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We study the structure at infinity of transfer matrices with time-varying coefficients. Such transfer matrices have their entries in a skew field F of rational fractions, i.e. of quotients of skew polynomials. Any skew rational fraction is the quotient of two proper ones, the latter forming a ring F p r (a subring of F) on which a "valuation at infinity" is defined. A transfer matrix G has both a "generalized degree" and a valuation at infinity, the sum of which is the opposite of the "defect" of G. The latter was first defined by Forney in the time-invariant case to be the difference between the total number of poles and the total number of zeros of G (poles and zeros at infinity included and multiplicities accounted for). In our framework, which covers both continuous- and discrete-time systems, the classic relation between the defect and Forney's left- and right-minimal indices is extended to the time-varying case. The exact model-matching problem is also completely solved. These results are illustrated through an example belonging to the area of power systems. [ABSTRACT FROM AUTHOR]

Relevance. Modern approach to development of oil and gas fields. Digitalization and automation of hydrocarbon production allow not only to reducing the response time to possible deviations from the operating level of production, but also quickly making decisions on its further normalization. The use of modern software to create a reservoir model using the material balance method allows you to calculate current and predicted reservoir pressures depending on the amount of selected and injected fluid. With high-quality adaptation, the integration of the reservoir model, wells and collection system, in its turn, allows you to correctly approach the choice of a field development system, as well as correctly assess the possible potential of the field. Forecast calculations performed on an integrated model can form the basis for budget planning, as well as the basis for carrying out geological and technical activities on wells, commissioning new wells, and modernizing equipment and infrastructure. Moreover, group optimization calculation allows us to maximize additional oil production, taking into account all geological and technical constraints. Aim. Formation and analysis of an approach to setting up a reservoir model by adapting parameters using the material balance method; formation of a solution for adapting the integrated model, reconciliation of the results with actual data; carrying out an optimization calculation and obtaining additional oil production. Methods. Adjustment and adaptation of reservoir models using the material balance method; adjustment and adaptation of the integrated model of a field/group of fields; calculation of the forecast for a period of 30/90 days; optimization calculations. Results. The obtained results make it possible to adapt the components of the integrated model with an accuracy of ±5 %, which makes it possible to simulate the operating mode of the field. As a result of the analysis, tuning criteria were identified, and the minimum required set of parameters for a qualitative adaptation of the reservoir model and the collection system was presented. The effectiveness and accuracy of the tool are also proven by comparing actual data on operating modes with synthetic ones. Based on the work done, it can be concluded that the use of the integrated model shows us a high convergence with real data, which allows us to optimally approach the field development mode. Moreover, an optimization calculation was carried out to obtain additional oil production using the proposed solution, and a forecast for oil production for a period of 90 days was calculated, which was later confirmed when compared with actual data.

In this paper, a model matching feedback law design technique is applied to a macroeconomical model. We calculate, using computational algebra methodology, which paths of government expenditure and extra taxation will lead the system to a desired dynamic behavior. The solution is based on algebraic methods and the development, in computer algebra software, of appropriate symbolic algorithms that produce a class of feedback laws as solutions. A method for solving a linear algebraic system of polynomials equations is provided, as well as its application to the feedback law design. [ABSTRACT FROM AUTHOR]

The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops. [ABSTRACT FROM AUTHOR]

A prompt and accurate prediction of air quality index (AQI) has become a necessity to tackle the mounting environmental threats. This paper proposes a feature-driven hybrid method for hourly, 3-step-ahead, and deterministic AQI prediction, which includes three modules. In Module 1, an "extract-merge-filter" procedure of feature engineering is created to capture the potential features from the AQI series. Ten feature sets are generated as candidates. In Module 2, six models including Light Gradient Boosting Machine, Extreme Gradient Boosting, Long Short-Term Memory, Convolutional Neural Network, Multilayer Perceptron, and Deep Neural Network are developed as base predictors and performed on the candidate features. In Module 3, predictors are first matched with their optimal features using a comprehensive metric, and then combined in an optimized ensemble using OPTUNA. A case study on the AQI data from four different Chinese cities is carried out to demonstrate the method. The experimental results show the following: (1) Feature engineering significantly boosts prediction performance and provides interpretable findings for practical use. (2) Customized input of features to the predictors is more effective than a fixed input and can rise the performance to a higher level. (3) OPTUNA is a promising tool for optimizing ensemble weights. The final ensemble model is superior to single machine learning models and has a good robustness. [ABSTRACT FROM AUTHOR]

