State Key Laboratory of Synthetical Automation for Process Industries

Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail. For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized. The relationship among stability results in different forms, such as algebraic inequality forms, M-matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared. Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed. Concluding remarks and future directions of stability analysis of recurrent neural networks are given.

This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. Under the data update policy with bounded update interval, a new control scheme that discards the outdated information is proposed, and the stability analysis of CPSs under DoS attacks is transformed into analyzing the stability of the system under a switched controller with the help of a class of linear matrix inequalities (LMIs). Then, inspired by the techniques for switched systems, sufficient conditions on the duration and frequency of the DoS attacks, under which the stability of the closed-loop systems is still guaranteed, are proposed. Compared with the existing method for the single-channel case, the considered multiple-channel case is more challenging, and the proposed LMI-based method is more flexible.

In this paper, for linear leader-follower networks with multiple heterogeneous actuator faults, including partial loss of effectiveness fault and actuator bias fault, a cooperative fault-tolerant control (CFTC) approach is developed. Assume that the interaction network topology among all nodes is a switching directed graph. To address the difficulty of designing the distributed compensation control laws under the time-varying asymmetrical network structure, a novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures. First, by constructing a group of distributed reference observers based on neighborhood state information, all followers can estimate the leader's state trajectories directly. Second, a decentralized adaptive fault-tolerant tracking controller via local estimation is designed to achieve the global synchronization. Furthermore, the reliable coordination problem under switching directed topology with intermittent communications is solved by utilizing the presented CFTC approach. Finally, the effectiveness of the proposed coordination control protocol is illustrated by its applications to a networked aircraft system.

This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm.

In this paper, the event-based control problems for nonlinear stochastic systems are investigated. First, a novel condition for stochastic input-to-state stability is established. Then, the dynamic event-triggered control approach is proposed and the stochastic stability of the resulting closed-loop system is also proved. Next, a new dynamic self-triggering mechanism is developed and the additional internal dynamic variable is designed according to the predicted value of the system state and error, which ensures that the closed-loop system is stochastically stable. It is shown that the lower bounds of interexecution times by the proposed dynamic event-triggered and self-triggered control approaches are all larger than zero, and the so-called Zeno phenomenon is avoided. Compared with the static event-triggering and self-triggering results, the interexecution times by the proposed dynamic approaches are prolonged on the whole. Two simulation examples are provided to show the efficiency of the proposed approaches.

In this article, the problem of adaptive backstepping finite-time tracking control is investigated for a class of strict-feedback nonlinear systems with unmodeled dynamics and dynamic disturbances. A modified finite-time dynamic signal is first introduced to dominate the dynamic disturbance. By using the adaptive control, backstepping technique, and finite-time stability theory, an adaptive finite-time tracking controller is developed. Under the proposed control scheme, the finite-time tracking performance and the boundedness property of all signals in the closed-loop system are ensured. Finally, simulation results check the effectiveness of the suggested approach.

This article proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large-scale multiobjective and many-objective optimization problems (MaOPs). Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large-scale multiobjective and MaOPs.

This note investigates the problem of output feedback H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for linear discrete-time systems. Three types of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controllers are considered, namely, static output feedback controllers, dynamic output feedback controllers, and observer-based output feedback controllers. New design conditions for the three type of output feedback controllers are introduced in terms of unified linear matrix inequality (LMI) representations, which guarantee the prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performances of the closed-loop systems. In contrast to the existing LMI conditions for designing the output feedback H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controllers, the improvement of the proposed results over the existing ones is shown by theoretical proof and numerical example.

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer’s perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method.

This paper is concerned with the problem of nonfragile H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering for continuous-time Takagi-Sugeno (T-S) fuzzy systems. The filter to be designed is assumed to have two types of multiplicative gain variations. First, two relaxed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering analysis conditions are proposed based on useful linear matrix inequality preliminaries. Whereafter, the results are exploited to derive sufficient conditions for designing a nonfragile H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter, which guarantees a prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of the filtering error system. Compared with the existing results, the proposed design methods not only suit for a standard form of the fuzzy filter but also give more relaxed design conditions. Finally, simulation examples will be given to show the efficiency of the proposed design methods.

This paper is concerned with a reinforcement learning-based adaptive tracking control technique to tolerate faults for a class of unknown multiple-input multiple-output nonlinear discrete-time systems with less learning parameters. Not only abrupt faults are considered, but also incipient faults are taken into account. Based on the approximation ability of neural networks, action network and critic network are proposed to approximate the optimal signal and to generate the novel cost function, respectively. The remarkable feature of the proposed method is that it can reduce the cost in the procedure of tolerating fault and can decrease the number of learning parameters and thus reduce the computational burden. Stability analysis is given to ensure the uniform boundedness of adaptive control signals and tracking errors. Finally, three simulations are used to show the effectiveness of the present strategy.

This paper is concerned with reliable state feedback control synthesis for Takagi and Sugeno (T-S) fuzzy systems with sensor multiplicative faults. By considering the influence of sensor faults on both the system states and premise variables of fuzzy controllers, a class of new convex reliable stabilization conditions are proposed for T-S fuzzy systems using the properties of fuzzy product inference engines. Furthermore, the obtained result is extended to the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> reliable control case. The resulting controllers are reliable in that they provide guaranteed asymptotic stability and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance when all sensors are operational, as well as when some sensor experiences failures. Different from the proposed approach, the influence of sensor faults on premise variables is not considered in the existing results; then, it might not guarantee the stability and control performance for T-S fuzzy systems with premise variables dependent on the system states. A numerical example is given to illustrate the effectiveness of the proposed method.

