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This paper proposes an improved zero-mean normalization sum of squared differences (ZNSSD) algorithm to solve the problem of the inability of traditional structural measurement to extract high-frequency vibration signals. In the proposed technique, the high-speed image sequence of target vibration is captured by a high-speed camera. Then, the ZNSSD template matching algorithm with subpixel accuracy is introduced to process the captured images in the computer. Additionally, a modified search algorithm, the ZNSSD template matching algorithm based on image pyramid (ZNSSD-P), is proposed to significantly reduce the computation time and increase efficiency. Then, a jumping ZNSSD template matching algorithm based on image pyramid (J-ZNSSD-P) is proposed to further improve the efficiency of the ZNSSD-P algorithm. Vibration signals were extracted with Grating Ruler Motion Platform and sound barriers. Results show that the vibration signal extraction method has high precision and efficiency.

Self-optimizing control (SOC) is a technique for selecting appropriate controlled variables (CVs) and maintaining them constant such that the plant runs at its best. Some tough challenges in this subject, such as how to select CVs when the active constraint set changes remains unsolved since the notion of SOC was presented. Previous work had some drawbacks such as structural complexity and control inaccuracy when dealing with constrained SOC problems due to the elaborate control structures or the limitation of local SOC. In order to overcome the deficiency of previous methods, this paper developed a constrained global SOC (cgSOC) approach to implement self-optimizing controlled variable selection and control structure design. The constrained variables that may change between inactive and active are represented as a nonlinear function of available measurement variables under optimal operations. The unknown function is then intelligently learnt over the whole operating region through neural network training. The difference between the nonlinear function and the actual constrained variables measured in real-time is then used as CVs. When the CVs are controlled at zero in real-time, near-optimal operation can be ensured globally whenever active constraint changes. The efficacy of the proposed approach is demonstrated through an evaporator case study.

To improve the performance of the Active Disturbance Rejection Control (ADRC) in systems which have large time delay, reduce online computation for Proportional Integral-type Generalized Predictive Control (PI-GPC) method, the Proportional Integral-type Active Disturbance Rejection Generalized Predictive Control (PI-ADRGPC) based on Controlled Auto Regression and Moving Average (CARMA) model is designed. The frequency domain analysis method is used to analyse the stability of the PI-ADRGPC based on CARMA model. By using the open-loop transfer function of the PI-GPC discrete form, the influence of parameter changes on the PI-ADRGPC performance is analysed. The performance of the PI-ADRGPC and ADRC algorithm is compared through the application in a second-order time delay system and distillation column system. The research results show that compared with the ADRC algorithm, the PI-ADRGPC method has a shorter rise time and better performance.

In this paper, we propose a framework to generate diverse clustering solutions and conduct solution retrieval to improve performance. Specifically, we first project unlabelled data from multiple domains into a shared space while preserving the respective semantics. This space allows that representations of samples in a hard domain are recovered by a linear combination of those of others in the easy domains. Meanwhile, a clustering algorithm is adopted to provide pseudo labels for a conditional generative adversarial network to synthesize representations that in turn promote the learning of the above space. Second, we conduct the joint learning of feature projection and partition matrices on batches of representations, where the former ones are considered as clustering solutions and input into another generative adversarial network to generate more solutions. Third, we utilize the fusion of diffusion to effectively retrieve and extract the knowledge in multiple solutions to obtain the final clustering. We perform comparative experiments against other methods on multiple benchmark data sets. Experimental results demonstrate the effectiveness and superiority of our proposed method.

Data-driven method has gained its popularity in fault detection. Conventional methods are associated with one-single-layer process monitoring. Information extracted by such a method may not be sufficient to detect some faults for complicated process systems. Inspired by the deep learning conception, a multi-layer fault detection method, namely Deep Principal Component Analysis (DePCA) was proposed previously in the literature. DePCA has the capability to extract deep features for a process resulting in better fault detection performance. However, it assumes that the value of the variable at each moment is unrelated, which is not suitable for complex nonlinear dynamic system. To address the concerns, by adopting dynamic PCA to extract dynamic features, a new deep approach, namely Deep Dynamic Principal Component Analysis (DeDPCA), is proposed. In the new approach, both Dynamic feature and nonlinear feature can be extracted in different layers so that more process faults can be detected. A Tennessee Eastman process case study was then employed for application and validation of the DeDPCA, which indicates the proposed method is suitable for monitoring complex dynamic nonlinear processes.

Climate change greatly influenced aquaculture in the Philippines and the deteriorating water quality; in effect, fish production reduced drastically. Thus, fish farmers and aquaculture researchers have been searching for innovative technologies to address these issues. One technology is the biofloc systems. This technology uses a zero-water exchange and accumulates microorganisms that serves as a food source. In the biofloc, water quality management is highly recommended. This study aimed to assess the technical and operational effect on the shrimp growth and survival of the developed automated water quality management internet of things (IoT) system in a biofloc system. The hardware prototype comes with a mobile application which has features namely: account management, fish/shrimp profile, water quality management, auto feeding and manual controls. This study conducts experimental research which assessed the impact of an indoor pond biofloc with the developed prototype on the Litopenaeus vannamei’s growth and survival rates. Results were favorable to the biofloc system with the developed prototype, having a 10% higher survival rate and 3.2% higher growth rate, compared to the traditional recirculating aquaculture systems. Adapting the biofloc and the IoT prototype, a fish farmer may earn P78,300 pesos for each white leg shrimp (Litopenaeus vannamei) harvesting period.

