The simulation results and the laboratory test setup of SIDO‐TLC using DSP TMS320F28335 are presented to prove the validity of the presented theoretical subjects. [ABSTRACT FROM AUTHOR]

In this study, a hybrid model‐based and data‐driven method is proposed for the current sensor fault diagnosis used in single‐phase pulse width modulation (PWM) rectifier. According to the principle of model‐based methods, the proposed diagnostic method is based on signal prediction and residual generation. Differently, instead of a mathematical model, the signal prediction model is developed based on a data‐driven method. Non‐linear autoregressive exogenous learning model, randomised learning technique, and extreme learning machine are utilised to generate the data‐driven prediction model. Once the fault is detected, fault‐tolerant control is activated by substituting the predicted signal for the information of faulty sensors. The offline test shows that the proposed method is able to predict the sensor signal accurately with the root mean square error of 4.276 × 10−5. In addition, hardware‐in‐the‐loop tests are conducted to verify the feasibility and reliability of the proposed method in the real‐time application. [ABSTRACT FROM AUTHOR]

With the real-time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs' output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs' DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF. [ABSTRACT FROM AUTHOR]

Shared control is a promising approach that can facilitate the mutual understanding and cooperative control between human and machine. In this study, a novel reference‐free framework for shared control of automated vehicles is proposed with the aim of mitigating conflicts between the human driver and the automatic system during their interactions. Position constraint and time to collision are deployed to prevent collision and guarantee stability by limiting the yaw rate and sideslip angle of the vehicle. The design of the shared controller is formulated as a model predictive control problem. It determines vehicle state based on the current driver command and implements control actions only if the vehicle state induced by the human driver violates the pre‐defined constraints. The system models, safety constraints and the proposed shared controller are then integrated. The algorithm is validated through computer simulation under two different driving scenarios. The simulation results show that the proposed reference‐free approach can offer the human driver more degrees of freedom during shared control, effectively mitigating human–machine conflicts, compared to the previous work. In addition, the algorithm ensures the safety and stability of the system under risky driving conditions, validating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This study presents the implementation of a new unsupervised machine learning based system called a buck converter controller using unsupervised machine learning (ABCML) to control the operation of a type of switching voltage regulators, commonly called buck converters. The system uses the voltage and current state variables of buck converters to evaluate four switching-based clustering algorithms, namely a Gaussian mixture model, a self-organising mapping (SOM), a k-means clustering and a hierarchical clustering algorithm. Step response results of the controller implementation with the buck converter show that the SOM controller improves the performance of the voltage regulator system by 99.77% in terms of voltage overshoot and by 63.24% in terms of fall time, whereas the hierarchical clustering algorithm improves the settling time of the output voltage by 3.91%. This finding of optimal machine learning implementation approaches and their comparison could be used to improve the stability and the performance of switching voltage regulation systems which are widely used in electronic systems of today. [ABSTRACT FROM AUTHOR]

Microgrids are often accomplished with power‐dense and energy‐dense hybrid storage systems for the assured improvement in the stability and the awake time. The conventional droop based steady‐state power bifurcation strategies for the energy‐dense storage elements can efficiently improve the awake time. However, the improvement in the stability and the robustness demands accurate transient demand allocation. The constant cut‐off frequency‐based physical power decomposition filter cannot ensure an accurate transient demand allocation, particularly, while the demand is fluctuating and uncertain. Therefore, the study proposes the concept of pseudo‐power decomposition filter. The concept of the pseudo‐decomposition filter has been executed for the microgrid through the novel selected power component droop. The real‐time performance of the proposed concept has been evaluated in the BTS‐7960 packaged‐Metal Oxide Semiconductor Field Effect Transistor (MOSFET) ‐based hardware platform, regularised through the ARM Cortex A‐72 based digital signal processor. In respect to the conventional linear droop, the improvement through the concept in the power decomposition and the allocation of transient demands is significant. [ABSTRACT FROM AUTHOR]

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members’ background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included.

An integrated Matlab model comprising of an electro-mechanical actuator (EMA) model, load model and state model has been constructed to investigate the cargo door actuator motion control, operation and working states, of which, a Simulink® servomotor system model is adopted to mimic the EMA actuator, a SimMechanics® model is used to compute the actuator's load, a Stateflow® state transition model is employed to simulate the working process. The results indicated that the motion is well controlled, generated load force is valid, working states and their transition logics are reasonable. The simulation verified the effectiveness and easiness of the integrated modelling method.

