This article considers the problem of output feedback-based decentralized periodic event-triggered control (PETC) for a class of large-scale systems with unknown nonlinear interconnections and measurement delays. When only the delayed sampled-data measurement output is accessible, a novel decentralized high-gain observer is first proposed based on an output predictor for each subsystem. Then, a set of decentralized sampled-data output feedback controllers that are driven by asynchronous periodic event-triggering conditions is developed to globally exponentially stabilize the large-scale systems. With the help of small-gain arguments and feedback domination approach, a rigorous stability analysis shows that there exist some sufficient conditions to ensure the global exponential stability of the overall systems. Different from the sample-and-hold implementation of output information, this article employs the prediction technique to obtain the current output prediction for each subsystem, which in turn effectively compensates for the undesirable effects of measurement delays and information loss. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control method.

The article investigates the speed regulation of permanent magnet synchronous motor (PMSM) systems. Existing control methods of the non-cascade structure suffer from the drawbacks of unsatisfactory anti-disturbance performance and slow convergence rate when the system is affected by disturbances, especially unmatched disturbances. Meanwhile, the requirements of current constraint and fast dynamics cannot be effectively balanced in the single-loop structure of speed and current using traditional control methods such as the PID controller. Because large transient currents induced by fast dynamics may damage the hardware of the system. Therefore, a current-constrained finite-time control approach is proposed. Specifically, a robust finite-time control scheme is developed with the assistance of the improved finite-time observer technique. The proposed method is capable of actively suppressing both matched and unmatched disturbances in non-cascade control systems. Simultaneously, an effective penalty mechanism is established to incorporate a specific gain function into the designed controller. This approach restricts the q-axis current to a predefined safe range without solving an optimization problem. Finally, comparative experiment results indicate that the newly proposed finite-time control method outperforms the baseline control methods in terms of disturbance rejection, convergence rate, and current constraint.

In overhead cranes, the inclination angle of the payload must be limited within an acceptable range to ensure safety, and the trolley reaches a desired position simultaneously. However, the payload can be disturbed by strong winds, which poses a certain threat to the safety of overhead crane operation. In addition, when multiple overhead cranes communicate with each other via a shared network, the communication and computational resources available to each overhead crane become constrained. Taking into account the above factors, a novel event-triggered safety-critical model predictive control (ESMPC) algorithm is proposed for underactuated overhead crane systems to achieve satisfactory performance. In the proposed ESMPC algorithm, disturbances acting on the payload are estimated by a discrete-time disturbance observer. Then, the discrete-time predictive control barrier function is devised to guarantee operational safety. Subsequently, the prediction model is derived and the quadratic programming (QP) problem is formulated. The optimal control sequence can be obtained by solving the QP problem at each event-triggering instant. After that, the predicted control inputs are fully exploited and applied to the trolley one by one chronologically. Finally, the experimental results show that the payload swing angle can be limited within the safe range and the trolley can reach the desired position by using fewer communication and computing resources under the proposed ESMPC method.

In this article, an online reinforcement learning (RL) control method through value iteration (VI) is developed to solve the optimal cooperative control problem for the unknown linear discrete-time multiagent systems (MASs). On the one hand, an online learning scheme with evolving policies is proposed in order to guarantee the stability of the MASs under immature policies generated by VI. Inspired by the event-triggered mechanism, the stability criterion is designed as a trigger to filter the admissible control policies, which eliminates the need to establish a monotonic value function sequence. On the other hand, an acceleration mechanism for the MASs is presented such that the convergence rate of VI can be accelerated. The relationship between the selection of the relaxation factor and the accelerated convergence process is elaborated. Simple backpropagation (BP) neural networks (NNs) are applied for the implementation. Two classical examples are introduced and simulation results are provided in order to substantiate the validity of the designed method.

This article investigates the practical finite-time spatial deployment of a class of large-scale heterogeneous nonlinear multi-agent systems (MASs), for which a novel hybrid analysis methodology based on ordinary differential equations (ODEs) coupled with partial differential equations (PDEs) is proposed. The assumption is made that a portion of the agents is sparsely distributed in space, while the other portion is densely distributed. By designing appropriate network communication protocols (NCPs), the dynamics of MASs are represented by a hybrid model consisting of several ODEs and a PDE. Particularly, the network topological weights are specifically designed as semi-Markov switched to better align with real communication situations of MASs, while complying with inconsistent switching rules. Moreover, for delay-free and time-delayed cases, this article proposes two novel projection-based adaptive neural control schemes and obtains two design criteria of controller gains, such that the practical finite-time stability of the tracking error systems could be guaranteed. Finally, numerical examples are provided to illustrate the effectiveness of the developed approaches.