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.

The absolute stability problem of discrete-time time-delayed Lur'e systems with sector bounded nonlinearities is investigated in this paper. Firstly, a modified Lyapunov-Krasovskii functional (LKF) is designed with augmenting additional double summation terms, which complements more coupling information between the delay intervals and other system state variables than some previous LKFs. Secondly, some improved delay-dependent absolute stability criteria based on linear matrix inequality form (LMI) are proposed via the modified LKF and the relaxed free-matrix-based summation inequality technique application. The stability criteria are less conservative than some results previously proposed. The reduction of the conservatism mainly relies on the full use of the relaxed summation inequality technique based on the modified LKF. Finally, two common numerical examples are presented to show the effectiveness of the proposed approach.

A lot of research is being carried out on the Direct Current (DC) servo motor systems due to their excessive applications in various industrial sectors owing to their superior control performance. Parameters of the DC servo motor systems to be used in the simulation software are usually unknown or change with time and have to be determined accurately for obtaining the precise simulation response. In this paper, three different estimation techniques for multi-domain DC servo motor model parameters are discussed namely the Compare Coefficient Method, MATLAB Parameter Estimation Toolbox, and System Identification Toolbox. The paper performs a comparison of these methods to identify the one that gives the most accurate results. Experimental data has been used for the comparison of the estimated response from the techniques. The results show that the parameters obtained from the parameter estimation method give the most accurate simulation results with the least error against the experimental results. The study is significant for guiding researchers to prefer this method for estimation purposes of DC servo motor simulation model parameters. The presented technique, i.e. parameter estimation technique, is relatively less complex and requires less computational cost as compared to other techniques found in the literature.

In this paper, the congestion control for named data networking (NDN) is studied. A novel dynamic content store-based congestion control strategy is proposed on account of the characteristic of in-network cache in NDN. A queuing network model is constructed to judge whether congestion occurs. If the network has the tendency of congestion or the congestion happened, the buffer of the output queue is dynamically expanded by borrowing NDN content store (CS), and the forwarding rates of data packets and corresponding interest packets are reduced so as to prevent or alleviate network congestion. In order to reflect fairness, the CS to be borrowed by the data output queue in the port is calculated in terms of the data output queue length and its weight. The simulation results based on ndnSIM show that the given scheme improves the bottleneck link utilization and maintains a low packet loss rate and average flow completion time.

The need for a perching robot is increasing in the field of rescue and transportation. Accordingly studies on perching an object by attaching a robot arm to a perching robot have been conducted. However, almost all the studies related to perching have been conducted using an actuated or electric device. However, perching by using an electric device has several disadvantages, such as additional power consumption and an increase in the mass of the multicopter used to load the electric source. Instead of using an electric device, perching by using an underactuated gripper can effectively avoid these disadvantages. Accordingly, we developed an underactuated passive gripper that has the advantage of nonconsumption of electric power for perching. A method to confirm the available range for stable perching is one of the problems of using an underactuated passive gripper. Therefore, in this study, we analyze a multicopter carrying an underactuated parallel-link passive gripper for available plane perching. To enable perching on planes with different thicknesses and being embedded at different depths, we summarize the available perching range and limitations based on the friction cone theory. Our conclusion is supported by both theoretical and experimental results.

This article addresses a reach‐avoid game with two heterogeneous defenders and one attacker in a bounded, convex domain consisting of a target region and a play region. The attacker aims to reach the target region before being captured by any defenders, while the defenders strive to capture the attacker in advance. A simple geometric method is introduced that only considers the position relation between the reaching regions of the players and the boundary of the target region to construct the barrier of the two‐player version reach‐avoid game analytically. Next, based on the speed ratio between the defenders, the necessary and sufficient conditions suitable for the general game setups to judge which defender contributes to the barrier and decompose the problem into several two‐player version subproblems are given. Then, the barrier of the original game is constructed analytically by combining these subproblems in all possible situations. The method provided an analytical expression of the barrier and has essentially no computation burden. Thus, the method is suitable for online applications. And solutions given in this work can be extended to general multiplayer situations and the game with complex setups for further research.

This paper studies optimal control and stabilization problems for continuous‐time mean‐field systems with input delay, which are the fundamental development of control and stabilization problems for mean‐field systems. There are two main contributions: (1) To the best of the authors' knowledge, the present paper is the first to establish the necessary and sufficient solvability condition for this kind of optimal control problem with input delay, and to derive the analytical form of an optimal controller through overcoming the obstacle that separation principle no longer holds for multiplicative‐noise systems. (2) For the stabilization problem, under the assumption of exact observability, it is strictly proven that the system is stabilizable if and only if the algebraic Riccati equation has a unique positive definite solution.

This paper presents new stability conditions for two‐dimensional (2‐D) systems in state‐space description. Both discrete and continuous systems are studied. These results are based on the criteria first presented by Huang, De Carlo, Strintzis, Murray, Delsarte, et al. and on the discrete Lyapunov equation with complex elements for 2‐D systems. The stability properties of the Mansour matrix are also used for stability testing in state‐space. Criteria for the VSHP property of 2‐D polynomials are further presented using the continuous Lyapunov equation with complex elements and the stability properties of the Schwarz matrix form. The stability properties of the Schwarz matrix are also used for testing the VSHP property of 2‐D polynomials in state‐space. The proposed new criteria are non‐conservative for the stability analysis of 2‐D discrete and continuous systems and achieve the aim of reducing the original 2‐D problem as much as possible to a set of 1‐D stability tests. Numerical examples are given to illustrate the utility of the proposed conditions.

