1,256 publications from this institution
Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric learning, MTS pieces building, and MTS classification is presented. After that, we conduct experiments on industrial benchmark of Tennessee Eastman (TE) process. The experimental results demonstrate the improved performance of the proposed algorithm when compared with other classical PM-FD classical methods.
This paper considers the problem of local capacity H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for a class of production networks of autonomous work systems with time-varying delays in the capacity changes. The system under consideration is modelled in a discrete-time singular form. Attention is focused on the design of a controller gain for the local capacity adjustments which maintains the work in progress (WIP) in each work system in the vicinity of planned levels and guarantees the asymptotic stability of the system and reduces the effect of the disturbance input on the controlled output to a prescribed level. In terms of a matrix inequality, a sufficient condition for the solvability of this problem is presented using an appropriate Lyapunov function, which depends on the size of the delay and is solved by existing convex optimization techniques. When this matrix inequality is feasible, the controller gain can be found by using LMI Toolbox Matlab. Finally, numerical results are provided to demonstrate the proposed approach.
Neural networks-based reinforcement learning control (NRLC) of autonomous systems is an active field due to its theoretical challenges and crucial applications. Note that there exist numerous difficulties in enhancing the intelligence and reliability of autonomous systems since autonomous and reliable techniques of guidance, navigation and control functionals are extremely involved in face of sophisticated and hazardous environments. In this context, high-intelligence reliable control technologies, especially based on neural networks tools, of autonomous systems are persistently pursued in trajectory tracking, path following, waypoints guidance, cooperative formation, etc. In addition, massive nonlinearities, sensor fault diagnosis, actuator failures tolerance, environment abnormalities, civil requirements and national security issues have led to strong demands for the NRLC technologies in autonomous systems. Reinforcement learning, inspired by learning mechanisms observed in mammals, is concerned with how agent and actor ought to take actions to optimize a cost of its long-term interactions with the environment, and is gradually becoming the focus of learning control for autonomous systems. The autonomous systems inevitably suffer from actuator faults, component failures, insecurity factors, complex uncertainties, such that neural networks induced intelligence in autonomous control, fault tolerant control, network communication and signal progressing becomes dramatically significant. To be specific, by combining with neural networks and reinforcement learning, advances in the NRLC technologies of autonomous systems are exclusively pursued in this special issue.
The potential of multiantenna interference cancellation receiver algorithms for increasing the uplink throughput in WLAN systems such as 802.11 is investigated. The medium access control (MAC) in such systems is based on carrier sensing multiple-access with collision avoidance (CSMA/CA), which itself is a powerful tool for the mitigation of intrasystem interference. However, due to the spatial dependence of received signal strengths, it is possible for the collision avoidance mechanism to fail, resulting in packet collisions at the receiver and a reduction in system throughput. The CSMA/CA MAC protocol can be complemented in such scenarios by interference cancellation (IC) algorithms at the physical (PHY) layer. The corresponding gains in throughput are a result of the complex interplay between the PHY and MAC layers. It is shown that semiblind interference cancellation techniques are essential for mitigating the impact of interference bursts, in particular since these are typically asynchronous with respect to the desired signal burst. Semiblind IC algorithms based on second- and higher-order statistics are compared to the conventional no-IC and training-based IC techniques in an open access network (OAN) scenario involving home and visiting users. It is found that the semiblind IC algorithms significantly outperform the other techniques due to the bursty and asynchronous nature of the interference caused by the MAC interference avoidance scheme.
In this paper we present a model in which the time and length are considered quantized. We try to explain the internal structure of the elementary particles in a new way. In this model a super-dimension is defined to separate the beginning and the end of each time and length quanta from another time and length quanta. The beginning and the end of the dimension of the elementary particles are located in this super-dimension. This model can describe the basic concepts of inertial mass and internal energy of the Elementary particles in a better way. By applying this model, some basic calculations mentioned below, can be done in a new way: 1- The charge of elementary particles such as electrons and protons can be calculated theoretically. This quantity has been measured experimentally up to now. 2- By using the equation of the particle charge obtained in this model, the energy of the different layers of atoms such as hydrogen and helium is calculated. This approach is simpler than using Schrodinger equation. 3- Calculation of maximum speed of particles such as electrons and positrons in the accelerators is given.