This article investigates the event-triggered model predictive control (MPC) problem for a class of networked nonlinear uncertain systems subject to time-varying disturbances. Different from the traditional MPC, the proposed periodic event-triggered MPC (PETMPC) method does not generate new control sequence unless a predesigned periodic event-triggering mechanism (PETM) is violated. First, a generalized proportional-integral observer (GPIO) is developed to estimate the unknown state and disturbance information by using the sampled-data output of controlled system. Then, the disturbance predictions for future finite steps are obtained based on forward Euler method. After that, with the help of prediction model, the optimal control sequence, including the future finite step predicted control inputs, is generated and dexterously exploited during the interevent interval by storing it in a buffer installed between the control sequence generator and actuator, thereby leading to the further reduction of signal transmission number and the frequency of control sequence computations. Through a rigorous stability analysis, it can be proved that the closed-loop hybrid control system is globally bounded stable under the nominal PETMPC law. Finally, numerical simulations are conducted to substantiate the feasibility and superiority of the proposed PETMPC method.

Malware proliferation is a major threat to computer systems, and malware classification techniques are effective for analyzing and identifying malware. Recent intelligent malware classifiers intend to integrate natural language processing techniques for better identification performance, however, representation learning presents a fundamental challenge in this new paradigm. Currently, representation models either rely on predetermined structures or ignore structure. Thus, we propose a malware vector representation model utilizing an improved context-separated bi-directional long short-term memory (CS-Bi-LSTM) network with reinforcement learning (RL) to discover optimized structures for learning sentence representations. Our model filters out irrelevant representations and captures long-term dependencies. To generate word vectors, we use the CS-Bi-LSTM network, which employs two unique unidirectional long short-term memory (LSTM) cells for each context. Following that, we develop a proximal policy optimization (PPO)-based RL structure to capture relevant representations with feedback from a quality estimator. Through intrinsic and extrinsic evaluations on two malware datasets, our proposed model demonstrates better performance by filtering redundant information and achieving an acceptable vector representation. Then, our proposed method achieves the highest predictive performance with a classification accuracy of 98.54%.

This article investigates the robust control problem for speed regulation of networked permanent magnet synchronous motor subject to the limited communication bandwidth. To handle this, a new sampled-data disturbance rejection control method is developed via a well-designed discrete-time dynamic event-triggered mechanism (DETM). First, a predictor-based generalized proportional integral observer is introduced to estimate the lumped disturbances, when only the sampled-data output is available. Then, a composite proportional feedback controller is formed by fully utilizing disturbance estimation. The composite controller updates only when the designed discrete-time DETM is violated, resulting in remarkable communication and computation resource savings while maintaining the desirable disturbance rejection ability. The designed DETM can be applied to digital computers easily due to the discrete-time detection. Simulations and experiments are carried out to validate the feasibility and effectiveness of the proposed control scheme.

In this article, we investigate the problem of decentralized event-triggered control for large-scale systems with disturbances and input and output delays. By using the decentralized sampled-data outputs, a set of predictor-based extended state observers is first proposed to estimate the unknown states and disturbances. Thanks to the proposed predictor-based observers, the undesirable influences of both input and output delays on control performance can be effectively attenuated. With the help of disturbance estimation and attenuation technique, a set of robust sampled-data controllers is then proposed based on discrete-time event-triggering conditions, such that the disturbance rejection ability of the large-scale systems can be considerably enhanced while saving the transmission times. The stability analysis is presented to guarantee the stability of the resultant closed-loop control system, and we give the quantitative relationship of control parameters and maximum allowable intersample time interval and input and output delays. Finally, the simulation results of a numerical example are presented to verify the effectiveness of the proposed control method.