In this study, moving object recognition is performed by using images from a camera mounted on an unmanned ground vehicle. A GPS coordinate-based algorithm has been developed to obtain moving object silhouettes. In order to classify these silhouettes, an interconnected artificial neural network (ICANN) architecture consisting of two stages has been developed. The method consists of two phases. In the first phase, real-time images are converted to binary images at the end of the GPS-assisted image registration process. Then, the silhouettes are extracted from the background of the images using connected component labelling. In the second phase, two interconnected neural networks are used. The first neural network classifies silhouettes as objects or noise. The second neural network divides objects into seven subclasses as pedestrians, potholes, cars, etc. Compared to CNN-based techniques, a simpler NN architecture was employed in the research, and better accuracy rates were achieved with fewer samples. Another contribution of the research is simultaneous localization and mapping (SLAM) applications can be performed in non-GPS environments using pre-recorded images containing GPS information. In experimental studies, maximum success rates of 96,1% in object classification were obtained. The results obtained were compared to YOLO, the recently popular algorithm for object recognition.

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system.

Bone age assessment is commonly used to determine the growth status and growth potential of children. In this paper, the bone age assessment is regarded as a fine-grained image classification problem as bone age assessment is usually performed on radiographs of the left hand. An end-to-end bone age assessment model was proposed. This model is composed of four parts: feature extractor, Region of Interest (ROI) selection subnet, guidance subnet, and assessment subnet. Feature extractor is implemented based on Convolutional Neural Networks (CNNs), ResNet50 was used to extract image features. ROI selection subnet is used to select multiple informative ROIs that contain representative images features in the radiograph. Guidance subnet can guide the ROI selection subnet to select ROI more appropriately. Assessment subnet is used for bone age assessment by utilizing the extracted image features. The proposed model can extract the most informative ROIs in the radiographs, and use these ROIs to improve the accuracy of bone age assessment. In this paper, the bone age assessment model is tested on a public data set. The experimental results show that the proposed bone age assessment model has the highest accuracy, and the Mean Absolute Error (MAE) reaches 6.65 months.

As vision is a versatile sensor, vision-based control of robot is becoming more important in industrial applications. The control signal generated using the traditional control algorithms leads to undesirable movement of the end-effector during the positioning task. This movement may sometimes cause task failure due to visibility loss. In this paper, a sliding mode controller (SMC) is designed to track 2D image features in an image-based visual servoing task. The feature trajectory tracking helps to keep the image features always in the camera field of view and thereby ensures the shortest trajectory of the end-effector. SMC is the right choice to handle the depth uncertainties associated with translational motion. Stability of the closed-loop system with the proposed controller is proved by the Lyapunov method. Three feature trajectories are generated to test the efficacy of the proposed method. Simulation tests are conducted and the superiority of the proposed method over a Proportional Derivative – Sliding Mode Controller (PD-SMC) in terms of settling time and distance travelled by the end-effector is established in the presence and absence of depth uncertainties. The proposed controller is also tested in real-time by integrating the visual servoing system with a 6-DOF industrial robot manipulator, ABB IRB 1200.

This paper investigates a bumpless transfer control problem for uncertain switched positive linear time‐delay systems (SPLTDSs) with discrete and distributed delays. The innovation of this study is to develop a linear feedback controller and design a switching law that ensures lower controller signal bumps due to the switches of the SPLTDSs and also guarantee the L1–gain performance of the system. To this end, a bumpless transfer performance is proposed, and stabilisation constraints for SPLTDSs with synchronous switching are developed. Dwell time criteria are utilised in the stability analysis. Also, the results are improved to cover the uncertain SPLTDSs with interval or polytopic uncertainties. Then, asynchronous switching is considered, and the proposed method is extended to deals with asynchronous switching mode. Asynchronous switching means switches of the controller have lagged behind the subsystem switches. All the stability conditions are derived by multiple co‐positive Lyapunov–Krasovskii functional for synchronous and asynchronous switching conditions. At last, two numerical examples are presented to show the effectiveness of the proposed method compared with the leading existing solutions.

This paper considers the locally and globally optimal solutions of the global optimisation problem whose objective function is a max‐plus vector‐valued function and constraint function is a real affine function. The formulas about global optimisation in the existing work are further explained and simplified. The necessary and sufficient conditions for the existence and uniqueness of locally optimal solutions of a single objective are established, which are then used to deduce these of globally optimal solutions. Furthermore, the general formulas of locally and globally optimal solutions are presented, respectively. The local optimisation is then applied to solve the load distribution problem of distributed systems with no globally optimal solution, and the optimal allocation scheme is proposed to complete the overall task at the earliest time. The proposed method is constructive, and the obtained results are illustrated by the numerical examples.

This paper addresses the problem of fault diagnosis in discrete event systems through a decentralized structure. Within the proposed decentralized diagnosis structure, local most permissive bbservers (MPOs) are employed as diagnosis tools, and an effective protocol is proposed to coordinate local MPOs to infer diagnosis information. It is shown that the proposed technique can diagnose fault occurrences subjected to proper sensing decisions. A sufficient condition on sensing decision policies is derived for diagnosis of faults in a decentralized setting. Further, it is proven that the proposed decentralized structure has less complexity than a centralized structure. The details of the proposed technique are illustrated by applying the developed method to a manufacturing system.