This article proposes a resource-aware triggering-based dynamic controller codesign scheme for Markov jump systems (MJSs) to fulfill both disturbance rejection and computational resources saving goals. Specifically, a self-trigger strategy is presented for precomputing the next execution time for state measurement, executing the controller, and updating the actuators using the current sampled information. To achieve a desired system performance and reduce the resource occupation, the self-triggering parameters and the gains of state feedback controller are codesigned. Moreover, a dynamic triggering technique is employed to improve the system performance by adapting the trigger coefficients to the different subsystems of MJSs. The effectiveness of the proposed method is demonstrated via two examples.
The problems of stability analysis and control synthesis for a class of neural systems with time-varying delays and nonlinear uncertainties are addressed. The dynamical system under consideration consists of different time-varying neutral and discrete delays without any restriction on upper bounds of derivatives of both delays. Based on the Lyapunov–Krasovskii functional theory, delay-dependent sufficient linear matrix inequalities (LMIs) conditions are established for the stability and stabilization of the considered system using some free matrices and the Leibniz–Newton formula. Control synthesis is to design a delayed state-feedback scheme based on a convex optimization method such that the resulting closed-loop system is asymptotically stable and satisfies a prescribed level of H ∞ performance. The simulation results illustrate the effectiveness of the proposed methodology.
This paper deals with output ℌ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control synthesis for a linear parameter-varying model of a floating wind turbine. A nonlinear model is introduced and linearized to obtain a linear model for each desired azimuth angle using the wind turbine software FAST. The main contributions of this paper are threefold. Firstly, the family of linear models are represented based on an affine parameter-varying model structure. Secondly, the bounded parameter-varying parameters are removed using upper bounded inequalities in the control design process. Thirdly, the control problem is formulated in terms of linear matrix inequalities (LMIs). The simulation results show a comparison between a controller design based on a constant linear model and a controller based on the linear parameter-varying model. The results show the effectiveness of our proposed design technique.
In this work, the cruise control problem of high-speed trains’ movements is investigated. Both cases of a single high-speed train and multiple high-speed trains are under consideration. Different with most existing studies where the centralized control or the decentralized control methods are adopted based on a single point mass model of the train, in this paper, a distributed control mechanism is proposed by virtue of the graph theory, and the high-speed train’s model is built as a cascade of point masses connected by flexible couplers. For a single high-speed train, the neighboring cars interact through the coupling force with each other, which can be described by a connected topological graph by regarding each car as a node. Besides, the speed information communication among the cars is considered to be described by another directed topological graph. A distributed control strategy is then developed, with which all the cars of a train track a desired speed asymptotically and the neighboring cars keep a safety distance from each other. For the multiple high-speed trains running on a railway line, the in-train force interaction topology and the speed information communication topology of all the trains are more complex than those of a single train. A new cluster consensus technique is developed, by which a distributed control law is designed. Under the control law, the trains can track the desired speeds asymptotically, the headway distance between adjacent trains and the distance between the neighboring cars of a train can be kept in appropriate ranges. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.
This article presents a self-triggered bipartite fault-tolerant consensus control scheme in a fixed-time stability sense for multiagent systems (MASs) with function constraints on states. Neural networks are introduced to identify unknown nonlinearities, thereby relaxing constraints on unknown functions. Then, based on time-varying barrier Lyapunov functions including time and previous states, all states of the considered MASs are constrained to given ranges. In addition, to improve the utilization of system transmission resources, a self-triggered control method in which the next triggering time can be computed according to the current information of the controller is proposed. By applying the Lyapunov theory, the designed self-triggered controller can ensure that all signals containing consensus tracking errors are fixed-time bounded under a given communication topology, and the Zeno phenomenon is successfully avoided. Finally, a practical example is provided to testify the feasibility of the developed control scheme.
Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities primarily utilized newspapers and later radio and television to inform the public, the political arena has transformed into a more complex structure with the rise of the Internet and online social media. Now, more than ever, people express themselves online while mainstream news agencies attempt to utilize the power of the Internet to spread their articles as much as possible. To grasp the political coexistence of mainstream media and online social media, in this paper, we analyze these two sources of information in the context of the U.S. 2020 presidential election. In particular, we collected data during the 2020 Democratic Party presidential primaries pertaining to the candidates, and, by analyzing this data, we highlight similarities and differences between these two main types of sources, detect the potential impact they have on each other, and understand how this impact relationship can change over time.