One of the basic requirements of today's sophisticated world is the availability of electrical energy, and neglect of this matter may have irreparable damages such as an extensive blackout. The problems which were introduced about the traditional power grid, and also, the growing advances in smart technologies make the traditional power grid go towards smart power grid. Although widespread utilisation of telecommunication networks in smart power grid enhances the efficiency of the system, it will create a critical platform for cyber attacks and penetration into the system. Automatic generation control (AGC) is a fundamental control system in the power grid, and it is responsible for controlling the frequency of the grid. An attack on the data transmitted through the telecommunications link from the sensors to the AGC will cause frequency deviation, resulting in disconnection of the load, generators and ultimately global blackout. In this study, by using a Kalman filter and a proposed detector, a solution has been presented to detect the attack before it can affect the system. Contrary to existing methods, this method is able to detect attacks that are stealthy from the area control error signal and χ^2-detector. Simulations confirm the effectiveness of this method.

This study investigates a small film DC-link capacitor inverter for a permanent magnet synchronous motor drive system. Two control methods, including a d–q-axis current control and a positive torque region current control, are originally proposed to improve the torque dynamics of the drive system. In addition, a damping compensation method is used to improve the input power factor and reduce the input current harmonics. A current-loop predictive controller and a speed-loop predictive controller are first proposed to improve the dynamic responses of the interior permanent magnet synchronous motor drive system. To implement the drive system, a digital signal processor, TMS 320LF2808, is used as a control centre to execute the control algorithms. Experimental results can validate the correctness and feasibility of the proposed methods.

In order to handle undesirable failures of a multicopter, which occurs in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory (SCT) is proposed for the semi-autonomous control mode. The failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real-time information from guidance, attitude control, diagnosis and other low-level subsystems. In order to design a failsafe mechanism for the multicopters, safety issues of the multicopters are introduced. Then, user requirements including functional requirements and safety requirements are textually described, where functional requirements guide the modelling of a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. Based on these requirements, several multicopter modes and events are defined. On this basis, the multicopter plant and control specifications are modelled by automata. Then, a supervisor is synthesized by using SCT. In addition, the authors present three examples to demonstrate the potential conflicting phenomena due to the inappropriate design of control specifications. Finally, based on the obtained supervisor, an implementation method suitable for multicopters is presented, in which the supervisor is transformed into decision-making codes.

This article analyses the structure and working principle of the brushless DC motor, the advantages and disadvantages of PID regulation and fuzzy control are analysed as well, and puts forward the fuzzy PID motor control technology with both advantages. The speed control of the brushless DC motor is carried out and the simulation model is built. The results show that, compared with the traditional PID adjustment, the fuzzy PID makes the motor speed more stable and the speed fluctuation decreases obviously, which satisfies the requirement of the speed control of the electric bicycle.

In this study, a distributed resilient control (DRC) scheme is proposed for inverter-interfaced autonomous AC microgrids (MGs). Due to unavoidable telecommunication vulnerabilities, time-delays already exist in communication links, and it seriously impresses the stability and performance of the network. Due to this fact, non-uniform time-varying communication delays are considered on the communication links (here, information exchanges between neighboring local controllers of DGs and BESSs). The authors consider a time-varying topology for distributed energy resources to address the uncertainties of communications links, improve flexibility, and enhance the security of the system against cyber-attacks. Stability analysis is characterised based on the Lyapunov–Krasovskii theory, and sufficient conditions for a precise and comprehensive control algorithm are derived in terms of linear matrix inequalities (LMIs). The provided LMIs yield the upper bound of the time delay, which guarantees the stability of the system. Finally, to evaluate the performance of the control law, offline digital time-domain simulation studies are performed on a test MG system in a MATLAB/Simulink environment, and also the results are compared with previously reported methods. [ABSTRACT FROM AUTHOR]