This is the first attempt to bring the fuzzy relational hybrid models from modelling to control. The main challenge is to configure both the plant model and the controller appropriately so that the overall system has good analytical and numerical properties. However, to close the control loop effectively by connecting the fuzzy relational hybrid model blocks, it is needed for its core mathematical operator to be associative. Therefore, in this paper: (1) It has been shown that the core mathematical operator that relates the connected g‐normal fuzzy relational hybrid model blocks, that is, the Yager‐product fuzzy relational inner composition, unlike most fuzzy relational inner compositions, is associative; (2) An intelligent adaptive control scheme has been developed for a class of plants in which the states of the system can be mixed‐valued and all inputs are of discrete‐valued type (either quantitative or qualitative). Benefiting from the associative property, the proposed control scheme uses the g‐normal fuzzy relational hybrid model building blocks in the role of the controller as well as the plant model with no prior knowledge of the elements of the fuzzy relational matrices. Being applied to control a synchronous buck converter in both regulation and tracking scenarios, and under parameters uncertainties, the proposed scheme has been evaluated by computer simulation.

In this paper, aiming at the actuator fault-tolerant trajectory tracking problem of two-wheeled differential-driven mobile robots, a super-twisting fractional-order sliding mode fault-tolerant control method combined with a fault observer is proposed. The method not only ensures the pose tracking of the robot in normal condition but also guarantees the tracking performance while the fault occurs. The proposed method mainly includes: A fractional-order sliding mode surface which improves the transient response is utilized to design the fault-tolerant controller, and the super twisting reaching algorithm is adopted to reduce the chattering; A fault observer is designed to estimate the fault value, ensures system stability and safety through real-time compensation. Finally, the proposed fault-tolerant control method is verified by numerical simulation. The results show that the method proposed in this paper can effectively reduce the impact of actuator fault and ensure trajectory tracking performance. And the advantage of the control strategy over the general sliding mode controller is that compared with the integer-order sliding mode control (IOSMC), the fractional-order sliding mode control (FOSMC) we designed in this paper converges all the error states to zero faster, and the tracking error chattering is smaller.

This paper studies a high‐performance control design for the servo system of a vehicle‐mounted mobile satellite communication antenna. Considering the different requirements for tracking and disturbance attenuation in the servo system, a novel controller structure consisting of a central controller and a disturbance filter is proposed. Based on this controller structure, a bi‐objective control design scheme is proposed: One is an optimal tracking design for the central controller, and the other is a disturbance attenuation design for the disturbance filter. It turns out that the resulting control system achieves an optimal tracking performance when the external disturbance is at a low level; meanwhile, the system achieves a robust disturbance attenuation performance when the disturbance is significant. Moreover, considering the trade‐offs between performance and the robust stability of the closed‐loop system, the weighting parameter selection in control design criteria is studied for a practical application. Furthermore, validation experiments and field tests are carried out to evaluate the effectiveness of this design, and their results show that the well‐designed bi‐objective controller significantly improves the dynamic pointing accuracy of the antenna in tracking a geostationary satellite when its carrier is driving off‐road.

This study proposes a new software platform, called ROS2-TMS, for an informationally structured environment. An informationally structured environment is vital for developing intelligent service robots by embedding various sensors in the environment to enhance the sensing capability and intelligence of robots. Thus far, we have been developing a software platform, named ROS-TMS, for an informationally structured environment, which connects various sensors and robots using ROS architecture. In recent years, ROS2, a next-generation version of ROS, has been released. ROS2 has many advantages, such as enhanced security, QoS control, and support for various platforms. ROS2-TMS, a new version of ROS-TMS, is developed not only by porting existing modules in ROS-TMS, such as the control system for a communication robot, but also by adding useful functions utilizing new features in ROS2. For instance, we added a voice user interface to control robots and various devices in the environment, such as lights or a bed. In addition, we implemented a new task scheduler that provides a cancelation function to stop running tasks and improve the security of the platform.

This paper investigates some synchronization behaviors of high‐dimensional Kuramoto models with nonidentical oscillators and interconnection digrphs. By using the matrix Riccati differential equation of the state error variables, it is proved that a high‐dimensional Kuramoto model can achieve local practical synchronization when the interconnection digraph is strongly connected or has a spanning tree. Compared with the existing practical synchronization literature, the results are based on general digraphs instead of complete graphs. Moreover, the complete synchronization is proved for proportional nonidentical oscillators limited on a half‐sphere and interconnected by a strongly connected digraph. Finally, some numerical simulations are given to validate the obtained theoretical results.

Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.

The changing weather conditions and Partial Shading Situation (PSS) create numerous challenges in harvesting available maximum power from the solar Photovoltaic (PV) systems. The limitations of classical and bio-inspired optimization-based Maximum Power Point Tracking (MPPT) methods are incapable of extracting maximum power under PSS. Therefore, this paper presents a Modified Seagull Optimization Algorithm (MSOA) based MPPT approach by incorporating Levy Flight Mechanism (LFM) and the formula for heat exchange in Thermal Exchange Optimization (TEO) in the original Seagull Optimization Algorithm (SOA) for accurate tracking of Global Maximum Power Point (GMPP) under transient and steady state operating conditions. The MSOA increases the capability of optimization in finding the optimal value of boost DC-DC converter’s duty cycle, D, for operating at GMPP. The superiority of the presented MPPT approach is contrasted with SOA MPPT under uniform irradiation situation and partial shading situations using Matlab Simulink platform. With the presented MSOA MPPT, the settling time and percentage maximum overshoot are reduced by 0.92 times and 0.55 times in comparison to SOA MPPT with increased efficiency. The hardware results validated the simulation results proving the proposed MSOA MPPT as an efficient MPPT for solar PV systems.