In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.
This paper deals with the robust delay-dependent H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⋡</inf> filtering problem for a class of LPV systems with multiple constant time-delays in the state vector. It is shown that, by using the Hamiltonian-Jacoby-Isaac (HJI) function and the polynomially parameter-dependent quadratic functions and a suitable change of variables, the required sufficient conditions with high precision are established in terms of parameter-independent linear matrix inequalities (LMIs) for the existence of the desired H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⋡</inf> filters. However, the explicit expression of the robust delay-dependent H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⋡</inf> filters is derived to satisfy both asymptotic stability and H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⋡</inf> performance. A numerical example is provided to demonstrate the validity of the proposed design approach.
In this paper, the problem of passivity-based sliding mode control (SMC) for uncertain delayed stochastic systems (DSS) within state-observer framework is under consideration. A novel linear sliding surface is first presented with the aid of the provided observer and output information, and a new sufficient condition on the passivity and mean square exponential stability of the underlying closed-loop system during the sliding mode is then derived by employing the stochastic stability theory and linear matrix inequality approach. Further, an associated SMC law is designed to guarantee the arrival of the sliding surface almost surely via the adaptive methodology. Finally, two illustrative examples concerning the desirable performance of the system are conducted to confirm the effectiveness and superiority of the proposed method.
In the operation of practical systems, it is often a challenging task to deal with limited knowledge of the system dynamics, therefore, it is needed to obtain a framework for the simultaneous learning and control of dynamic systems, able to cope with uncertain dynamics. This paper proposes a model-free aperiodic tracking control method based on MAXQ hierarchical reinforcement learning for unknown dynamics in discrete-time systems. Firstly, a MAXQ hierarchical framework is developed for aperiodic tracking control that decomposes the task into pre-control and accurate control subtasks. The former achieves sub-optimal tracking which prompts the implementation of accurate control. The latter implements aperiodic tracking based on the pre-control result to reduce resource occupation. The structure of the MAXQ framework simplifies the complexity of the tracking task, and the incorporation of pre-control accelerates the learning process in the formal control stage. Secondly, considering the disparities between the control inputs and the control update instants, pre-control is decomposed to learn the triggering strategy and control policy, respectively. Meanwhile, the control policy learns optimal control inputs by minimizing the cost function. Similarly, the triggering strategy and control policy are also separately learned in accurate control. In contrast to traditional aperiodic triggering mechanisms, in accurate control stage, the triggering strategy is developed based on the cumulative error of the pre-control result, thus avoiding the influence of accidental factors and the cumulative effects of errors, enhancing system robustness. Thirdly, the proposed tracking control is derived by using only the input, output, and reference signal data from the system, without relying on system dynamics. It is applicable to both linear and nonlinear systems, demonstrating strong generalizability. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed method.
It is well known that cyber-physical systems (CPSs) commonly exist in both industrial manufacturing and people’s daily lives. As a hot topic within Industry 4.0, CPSs have attracted interest from both academia and industry. Typical examples of CPSs include autonomous vehicles, smart grid, process control systems, and industrial robotics systems. The traditional techniques are mainly focused on either the physical object or the abstract data model individually. A simultaneous consideration of both domains is needed.
This paper considers the problem of robust control for a class of uncertain state-delayed singularly perturbed systems with norm-bounded nonlinear uncertainties. The system under consideration involves state time delay and norm-bounded nonlinear uncertainties in the slow state variable. It is shown that the state feedback gain matrices can be determined to guarantee the stability of the closed-loop system for all ε ∈ (0, ∞) and independently of the time delay. Based on this key result and some standard Riccati inequality approaches for robust control of singularly perturbed systems, a constructive design procedure is developed. We present an illustrative example to demonstrate the applicability of the proposed design approach.
Object Oriented Programming (OOP) is a programming paradigm which be highlighted using the 800xa system. This work will investigate the use of OOP programming for designing industrial application control based on a soft controller. The different types of objects which can be defined in different classes, declaration and implementation using the 800xa will be explored, thus highlighting the use of static, dynamic and embedded objects. In the next phase, an illustration on how to map and use these objects in ABB 800xA for efficient control, hence, providing a systematic approach for accessing any of the plant aspects using a unique plant object.