In order to improve the rapidity and immunity of microgrid inverter response, an inverter control structure with augmented state feedback plus dynamic feedforward is designed. The control structure is based on the augmented state feedback, robust mutual mass decomposition and Youla parameterisation theory, which can get the augmented state feedback control parameters and feedforward control structure, and the specific expression of feedforward controller is obtained by model matching theory, after which the Current loop compensation structure is added by combining the inverter and controller structures. The load switching and three-phase unbalance experiments are designed by Matlab/Simulink. The results show that without changing the structural parameters of the original system, the new controller can quickly stabilise the voltage fluctuation within 20 ms, change the fluctuation from 15 V to 2 V, and reduce the unbalance degree from 4% to less than 1%, effectively improving the response speed and anti-interference performance of the whole system, The speed and robustness of the inverter are improved.

Carbon price is closely related to energy conservation and emission reduction cost, and carbon price forecasting is conducive to improving and stabilizing market trading mechanisms. Considering the matching relationship between the prediction model and the carbon price market, a decomposition ensemble carbon price forecasting method based on reinforcement learning model fusion is designed. Firstly, considering the problem of disharmony between different base models and data patterns, a model matching strategy is developed to match the appropriate prediction base model for each carbon trading market. Secondly, the empirical wavelet transform (EWT) algorithm is used to decompose the carbon price into several subseries, which reduces the complexity of the original data and improves the prediction effect of each base model. Finally, for each carbon price market, the Q-learning algorithm of reinforcement learning is used to integrate the prediction results of the selected multiple base models into the final price forecasting of the corresponding market. The model is verified in the seven carbon trading markets, and experiments show that the model has superior and stable prediction performance compared with other methods. The mean absolute percentage errors of the Hubei, Beijing, Shenzhen, Chongqing, Tianjin, Shanghai, and EU markets are only 0.3515%, 1.2335%, 1.3388%, 1.2128%, 0.3229%, 0.2418%, and 0.4094%, respectively. It shows that the EWT method and reinforcement learning ensemble strategy do improve the prediction performance, and the proposed model can be used as a feasible tool for price assessment and management in carbon price markets. [ABSTRACT FROM AUTHOR]

The permeability characteristics of sandstone are of great importance for unconventional natural gas extraction, CO2 geological storage, coal mine gas safety, etc. Under the influence of tectonic stress and mining-induced stress, the evolution of permeability becomes exceptionally complex. A number of studies have focused on the evolution of permeability under different stress paths, ignoring the effect of water on sandstone permeability. The permeability evolution of sandstone in dry and saturated states under high and low surrounding pressures was investigated by triaxial tests. The results show that the triaxial compressive strength of the specimen significantly decreases under saturated conditions. Specifically, the triaxial compressive strength decreases by 27.12% and 10.52% under low and high confining pressure, respectively. Compared to the dry specimen, the permeability of the saturated specimen significantly decreases at all stages of loading, the initial permeability of the saturated specimen is decreased by 92.09% and 56.25% at low and high confining pressure, respectively. The S&D model was matched with the test data, an excellent goodness of fit was obtained in the elastic and plastic phase, and the mechanism of water affecting sandstone permeability was analyzed. [ABSTRACT FROM AUTHOR]

In this paper, a model matching feedback law design technique is applied to a macroeconomical model. We calculate, using computational algebra methodology, which paths of government expenditure and extra taxation will lead the system to a desired dynamic behavior. The solution is based on algebraic methods and the development, in computer algebra software, of appropriate symbolic algorithms that produce a class of feedback laws as solutions. A method for solving a linear algebraic system of polynomials equations is provided, as well as its application to the feedback law design.