This paper proposes a novel guidance law based on model predictive control for protecting an aircraft from a homing missile. Considering that in the target‐missile‐defender engagement the attacking missile is pursuing the target aircraft by employing a known linear guidance law, a non‐linear predictive model for the engagement is established. This predictive model allows converting the three‐body engagement to a one‐on‐one engagement. The time‐to‐go for the defender towards the future position of the incoming missile is analytically derived, indicating that the optimal time for the defender to intercept the incoming missile is a fixed‐point of a real‐valued function. As a result, the optimal interception duration can be obtained by a standard fixed‐point iteration method, and thus the interception point can be readily predicted via integrating the predictive model. The guidance command is generated by directly leading the defender to the predicted intercept point. Numerical simulations in different scenarios validate the algorithm to be computationally efficient, and demonstrate that the proposed strategy outperforms previous techniques in terms of miss distance, engagement duration, and the control effort.

An electrical plan drawing–sometimes called a wiring diagram or electrical drawing–consists of lines and symbols. Electrical plan drawings are prepared on 2D architectural floor plans using Computer-Aided Design and/or Drafting (CAD) programs. The placement/drawing of electrical power symbols–such as sockets, lights, and switches–is the first step of an electrical plan drawing. For this purpose, a smart system has been developed in this study to automatically draw/place electrical power symbols in appropriate locations. The system is based on the detection and classification/recognition of furnishing (decorative) symbols in the floor plans. We have created a furnishing symbol dataset drawing on dozens of architectural plan drawings that contain symbols of the most commonly used tools in floor plans, such as furniture, appliances, plumbing, doors, and windows. We used a Deep Convolutional Neural Network (D-CNN) with transfer learning–Inception-v3 model– to classify furnishing symbols. We tested the model on 20 real floor plans and achieved a very satisfactory accuracy of 97.05% in furnishing symbol classification. The symbol drawing step, which is the first step of drawing the electrical plan, was automated using the work developed, thus achieving the aim of saving time and labour. Experimental studies show the effectiveness of the proposed automated system.

In this paper, we discuss the cooperative output tracking problem for networked multi‐agent systems (NMASs) with plant‐model mismatch as well as random communication constraints in the forward and feedback channels of each agent. In order to compensate actively for random communication constraints, that is, network‐induced delays and packet dropouts, an incremental networked predictive control scheme based on a state observer is proposed, which consists of a data buffer, an incremental networked predictor, and a network delay compensator. For both the plant‐model mismatch case and the plant‐model match case, the stability conditions of resulting closed‐loop NMASs are derived, respectively. Using the proposed method, the simulation results for three DC motor systems are presented to indicate that desired tracking performance can be achieved.

An H∞ observer‐based control problem of delayed Linear Parameter Varying (LPV) stochastic system is addressed in this paper. In the LPV stochastic system, stochastic behaviors and external disturbance in real environment are respectively described by stochastic difference function. Furthermore, the delay effects on state and control input are considered as time‐varying case for wide application. For the control problem, a lemma dealing with the delay terms is proposed to increase some slack variables. Based on the proposed lemma and the chosen Lyapunov‐Krasovskii function, some sufficient conditions are derived to relax the conservatism of stability criterion. Besides, disturbance effect on state and estimation error is constrained via H∞ performance for improving transient response. Through using the extended projection lemma, the derived conditions are formulated as strict Linear Matrix Inequality (LMI) problem. To avoid the potential oscillation, another lemma is proposed to separately assign the performance level during solving the conditions. All feasible solutions can be directly obtained by convex optimization algorithm without using extra calculation. Therefore, an observer‐based controller is established to guarantee the asymptotical stability and H∞ performance in the mean square. Finally, some numerical simulations are provided to demonstrate the effectiveness and applicability of the proposed method.

This paper intends to design an integral backstepping Lyapunov redesign controller (IBLRC) for various uncertain strict‐feedback‐form nonlinear systems. The pros and cons of backstepping Lyapunov redesign resulted in the design idea. An integral term is incorporated in conventional backstepping with Lyapunov redesign to indirectly decrease chattering and steady‐state error of a reference signal tracking when unknown static parameters and matched and unmatched dynamic uncertainties exist. The closed‐loop system is mathematically confirmed as stable in a Lyapunov frame, and we ultimately reach uniformly semi‐global boundedness of all signals. Simulation results of the proposed IBLRC are juxtaposed with those of the classic backstepping with the Lyapunov redesign. The proposed IBLRC realizes excellent tracking and enhanced robust performance than the BLRC for time‐varying and step tracking references.