This paper presents a novel adaptive fuzzy backstepping dynamic surface control (DSC) scheme for a class of single-input single-output strict-feedback fractional-order uncertain nonlinear systems. The controlled systems contain unknown nonlinear functions and unknown external disturbances. Fuzzy logic systems are employed for approximating the unknown nonlinear functions. Further, an auxiliary function is introduced into the control function to simultaneously compensate the unknown external disturbance and the approximation error caused by fuzzy approximation, which erases the possible chattering phenomenon in the existing results. Meanwhile, a new DSC method based on the fractional-order filter is proposed to avoid the issue of explosion of complexity inherent in the backstepping procedure, which releases the limitation that the fractional-order derivative of the intermediate control function needs to be completly known in the existing references. Under certain assumptions, the stability of the closed-loop system is proved by using the fractional-order Lyapunov function stability criterion. Finally, contrastive simulation results are provided to validate the effectiveness of our proposed control strategy.

This paper is concerned with the problem of robust fault estimation and fault-tolerant control for a class of Takagi-Sugeno (T-S) fuzzy systems with time-varying state delay and actuator faults. Based on the ( k-1)th fault estimation information, a novel k-step fault-estimation observer is proposed to construct the kth fault error dynamics. The obtained fault estimates via k-step fault-estimation can practically better depict the size and shape of the faults. Then, based on the information of online k -step fault-estimation, a dynamic output feedback fault tolerant controller is designed to compensate the fault effects on the closed-loop fuzzy system. Furthermore, some less conservative delay dependent sufficient conditions for the existence of fault estimation observers and fault tolerant controllers are given in terms of solution to a set of linear matrix inequalities. Finally, simulation results of two numerical examples are presented to show the effectiveness and merits of the proposed methods.

This article studies the robust trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV). In order to guarantee the desired trajectory tracking performance in the presence of external disturbances and model uncertainties, the design process of the quadrotor UAV controller is divided into two steps. First, by decomposing the attitude dynamic system into two serial-connected subsystems, a cascade active disturbance rejection control scheme is applied to the attitude subsystem. Second, by introducing an additional high-gain design parameter, a novel backstepping sliding-mode control scheme for position subsystem is constructed. Moreover, the Lyapunov stability analysis is provided to show that the trajectory tracking error can converge to an arbitrarily small residual set. Numerical results illustrate the effectiveness of the designed control method and its robustness to the external disturbances and model uncertainties. Finally, the proposed method is implemented on a quadrotor UAV to demonstrate its feasibility in practical application.

Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

The limited fault information caused by small fault data samples is a major problem in wind turbine (WT) fault detection. This paper proposes a small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs). First, based on prior knowledge, a rough fault data generation process is developed to transform the normal data to the rough fault data. Second, a rough fault data refiner is developed by GANs to make the rough fault data more similar with the real fault data. Moreover, to make the generated data better suited to the WT conditions, GANs are improved in both the generative model and the discriminative model. Third, artificial intelligence (AI)-based WT fault detection models can be well trained by using only the generated data in the condition of small fault data sample. Finally, three groups of generated data evaluation experiments and four groups of WT fault detection comparative experiments are conducted using real WT data collected from a wind farm in northern China. The results indicate that the method proposed in this paper is effective.

This article addresses an adaptive fuzzy finite-time control for a class of uncertain strict-feedback nonlinear systems with backlashlike hysteresis and stochastic disturbances. At first, a novel criterion of semiglobally finite-time stability in probability (SGFSP) is established based on Lyapunov function method. Under the proposed stability criterion, an adaptive fuzzy finite-time control scheme is designed. In the design process of the controller, command filter technique is introduced to overcome the problems of “explosion of complexity” and “singularity” inhered in the traditional adaptive finite-time control based on the backstepping method. Meanwhile, via constructing the corresponding error compensating systems, the effect of errors generated by the command filters is reduced, such that the original systems have more better tracking performance. To cope with the influence of backlashlike hysteresis input, an auxiliary system is constructed, in which the output signal is applied to compensate the effect of the hysteresis. It is shown that the tracking error can converge to a small neighborhood of original in finite time, and the closed-loop system is SGFSP under the constructed controller. Finally, the effectiveness of the proposed control strategy is further verified by two simulation examples.

Distributed algorithms are increasingly being used to solve the economic dispatch problem of integrated energy systems (IESs) because of their high flexibility and strong robustness, but those algorithms also bring more risk of cyber-attacks in IESs. To solve this problem, this article investigates the distributed robust economic dispatch problem of IESs under cyber-attacks. First, as the first line of defense against attacks, a privacy-preserving protocol is designed for covering up some vital information used for economic dispatch of IESs. On this basis, a distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process. The proposed strategy is implemented in a fully distributed fashion and possesses strong robustness against various colluding and noncolluding attacks. In addition, the strategy can not only ensure the reliability of information transmission among energy units, but also solve the problem of incorrect measurement of distributed local load data caused by cyber-attacks. Finally, the effectiveness of the proposed strategy is illustrated by simulation cases on a 39-bus 32-node power–heat IES.