The problem of the robust neural network-based model matching control is considered for a large class of uncertain immune systems. In order to achieve the purpose of therapeutic enhancement, it is essential to deal simultaneously with the effects of plant uncertainties, time-varying perturbations, and continuing environmental pathogens. Neural network control algorithm, robust $H_{\infty } $ control theory and VSC technique are combined to construct the hybrid adaptive/robust tracking control scheme such that the controlled immune system achieves a satisfactory model matching control performance. An adaptive neural network system is constructed to learn the behavior of the immune system dynamics. Moreover, an algebraic Riccati-like inequality must be solved to achieve a desired $H_{\infty } $ control performance. Consequently, the robust control scheme developed here can be analytically computed and easily implemented. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

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In this paper, a dynamic compensation strategy based on a residual generator was proposed to solve the problem of the power quality of the bus in an isolated AC microgrid. Firstly, the disturbance analysis of power quality problems such as voltage fluctuation, three-phase imbalance, harmonic wave is carried out, and the disturbance current is converted into the time domain function in the dq rotating coordinate system. Secondly, the inverter establishes the state space model under the dq rotation coordinate system. Then, the control structure based on residual generator is proposed, the voltage compensator is solved by model matching method, and the current loop secondary compensator is designed to eliminate the influence of the voltage compensator on the current loop. Thirdly, the residual capacity calculation method of the inverter is analysed, and the residual capacity is used to achieve the power quality collaborative governance on the parallel inverter system with the perturbation allocation method. Finally, the control strategy proposed in this paper is verified by the simulation built in PSCAD/EMTDC, and the results can be effectively applied to the low-level control of microgrid, and the power quality control effect of voltage fluctuation, three-phase unbalance and harmonics is good, which improves the robustness of the system to a certain extent.

The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops.

Automatic and robust hand gesture recognition remains challenging after many decades of study. Human beings are able to recognize a variety of hand gestures with 100% accuracy solely based on the contour of the hand. Hence, there must be an automatic method that is able to recognize the same variety of hand gestures solely based on the contour of the hand with 100% accuracy. The key technique lies in how to extract the features of the hand's contour effectively. In this paper, we propose to recognize the hand gestures with the contour features extracted by slope difference distribution (SDD). Firstly, the hand is segmented, its centroid is computed and its contour is extracted. Secondly, the peak features and valley features of the hand contour are computed by the SDD. Thirdly, the hand gesture is recognized by model matching based on the SDD peak features and the SDD valley features. The proposed hand gesture recognition method was tested on three public datasets and it achieved 100% recognition accuracy for all the 10 gestures in two public datasets. [ABSTRACT FROM AUTHOR]

Chronic pain affects about 100 million adults in the US and is primarily treated with drugs, which can have significant side effects. A promising alternative therapy is electrical peripheral nerve stimulation (PNS) treatment. However, it has been associated with suboptimal efficacy since its modulation mechanisms are unclear, and the current therapies are primarily open-loop (i.e. manually adjusting the stimulation parameters). Critical to advancing PNS treatment is a deeper understanding of pain processing, but the pain system is difficult to probe experimentally and analyze via mathematical models. Previously, mechanistic models of pain transmission have been developed to investigate modulation mechanisms but are nonlinear and high-dimensional. In this work, we construct linear phenomenological models of spinal cord activity in chronic pain and healthy conditions derived from state-of-the-art electrophysiological recordings from rats. We can then accurately predict the neural responses to electrical stimulation of the peripheral nerve in both conditions. We then apply H ∞ control systems techniques to drive the dynamics of the chronic pain model into normal ranges of physiological pain by using closed-loop control of the PNS. This proof-of-concept computational framework will guide the development of new closed-loop PNS therapies for chronic pain, one of the most prevalent diseases on earth. [ABSTRACT FROM AUTHOR]

Aiming at the problem of road parking space recognition and counting, this paper studied the method based on affine transformation and template matching. Firstly, it preprocessed the collected video images, and detected the candidate par-king spaces based on PPHT and line clustering, and calculated the inclination angle of parking lot marking. Then, it used an algorithm based on affine transformation and template matching to detect the marking at the entrance of the parking space and mark it with a red circle. Then, it used the perceptual hash algorithm to eliminate the images with high similarity and connected the remaining images in order. Finally, it used the dynamic gauge method, Lagrange function and Grubbs criterion to record the number of parking spaces in the connection diagram. In the experiment, it collected the parking space images of four different sections in sunny, cloudy and rainy days to make the corresponding data sets, and the proposed method was evaluated quantitatively. The results show that the accuracy of the proposed method is 98.46% and the accuracy of counting is 99.52%. [ABSTRACT FROM AUTHOR]