[ES] En este trabajo se aborda la integración de dos métodos o estrategias de Control Predictivo basado en Modelos, a saber: Control Predictivo basado en Modelos Borrosos (FMBPC) y Control Predictivo en Lazo Cerrado (CLP MPC). La primera de estas estrategias utiliza principios de Control Predictivo Funcional (PFC) y está enmarcada, al mismo tiempo, en el ámbito del Control Inteligente (IC). La integración tiene como principal objetivo proporcionar a la estrategia de control no lineal FMBPC un procedimiento de optimización que permita el manejo automático de restricciones en la variable de control. La solución propuesta consiste en hacer uso de una estructura complementaria de tipo CLP MPC para determinar mediante optimización, en cada instante de muestreo, los valores óptimos de un cierto término aditivo, a sumar a la ley de control FMBPC, de tal modo que se satisfagan las restricciones. El modelo de predicciones y la ley de control base necesarios para realizar los cálculos en la estructura CLP MPC son proporcionados por la estrategia FMBPC. La estrategia mixta FMBPC/CLP propuesta ha sido validada, en simulación, aplicándola al control de fangos activados en plantas de tratamiento de aguas residuales (EDAR), poniendo el foco en la imposición de restricciones a la acción de control. Los resultados obtenidos son satisfactorios, observando un buen rendimiento del algoritmo de control diseñado, al tiempo que se garantiza tanto la satisfacción de las restricciones, que era el principal objetivo, como la estabilidad del sistema en lazo cerrado., [EN] This work addresses the integration of two methods or strategies of Model-Based Predictive Control, namely: Fuzzy Model-Based Predictive Control (FMBPC) and Closed-Loop Predictive Control (CLP-MPC). The first of these strategies uses principles of Predictive Functional Control (PFC) and is framed, at the same time, in the field of Intelligent Control (IC). The main objective of the integration is to provide to the FMBPC nonlinear control strategy an optimization procedure that allows the automatic handling of constraints in the control variable. The proposed solution consists of making use of a complementary structure of the CLP-MPC type to determine by optimization, at each sampling instant, the optimal values of a certain additive term, to be added to the FMBPC control law, in such a way that they are satisfied the constraints. The prediction model and base control law necessary to perform the calculations on the CLP-MPC structure are provided by the FMBPC strategy. The proposed FMBPC/CLP mixed strategy has been validated, in simulation, applying it to the control of activated sludge processes in wastewater treatment plants (WWTP), focusing on the imposition of constraints on the control action. The results obtained are satisfactory, observing a good performance of the designed control algorithm, while guaranteeing both the satisfaction of the constraints, which was the main objective, and the stability of the closed-loop system., Este trabajo contó con el apoyo económico del Gobierno de España a través del proyecto MICINN PID2019-105434RB-C31 y de la Fundación Samuel Solórzano a través del proyecto FS / 20-2019.

[EN] This work proposes the design of an adaptive controller for a marine vessel; the proposed control strategy applies a controller designed on linear algebra for the kinematics and an adaptive control technique for the dynamic part of the vessel. The linear algebra based controller (LABC) for kinematics receives the desired position references and this generates another reference velocity pair for the adaptive (dynamic) controller. The main goal of the application of the adaptive control technique in this kind of enforcement is presented in the case that the mass of the vessel varies with its trajectory (e.g. fishing vessel, refueling vessel, etc.) where the adaptive controller adjusts its parameters through of adaptation law, which in turn generates a control action that compensates dynamic variations of the ship. Besides, this work presents the stability analysis and adaptive adjustment law based on the Lyapunov theory. And the simulation results that are presented prove that the control can deal with non-linearities and time-variant dynamics., [ES] Este trabajo muestra el diseño de un controlador adaptable para un buque marino; la estrategia de control que se propone es la aplicación de un controlador basado en álgebra lineal para la cinemática y una técnica de control adaptable para la parte dinámica del buque. El controlador basado en álgebra lineal (LABC) para cinemática recibe las referencias de posición deseadas y esto genera otro par de velocidad de referencia para el controlador adaptable (dinámico). El objetivo principal de la aplicación de la técnica de control adaptable se presenta en el caso de que la masa del buque varíe con su trayectoria (por ejemplo, buque pesquero, buque de reabastecimiento de combustible, etc.) donde el controlador adaptable ajusta sus parámetros mediante la ley de adaptación, que a su vez genera una acción de control que compensa las variaciones dinámicas del buque. Además, este trabajo presenta el análisis de estabilidad y la ley de ajuste adaptable basada en la teoría de Lyapunov. Los resultados de simulación muestran que el sistema puede seguir las señales de referencia con un error muy bajo aún en presencia de incertidumbre.

A prominent and realistic problem in the domain of magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor such that it functions efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a novel optimization technique inspired by nature, called the Coronavirus Herd Immunity Optimizer (CHIO), is proposed. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of Coronavirus Herd Immunity Optimizer called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it has been directly used to optimize the BLDC motor design optimization problem. The BLDC motor design problem has two main objectives, such as minimization of the motor mass and maximization of the motor efficiency with five constraints and five design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. In addition, the experimental results are compared with the multi-objective whale optimization algorithm (MOWOA), multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimizer (MOPSO), multi-objective moth flame optimizer (MOMFO), and multi-objective bat algorithm (MOBA) are presented to confirm the viability and dominance of the proposed MOCHIO algorithm.

This article presents a predictive extended state observer-based repetitive controller (PESO-RC) to simultaneously track and reject periodic signals on systems with long input delay and parameter uncertainties. First, a novel extended state observer (ESO) is proposed to tackle periodic signals on processes with input delay. Then a simple low pass filter is incorporated and tuned to improve robustness against modelling errors. Moreover, the modified repetitive controller (MRC) is integrated to enhance the performance when compensating periodic signals without affecting the overall system’s stability. Stability criteria and robust stability analysis under modelling errors are studied to develop tuning guidelines. Furthermore, validation of the proposed controller and comparison studies are simulated in MATLAB and tested on a brushless DC servo motor which highlight the superior performance of PESO-RC.