Wind energy conversion systems have gained an essential share of the electricity supply and are increasingly competitive with conventional power plants based on fossil fuel. Wind turbines are expected to continue their increase in rated power and size, exceeding 10 MW and more, imposing major challenges concerning not only the operation of future power grids but also mechanical stress on individual wind turbines, their aerodynamic interaction and holistic wind farm control strategies as well as simulation models being able to cover all these aspects. This study deals with requirements of a real‐time capable three‐dimensional wind flow model for wind farm simulation, covering key issues from wind modelling through wake interactions to prospects of advanced control strategies. Building upon this, a modular framework is proposed and implemented by integrating existing validated analytical models. Simulation results demonstrating the model capabilities are presented, discussed and evaluated concerning future research focus. [ABSTRACT FROM AUTHOR]

Classifying power quality (PQ) disturbances is one of the most important issues for PQ control. The S‐transform (ST)‐based neural networks in conjunction with Kalman filter based on maximum likelihood (KF‐ML) are presented for classification of PQ disturbances. To accurately extract features in high‐noise cases, the KF‐ML is used to remove noise from the original distorted waveform. Then, ST technique is used to extract the significant features of disturbances. Finally, a classifier based on multilayers feedforward neural networks can accurately recognise various types of PQ disturbances. Six simulated single disturbances and six complex ones with different noise levels are tested for the sensitivity to noise. Classification results show that the classification accuracy of the proposed method is more than 95% even in 20 dB high‐noise condition, and also validate the superiority of strong rejection to noises. Comparison studies between the proposed method and other classification methods are also reported to show the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Swarms of drones are being more and more used in many practical scenarios, such as surveillance, environmental monitoring, search and rescue in hardly‐accessible areas and so on. While a single drone can be guided by a human operator, the deployment of a swarm of multiple drones requires proper algorithms for automatic task‐oriented control. In this study, the authors focus on visual coverage optimisation with drone‐mounted camera sensors. In particular, they consider the specific case in which the coverage requirements are uneven, meaning that different parts of the environment have different coverage priorities. They model these coverage requirements with relevance maps and propose a deep reinforcement learning algorithm to guide the swarm. This study first defines a proper learning model for a single drone, and then extends it to the case of multiple drones both with greedy and cooperative strategies. Experimental results show the performance of the proposed method, also compared with a standard patrolling algorithm. [ABSTRACT FROM AUTHOR]

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members' background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included. [ABSTRACT FROM AUTHOR]

This study reports on the development and implementation of the fuzzy incremental controller on a small‐scale linear fresnel reflector solar plant. The control problem is concerned with forcing the output temperature to follow the reference, despite the existence of disturbances. Owing to the non‐linear effects of solar plants, the capability of fixed parameters of the proportional–integral controller to cope with the control problem is limited and leads to unsatisfactory control performance. The proposed controller is a modification to a conventional proportional–integral algorithm with better tuning flexibility. Firstly, the ant colony optimisation algorithm is used to define the optimal parameter of proportional–integral plus series feed‐forward controller while guaranteeing a satisfactory performance of the plant. Secondly, the resulting controller is replaced with an equivalent fuzzy knowledge‐based controller with the error and change of error of the plant as the input variables and flow rate as an output variable. The proposed controller is tested on a quasi‐dynamic model of linear fresnel reflector plant using conventional and renewable energy optimisation toolbox in the environment of MATLAB/Simulink. The controller's performance is compared to the proportional–integral controller, where its effectiveness is evaluated under nominal conditions, measurement delay, noise, and presence of disturbance. [ABSTRACT FROM AUTHOR]

The frequency responses of a power system following a generator outage are closely related to the commitment states of generators, which describe the droop and inertial characteristics and system pre‐contingency power dispatch. This study presents a new model to estimate the transient and steady state frequency responses of two‐area power systems at any moment following the loss of an online generator. In the proposed model, the deviations of tie‐flow, area frequencies and rate of change of area frequencies as well as the extreme values of these functions and their associated time of occurrence following the outage of a generation unit are derived as modified analytical functions. A new approach is also proposed to linearise the modified extreme deviations based on pseudo‐Boolean function theorem. In addition, a frequency‐constrained unit commitment model with modified frequency‐dependent constraints incorporating primary frequency regulation reserve is presented as a mixed‐integer linear programming problem. The results indicate that the proposed method gives acceptable approximations for post‐contingency responses and can ensure power system security from frequency viewpoint. [ABSTRACT FROM AUTHOR]