As a feedback control methodology exclusively targeting asynchronous sequential machines (ASMs), corrective control has been rapidly developing for the past two decades. This paper presents a comprehensive survey on the theory and application of dynamic corrective control in which the controller also has the form of an ASM. First, basic notions and principles of dynamic corrective control, including models of ASMs and configurations of closed-loop systems, are reviewed. Next, assorted dynamic corrective control schemes are presented aiming at solving specific control problems of ASMs, such as model matching and fault-tolerant control. Variations of control aspects are classified according to modeling formalisms of controlled ASMs—input/state, input/output, and composite ASMs—and involved fault characteristics, such as transient, permanent, and intermittent faults and intelligent attacks. Representative results on the application of fault-tolerant corrective control to real-world engineering systems are also provided with an emphasis on space-borne digital systems. Finally, some challenging topics for future studies on corrective control are discussed. [ABSTRACT FROM AUTHOR]

This work deals with the problem of designing a feedback compensator that forces the output of a linear system with abrupt discontinuities in the state evolution and polytopic uncertainties to match that of a given model with the same features. First, the case in which the system and the model are initialized at zero and output matching is required to be exact is considered. Then, the case in which, for arbitrary initialization, output matching is required to be asymptotic for sufficiently slow sequences of the time instants wherein the state exhibits abrupt discontinuities is studied. In addition, on the assumption that the model is stable for sufficiently slow jump time sequences, also the further requirement that asymptotic output matching be achieved with stability of the compensated system is investigated. Constructive, directly checkable, solvability conditions for the problems addressed are derived by leveraging on appropriate structural notions and geometric tools. Algorithmic procedures for the synthesis of the compensators, when the solvability conditions are met, are devised. Some illustrative examples conclude the work. [ABSTRACT FROM AUTHOR]

It is a common practice to handle labeled data with classifiers and unlabeled ones with clusterings. The traditional Bayesian network classifiers (BNC$^{\mathcal {T}}\text{s}$ ) learned from labeled training set $\mathcal {T}$ directly map the unlabeled test instance into the network structure to calculate the conditional probability for the classification, which neglects the information hidden in the unlabeled data and will result in classification bias. To address this issue, we propose a novel learning framework, called model matching, that uses the “clustering” strategy to solve the classification problem. The labeled data is divided into several clusters according to the different class label to learn a set of BNC$^{\mathcal {T}}\text{s}$ and a corresponding set of BNC$^{p}\text{s}$ is built for each unlabeled test instance. To make a classification, the cross entropy method is applied to compare the structural similarity between BNC$^{\mathcal {T}}$ and BNCp. The extensive experimental results on 46 datasets from the University of California at Irvine (UCI) machine learning repository demonstrate that for BNCs model matching helps improve the generalization performance and outperforms the several state-of-the-art classifiers like tree-augmented naive Bayes and Random forest.

This paper reviews the identification and optimal control problems using recurrent neural networks and reinforcement learning for nonlinear systems both in discrete- and continuous-time. Since neural networks can approximate any nonlinear function, then is shown that it can approximate a dynamical system using some well-identified elements and different neural structures. Existing methods using Lyapunov and Riccati equations to get the neural weights update rules are reviewed. Optimal control using a linear quadratic regulator formulation or reinforcement learning methods are discussed. We discuss the normalized gradient descent algorithm as core algorithm for the optimal control design using reinforcement learning. [ABSTRACT FROM AUTHOR]