The design of a Fractional‐Order fuzzy‐based PID (FO‐F‐PID) control structure is presented for Buck converter in the presence of harmful disturbances. A fractional‐order proportional‐integral‐derivative (FO‐PID) control scheme is utilized initially to damp the oscillations and remove the steady‐state error. To increase the tendency rate of the error to zero, the FO‐PID method is applied to a fuzzy‐logic‐based compensatory stage. At the same time, the fuzzy part gathers the data based on the error and error derivative. The FO‐PID control scheme has the capability to enhance the robustness of the control technique against disturbances and parametric variations. Furthermore, to optimize the control parameters, an efficient algorithm so‐called Antlion Optimization (ALO) algorithm, is used. Utilizing the ALO algorithm for tuning the FO‐PID gains depicts more accurate responses in solving constrained problems with diverse search spaces. Considering numerous disturbances on DC‐DC converters, an FO‐F‐PID controller can be an appropriate alternative since it is more robust against load variations and noise. Moreover, PSO‐PID and FO‐PSO‐PID controllers are designed to drive a comparison between them. Finally, the merits of the presented controller are validated for various scenarios. It can be seen that the FO‐F‐ALO‐PID method provides much better results with faster dynamics. Matlab‐Simulink environment is used for the simulations, and the experimental results are tested by the micro‐processor to validate the superiority of the proposed method.

This paper investigates the heading control problem of maritime autonomous surface ships (MASSs) in the presence of actuator anomalies. A shared control framework that includes a ship autopilot and a human pilot, is constructed to realize the accurate tracking of the time-varying command signals. Specifically, the human pilot is responsible for high-level decision making such as anomaly estimation, anomaly correction and monitoring analysis, and the ship autopilot is responsible for a low-level task of command following. With the proposed shared control framework, the ability of the ship autopilot can be significantly enhanced compared to entirely automated tracking. Through Lyapunov stability analysis, it is proven that the tracking error is ultimately bounded, while all the signals of the closed-loop system remain bounded. Finally, a simulation example is presented to prove the effectiveness of the proposed shared control architecture for MASSs under actuator anomalies.

A quasi‐differential type event‐triggered model predictive control (ET‐MPC) framework for continuous‐time linear systems with additional disturbances is constructed. Different from the existing ET‐MPC, the triggering condition of the proposed method is focused on the differences of the errors between the actual states and the best prediction sequence at two consecutive sampling moments. Its advantage is that the dynamic characteristics of state changes can be better considered, which will achieve a more effective balance between communication load and system control performance. Then, to deal with persistent disturbances, a time‐varying tightening robustness state constraint is utilized in this scheme, instead of conventional state constraints. Finally, the new ET‐MPC algorithm is designed based on the dual‐mode control framework, and its feasibility, the system closed‐loop stability, as well as avoidance of Zeno behaviour are rigorously verified. Simulations and comparisons are provided to demonstrate the algorithm effectiveness.

An asymptotic output feedback controller for the hydraulic systems with model uncertainty compensation and prescribed performance is proposed here, where state observers, funnel function and nonlinear adaptive controller are integrated via backstepping technology. Only position signal is available in the process of controller design, the velocity and load pressure are estimated by observers. And, the adaptive control law is carried out to approximate disturbance. Then, a funnel function is introduced to guarantee the prescribed tracking error performance and the potential singularity problem in prescribed performance control is avoided. Furthermore, the whole closed system is proved to be asymptotically stable using Lyapunov method. Comparative experimental results performed on hydraulic systems are studied to illustrate the effectiveness of the proposed controller.

This paper proposed an adaptive super‐twisting (AST)‐based fractional‐order sliding mode control algorithm under disturbances for redundantly actuated cable driving parallel robots. A novel fractional‐order non‐singular fast terminal sliding mode surface is constructed, and the fast response convergence and accuracy tracking performance can be obtained in the sliding mode phase; meanwhile, an auxiliary system is designed to overcome the adverse effects of the input saturation. Then, to compensate for the model uncertainty and external disturbances, the uncertainty and disturbance estimation is designed, and a novel AST algorithm is developed to suppress the estimation error without the known bound of the adaptive gains and guarantee the finite‐time convergence in the reaching phase. The Lyapunov stability theory is employed to prove the convergence of the proposed controller. Finally, numerical simulations are conducted in the commercial software of MATLAB/SIMULINK to demonstrate the effectiveness of the proposed controller.

This article presents an active disturbance rejection approach for the control of the temperature in the cold face of a thermoelectric module fed by a type buck DC-DC converter. The dynamics of the module is seen as a disturbance of an unknown nature and varying in time, of the output voltage of the converter. Said disturbance is estimated by means of a generalized proportional integral type observer, which in combination with the controller allows to regulate the temperature on the cold face of the thermoelectric module to a desired constant value. The designed observer simultaneously estimates the step-down converter output voltage and the exogenous disturbance in an on-line cancellation scheme, known as active disturbance rejection control. For comparison purposes, a proportional integral type controller and a linear quadratic regulator are designed, based on an approximate linearization of the combined dynamic model of the buck converter and the module. The experimental results were obtained by means of an experimental prototype and allow to show the effectiveness of proposed control technique for this type of thermoelectric devices.