Voltage sag and short interruption lead to the tripping of sensitive equipment and cause the economic loss of the industry users. A programmable logic controller (PLC) is essential equipment used in the industrial process. However, there is no detailed understanding of the sensitivity of PLC under voltage sag and short interruption. The response characteristic of PLC under voltage sag is studied. This study proposes a new testing method, called the dichotomy test method (DTM) to do the test. This study builds the testing platform and tests the sensitivity of six PLCs from five famous PLC manufacturers. The testing considers the characteristics of voltage sag, including magnitude, duration, point‐on‐wave, phase‐angle‐jump, different supply voltage, slow recovery voltage sag, multiple voltage sags and so on. The tests prove that DTM has the advantage of accuracy, robustness and workload, compared with the traditional test method. The quantitative and qualitative response characteristics of PLC under voltage sag are obtained. [ABSTRACT FROM AUTHOR]

An integrated Matlab model comprising of an electro-mechanical actuator (EMA) model, load model and state model has been constructed to investigate the cargo door actuator motion control, operation and working states, of which, a Simulink® servomotor system model is adopted to mimic the EMA actuator, a SimMechanics® model is used to compute the actuator's load, a Stateflow® state transition model is employed to simulate the working process. The results indicated that the motion is well controlled, generated load force is valid, working states and their transition logics are reasonable. The simulation verified the effectiveness and easiness of the integrated modelling method. [ABSTRACT FROM AUTHOR]

This study proposes an effective diagnosis method for single and multiple open‐switch faults (OSFs) in three‐phase permanent magnet synchronous motor (PMSM) drives. The proposed method is based on the real‐time estimation of the three‐phase motor currents using three Kalman filters (KFs). Three residual signals are defined as the differences between the three‐phase measured and estimated stator currents. The averaged normalised residual signals are used as diagnostic criteria for the detection of OSFs. The proposed method accurately detects the OSFs, localises the faulty switches and effectively discriminates between the OSFs and current sensor faults. The main superiorities of the proposed KF‐based method are its fast detection time and high robustness to measurement noises/errors and load variations. Moreover, the proposed method is useful for both closed‐loop and open‐loop PMSM drives. The proposed method can be embedded as a subroutine in the drive control unit without any hardware extension. The effectiveness of the proposed method is confirmed through extensive simulations and several hardware‐in‐the‐loop experiments on a testbed with a 1.5 kW PMSM and dSPACE1104 control board. In addition, the real‐time feasibility of the proposed method using low‐cost microprocessor technology is guaranteed by testing it on TMS320F28335 from Texas Instruments©. [ABSTRACT FROM AUTHOR]

This Letter presents the implementation in a hardware device of the mean arterial pressure (MAP) response model of patients subjected to the infusion of the vasoconstrictor drug phenylephrine, enabling the development of automatic control systems in the future. The proposed model was firstly developed in a software platform with Simulink/MatLab using block libraries based on previous research carried out with animals and then implemented on hardware using a single-board computer with microcontroller and code generated in C/C++. The responses of MAP were simulated and compared using the proposed software and hardware model, considering the dynamic effects of several patients. The results showed that the hardware model presented 0.43% as the largest absolute percentage error observed in all trials, for an injection rate of the drug varying in steps with levels of 40 ml/h [ABSTRACT FROM AUTHOR]

This study proposes an automatic recognition and location system of electric vehicle charging port with application to automatic charging. The system obtains the charging port posture through image processing, and performs the insertion motion in combination with the robot arm to complete the charging gun insertion of the automatic charging link. The framework of the system is mainly divided into three parts, recognition, location and insertion. In the charging port recognition, the convolutional neural network‐based method is used, and the recognition success rate is up to 98.9% under the light intensity of 4000 lux; in the location of the charging port, the method of solving the pose based on the circle feature is adopted. The average value of the position error is within 1.4 mm, and the average value of the attitude angle error is within 1.6°, which meets the accuracy requirement of the insertion experiment; in the charging gun insertion, the motion of the robot is planned by interpolation algorithm. The lower limit of the successful insertion is about 135 lux and the upper limit is about 9350 lux. [ABSTRACT FROM AUTHOR]