Recently, in the steering control system based on the steer by wire and the automatic operation, it is possible to flexibly change the relationship between steering input and vehicle response characteristics. In the previous research, robust stabilization by H.inf. Control and model following control Automatic steering by LQG control optimal observer such as vehicle steering system considering uncertainty of vehicle dynamics model, improvement of vehicle performance by robust control rule, and inverted pendulum control by robust model matching Applied research has been applied. Based on such background, we extend the original state variables (yaw rate, transverse slip angle, and steering angle) of an optimal observer and add the disturbance model which models the frequency spectrum of the disturbance as a state variable, and estimate the disturbance value simultaneously with the estimation of the original state quantity. In this paper, we propose a new method of control law, which is based on the estimation of the disturbance value and to obtain the desired response by state feedback. This paper reports the effect of robust steering control law by combination of model matching and optimal observer capable of realizing robustness to input disturbance while realizing steering desired response by proposed steering control system. In the case of the robust control law for an automobile, it is assumed that the disturbance estimation by the optimal observer is suitable to suppress the whole disturbance energy. In this paper, we show the robust control effect of the steering control law on the real vehicle motion in the case where an optimal observer and an H∞filter are observers.

As a feedback control methodology exclusively targeting asynchronous sequential machines (ASMs), corrective control has been rapidly developing for the past two decades. This paper presents a comprehensive survey on the theory and application of dynamic corrective control in which the controller also has the form of an ASM. First, basic notions and principles of dynamic corrective control, including models of ASMs and configurations of closed-loop systems, are reviewed. Next, assorted dynamic corrective control schemes are presented aiming at solving specific control problems of ASMs, such as model matching and fault-tolerant control. Variations of control aspects are classified according to modeling formalisms of controlled ASMs—input/state, input/output, and composite ASMs—and involved fault characteristics, such as transient, permanent, and intermittent faults and intelligent attacks. Representative results on the application of fault-tolerant corrective control to real-world engineering systems are also provided with an emphasis on space-borne digital systems. Finally, some challenging topics for future studies on corrective control are discussed.

In order to solve the problem of uncomfortable wearing shoes caused by foot type or shoe type of consumers, a virtual shoe test system is developed based on computer graphics knowledge. If the online shoe test has problems, it can provide personal customization for consumers. Firstly, the shoe database is constructed to store shoe last model and key parts data for data comparison and fast data query. Secondly, the method based on OBB and AABB bounding box and plane fitting is proposed. Based on the foot bottom surface, combined with the foot shape law and foot skeleton composition of Chinese people, the automatic extraction of key data and the automatic matching of foot and shoe last are realized. Finally, the OpenGL function is realized in the MFC program, and the 3D visualization of shoes and boots is realized. The mouse control function is added to the system, which improves the human-computer interaction experience.

The primary aim of this article is to provide a comprehensive review and elaboration of model matching and its theoretical propositions. Model matching explains and predicts individuals’ outcomes related to gameplay by focusing on the interrelationships among games’ systems of mechanics, relevant situations external to the game, and players’ mental models. Formalizing model matching theory in this way provides researchers a unified explanation for game-based learning, game performance, and related gameplay outcomes while also providing a theory-based direction for advancing the study of games more broadly. The propositions explicated in this article are intended to serve as the primary tenets of model matching theory. Considerations for how these propositions may be tested in future games studies research are discussed.

This paper provides a complete characterization of solvability of the problem of structural model matching by output feedback in linear impulsive systems with nonuniformly spaced state jumps. Namely, given a linear impulsive plant and a linear impulsive model, both subject to sequences of state jumps which are assumed to be simultaneous and measurable, the problem consists in finding a linear impulsive compensator that achieves exact matching between the respective forced responses of the linear impulsive plant and of the linear impulsive model, by means of a dynamic feedback of the plant output, for all the admissible input functions and for all the admissible sequences of jump times. The solution of the stated problem is achieved by reducing it to an equivalent problem of structural disturbance decoupling by dynamic feedforward. Indeed, this latter problem is formulated for the so-called extended linear impulsive system, which consists of a suitable connection between the given plant and a modified model. A necessary and sufficient condition for the solution of the structural disturbance decoupling problem is first shown. The proof of sufficiency is constructive, since it is based on the synthesis of the compensator that solves the problem. The proof of necessity is based on the definition and the geometric properties of the unobservable subspace of a linear impulsive system subject to unequally spaced state jumps. Finally, the equivalence between the two structural problems is formally established and proven.