Two-wheeled self-balancing robots (TWSBR) is a highly nonlinear and inherently unstable under-driving system. When controlling its movement on an inclined surface, it is more difficult than when it is on a level road. This paper proposes a fuzzy fractional-order PID (FFOPID) controller for the motion control of a TWSBR system in an inclined environment. The control goal of TWSBR is to realize the wheel position control and to stabilize the non-vertical direction of intermediate body (IB). Finally, we compare the control effect of the proposed FFOPID controller with that of the integer-order PID controller, the fuzzy PID (FPID) controller, and the fractional-order PID (FOPID) controller when TWSBR moving on the inclined plane. The simulation results show that the FFOPID controller has better control performance and anti-interference ability.

This article examines a decentralized event‐triggered robust model predictive control (MPC) for a class of networked large‐scale non‐linear Lipchitz systems. It is assumed that the subsystems are geographically distributed and the connections can be made over a communication network and therefore local event generator modules are used. An event‐triggering condition is then proposed for each module, which only uses local information to trigger data via the communication channel. In this way, the information exchange between subsystems can be reduced significantly compared to time‐triggered conventional control approaches, while the asymptotic stability of the closed‐loop is maintained. In contrast to the reported event‐triggered MPC results, the optimized controller is calculated based on state feedback control law for individual subsystems, which minimizes the upper limit on the infinite horizon cost function subject to constraints on the control inputs. The validness of the proposed scheme is demonstrated by simulation results.

Bilateral teleoperation systems are widely used to compensate for image spin and in many other applications. Good transparency (position tracking and force feedback) is an important performance index in the control design of bilateral teleoperation systems. This study aims to achieve good transparency for teleoperation systems using force sensorless by proposing an alternative to direct force measurement. Our proposed method is a finite‐time adaptive sliding‐mode‐assisted disturbance observer, which combines disturbance observer with adaptive sliding mode control. The finite‐time stability of adaptive sliding‐mode‐assisted disturbance observer is analysed using the Lyapunov theory. Moreover, a bilateral control law based on adaptive sliding‐mode‐assisted disturbance observer is designed. The proposed method not only improves the system transparency with both force feedback and position tracking, but is also chatter‐free. Experiments are conducted using bilateral brushless DC motors in an experimental setup to demonstrate the effectiveness of the proposed method.

This article discusses the exponential synchronisation problem for multiple neural networks with time‐varying delay and a more general non‐Laplacian coupling matrix is considered. To reduce the redundant communication, two event‐based time‐varying delay hybrid impulsive control schemes are proposed with static event‐triggered condition and dynamic event‐triggered condition. These novel control schemes neatly combine the event‐triggered coupling strategy with the time‐varying delay hybrid impulsive control, where the impulsive instant is only determined by the specified event‐triggered condition. Moreover, a delay impulsive differential inequality is established to discuss the synchronisation problem for controlled multiple neural networks. By using Stolz's theorem and introducing the average event‐triggered delay impulsive gain, some less conservative sufficient conditions are derived to assure the stability of the time‐varying delay multiple neural networks. Furthermore, the Zeno behaviour can be eliminated effectively and the consumption of the computing resources can be reduced. Finally, some examples are given to verify the superiority of the results. First, a static event‐triggered time‐varying delay hybrid impulsive control scheme was proposed to lessen the communication burden. Then, a time‐varying delay impulsive differential inequality was established to guarantee the synchronisation of multiple neural networks. Moreover, the authors also discussed the issue that continuously triggering and sampling can be excluded.

Optimal control and reinforcement learning have an associate “value function” which must be suitably approximated. Value function approximation problems usually have different precision requirements in different regions of the state space. An uniform gridding wastes resources in regions in which the value function is smooth, and, on the other hand, has not enough resolution in zones with abrupt changes. The present work proposes an adaptive meshing methodology in order to adapt to these changing requirements without incrementing too much the number of parameters of the approximator. The proposal is based on simplicial meshes and Bellman error, with a criteria to add and remove points from the mesh: modifications to proposals in earlier literature including the volume of the affected simplices are proposed, alongside with methods to manipulate the mesh triangulation.

Bilateral systems of teleoperation are useful in a wide range of tasks that can be dangerous or inaccessible for a human operator. However, when time varying delays appear in the communication channel, perfect transparency can no longer been sured. Instead, other control objectives may be pursued, such as the kinematic correspondence between the local and the remote robots. Furthermore, since most of the commercially available robots are not equipped with velocity sensors, to guarantee a certain level of transparency or kinematic correspondence the controller can not rely on velocity measurements. This article is focused on presenting the experimental results of a robust scheme to time delays for non-linear manipulators connected through a communication channel. The main advantages of the control scheme are: i) it is not necessary to know the dynamic parameters of the manipulators; ii) it is not necessary to impose restrictions in the behavior of the human operator and the remote surface; iii) an estimation of velocities is obtained through a second order linear filter; iv) a term that guarantee force tracking is added to the scheme as well. In order to show good performance, we report a set of experiments that have been carried out between different cities in Mexico using the Internet as a communication channel.

Restoring the controllability of networked control systems is a fundamental issue that needs to be settled, especially when malicious attacks or malfunctions occur. Previous studies tried to address such an issue by adding extra driver nodes or rewiring edges, which will inevitably change the original network structure. In this paper, an algorithm to restore the controllability of networked systems while preserving the integrity of the original network structure is proposed. Specifically, a static uncontrollable network will be transformed into a controllable temporal network by means of the cactus‐based segmentation method. The original problem will be equivalent to the classical set cover problem, which is known to be NP‐Hard, if the least number of segmentations is considered. An approximation algorithm with polynomial time complexity is proposed and it is proved that the solution to the problem is two‐optimal. Finally, simulations are carried out to verify the effectiveness and feasibility of the proposed algorithm.