This paper proposes coordinated corrective control of input/state asynchronous sequential machines (ASMs). The states of the considered ASM are physically or logically separated to which complete observation by a single controller is impossible. A number of local dynamic controllers that have access to local states cooperate to match the stable-state behaviour of the closed-loop system to that of a reference model. We investigate reachability description of ASMs in the configuration of coordinated control and present the existence condition and design procedure for local controllers that solve the model matching problem. An illustrative example is provided to validate the proposed coordinated corrective control scheme. [ABSTRACT FROM AUTHOR]

In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems. There are a number of different approaches to model matching, with most of them focusing on different goals, i.e., the accuracy of the matching process, or the generality of the algorithm. Moreover, there exist algorithms that use the textual representations of the models during the matching process. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. The search process was conducted based on a well-defined methodology. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. We present the state-of-the-art of model matching, highlighting the differences between different matching techniques, mainly focusing on text-based and graph-based algorithms. Finally, the main open questions, challenges, and possible future directions in the field of model matching are discussed, also including topics like benchmarking, performance and scalability, and conflict handling. [ABSTRACT FROM AUTHOR]

This paper investigates the static correction problem of asynchronous sequential machines (ASMs) via semi-tensor product (STP) of matrices. For an input/state ASM, the static correction problem is to find a static state feedback controller to solve model matching. This controller contains no memory units and consists of only some logic gates. First, by STP method, two algebraic forms are derived to describe the dynamics of an input/state ASM and the function of a static state feedback controller, respectively. Then, as two special cases, the static state feedback controllers for no mismatch and only one mismatch are given. Based on these two cases and analyzing reachability of an ASM, a static state feedback controller design for model matching is presented. Moreover, the number of working points this controller contains is the least. Finally, the proposed method is applied to a simple home security system. [ABSTRACT FROM AUTHOR]

This paper investigates model matching problem of switched asynchronous sequential machines (ASMs) via semi-tensor product (STP) of matrices. A switched ASM is composed of several input/state ASMs and it cut-overs between submachines in asynchronous mechanism. By using STP method, a switched ASM is depicted as a discrete-time equation form. Under this framework, stable transition matrix and admissible model-input-state transition matrix are defined. Then, two important problems, reachability and optimal control input design of switched ASMs, are discussed by these two new matrices, respectively. Finally, a necessary and sufficient condition for the existence of a controller for model matching is obtained and a practical example on asynchronous error accumulators is provided to show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Switched asynchronous sequential machines (ASMs) are endowed with inherent fault‐tolerance capability. This study presents model matching and fault‐tolerant control of switched ASMs based on corrective control theory. The objective is to design a corrective controller that matches the stable‐state behaviour of the closed‐loop system to that of a reference model while overcoming all transient faults infiltrating into the machine. In particular, the proposed control scheme can invalidate not only transient faults causing unauthorised state transitions in single submachine, but also those ones invoking unwanted switching operations between submachines. The existence condition and design procedure for a fault‐tolerant corrective controller is addressed in the framework of corrective control. For experimental verification, the proposed scheme is applied to the read‐only memory controller for on‐board computers implemented on a field programmable gate array system. [ABSTRACT FROM AUTHOR]

• LTI model of an SMA actuator has been worked out for vibration control. • First-order lag system has been chosen as the LTI model from experimental results. • Model matching, PI and H-infinity control have been combined methodically. • The extent of model match has been measured through step and frequency responses. • The results suggest that the creation of LTI modelled SMA actuators is viable. Wires of shape memory alloys (SMAs) contract in response to heat, and are used as actuators because of this ability. However, as this deformation is accompanied by hysteresis, SMA actuators cannot be described as a linear time invariant (LTI) system. In this article, we demonstrate a method for modelling SMA wire actuators that is valid in the full displacement range, to be applied in linear control systems. To this end, we describe the response of the SMA actuators as a first-order lag system, using a model matching method. The time constant used in this first-order lag system is selected based on observations of the limited range below the austenite onset temperature. Analysis of the step response of the SMA actuator established that a PI controller alone is unable to approximate this first-order lag system properly. Hence, with our technique, a PI-controlled SMA actuator is considered as a plant, to which we apply H-infinity control design, which improves the approximation to the first-order lag system. This improvement was verified through a comparison of the transfer functions of the H-infinity-based system and a solely PI-controlled system. We also consider self-sensing by measuring the resistance of the SMA actuator directly, instead of using a load cell to observe the force induced by actuator deformation. Based on these results, we are able to demonstrate that the effect of self-sensing on the closed-loop characteristic is within the permissible range, suggesting that the creation of compact self-sensing actuators is viable. [ABSTRACT FROM AUTHOR]

Featured Application: The control scheme proposed in this work can be applied to several dynamics systems subjected to parameter uncertainties and external disturbances such as vehicle systems and robotic systems. This paper proposes a systematic feedback controller design methodology for multi-input multi-output (MIMO) uncertain systems using the quantitative feedback theory (QFT). To achieve this goal, the model matching problem was considered and the inversion feedforward controller was designed to improve control performance while reducing the demand on feedback control alone. The proposed method is formulated based on the concept of equivalent disturbance attenuation (EDA) approach in which the uncertain system problem is converted into an external disturbance rejection problem based on a nominal system. This proposed approach exhibiting non-sequential design method result in the suboptimal solution showing design simplicity and computational efficiency compared to the existing method. In order to validate the effectiveness of the proposed control methodology, the MIMO magnetic levitation system as adopted and control performances such as time response were presented in both time and frequency domains. [ABSTRACT FROM AUTHOR]

This paper presents a model matching framework for design of haptic controllers. The controller design problem is formulated as a sequence of ${H_{\infty }}$ optimization problems in order to achieve maximal transparency bandwidth. This enables automatic synthesis of robust controllers to achieve a desired bandwidth while limiting actuator forces and guaranteeing stability. This method is applied to address the problem of accurate rendering of high-stiffness virtual walls. Simulation of virtual walls of various stiffnesses are tested on a PHANToM Premium 1.5, at a sampling rate of 1 KHz. The sampled nature of the interface, sensor quantization and Coulomb friction impose limits on the maximum stiffness that can be stably achieved with the commonly used open loop impedance controller. Results presented here demonstrate that the proposed controllers can be used to accurately render virtual walls with stiffness up to 5000 N/m in an oscillation free manner. This stiffness value is well beyond stability limits for the PHANToM under open loop impedance control. Relationships between the weighting functions and the transparency bandwidth are established to outline a procedure for selection and tuning of the weighting functions. Specifically, it is shown that through appropriate choice of weighting functions high-frequency oscillations observed during rendering of stiff surfaces can be eliminated. The passivity of the designed controllers over the frequency range of operation is verified. [ABSTRACT FROM AUTHOR]

This study investigates the use of unmanned aerial vehicles (UAVs) for track gauge measurement, as an alternative to traditional methods that rely on specialized equipment mounted on rail inspection vehicles or trains. Such equipment not only consumes valuable rail resources but also poses safety risks to inspectors. The proposed method involves utilizing image data collected by a UAV to generate a point cloud of the railway scene. A hybrid segmentation algorithm based on augmented Region-Growing and improved alpha-shape techniques is then employed to extract rails from chaotic scene data. To determine the track gauge, a pre-built rail Building Information Modeling (BIM) model is discretized into a point cloud template, which is then matched with the extracted rail features using a template-oriented model matching approach. Validation experiments on four groups of data demonstrate the superiority of this method in terms of safety, efficiency, speed, and accuracy. • This study proposes a novel non-contact method for measuring railway gauge based on three-dimensional model matching. • A hybrid segmentation algorithm is developed. • A template-based model matching approach is suggested. • The proposed method offers a simple, low-cost, and applicable solution for track gauge measurement. [ABSTRACT FROM AUTHOR]

Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. However, 3D editing techniques fall short of maintaining the semantic information across edit operations if they are not tailored to a specific data model. While semantic information is often lost during edit operations, geometry, UV mappings, and materials are usually maintained. This article presents a data model synchronization method that preserves semantic information across editing operation relying only on geometry, UV mappings, and materials. This enables easy integration of existing and future 3D editing techniques with rich data models. The method links the original data model to the edited geometry using point set registration, recovering the existing information based on spatial and UV search methods, and automatically labels the newly created geometry. An implementation of a Level of Detail 3 (LoD3) building editor for the Virtual Singapore project, based on interactive push-pull and procedural generation of façades, verified the method with 30 common editing tasks. The implementation synchronized changes in the 3D geometry with a CityGML data model and was applied to more than 100 test buildings.