Abstract Recent developments in the field of Internet of things (IoT) have aroused growing attention to the security of smart devices. Specifically, there is an increasing number of malicious software (Malware) on IoT systems. Nowadays, researchers have made many efforts concerning supervised machine learning methods to identify malicious attacks. High‐quality labels are of great importance for supervised machine learning, but noises widely exist due to the non‐deterministic production environment. Therefore, learning from noisy labels is significant for machine learning‐enabled Malware identification. In this study, motivated by the symmetric cross entropy with satisfactory noise robustness, the authors propose a robust Malware identification method using temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface function names. Here, considering the numerous unlabelled samples in real‐world intelligent environments, the authors pre‐train the TCN model on an unlabelled set using a word embedding method, that is, Word2Vec. In the experiments, the proposed method is compared with several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real‐world dataset. The performance comparisons demonstrate the better performance and noise robustness of their proposed method, especially that the proposed method can yield the best identification accuracy of 98.75% in real‐world scenarios.

This article investigates the problem of periodic event-triggered output-feedback control for networked control systems in the presence of external disturbance and input and output delays. With the aid of the prediction technique, we first develop the predictor-based-extended state observer to reconstruct the system information, including the unknown state and disturbance. The periodic event-triggered output-feedback control law is then designed via the disturbance/uncertainty estimation and attenuation (DUEA) method, such that the communication times can be remarkably reduced and, at the same time, the disturbance rejection ability can be effectively enhanced. Under the predictor-based event-triggered control method, the influence of the time delays is effectively attenuated, and the effect of external disturbance is considerably attenuated due to the prediction technique and the DUEA method. By using the small-gain arguments, this article gives some sufficient stability conditions for the overall control system, and the explicit computations of sampling/updating period and time delays are presented as well. Finally, we employ a practical example and show some comparative simulation results to demonstrate the advantages of the predictor-based event-triggered control method proposed in this article.

In recent years, joint detection and embedding (JDE) has become the research focus in multi-object tracking (MOT) due to its fast inference speed. JDE models are designed and widely utilized to train the detection task and the re-identification (Re-ID) task jointly. However, there exists a severe issue overlooked by previous JDE models, i.e., the detection task requires category-level features but the Re-ID task requires instance-level features. This could lead to feature conflict, which would hurt the performance of JDE models. Furthermore, inaccurate detection results can degrade the final tracking accuracy even when discriminative Re-ID features are provided. In this article, we propose a new balancing method for training JDE models, which monitors the training process of the detection task and adjusts the weights of the detection task and Re-ID task in the training phase. Our proposed balancing method ensures a well-trained detection model and a good trade-off between the detection task and Re-ID task. Comprehensive experiments on two public MOT benchmarks demonstrate the effectiveness and superiority of our proposed balancing method. In particular, our proposed balancing method could achieve new state-of-the-art results on MOT challenges without additional training data.

In this article, we investigate the problem of sampled-data robust output feedback control for a class of nonlinear uncertain systems with time-varying disturbance and measurement delay based on continuous-discrete observer. An augmented system that includes the nonlinear uncertain system and disturbance model is first found, and by using the delayed sampled-data output, we then propose a novel predictor-based continuous-discrete observer to estimate the unknown state and disturbance information. After that, in order to attenuate the undesirable influences of nonlinear uncertainties and disturbance, a sampled-data robust output feedback controller is developed based on disturbance/uncertainty estimation and attenuation technique. It shows that under the proposed control method, the states of overall hybrid nonlinear system can converge to a bounded region centered at the origin. The main benefit of the proposed control method is that in the presence of measurement delay, the influences of time-varying disturbance and nonlinear uncertainties can be effectively attenuated with the help of feedback domination method and prediction technique. Finally, the effectiveness of the proposed control method is demonstrated via the simulation results of a numerical example and a practical example.

In this article, we consider the disturbance rejection control problem for networked control systems using a subpredictor-based extended state observer. The networked control system is subject to large input and output delays. To handle the time delays, a subpredictor-based extended state observer is proposed to estimate the unknown state and disturbance by dividing the total delay into small pieces. The proposed observer is composed of a set of predictor-based observers connected in series, and each elementary observer is only responsible for compensating the effect of a fraction of the total delay. By using the disturbance compensation technique, we design a sampled-data composite controller to attenuate the influence of external disturbance. The proposed control method can handle the multirate case where the sensor and controller have different sampling and updating rates. It shows that under the proposed control method, the disturbance rejection ability of the control system can be effectively improved in the presence of arbitrary large input and output delays. The simulation results are given to demonstrate the effectiveness of the proposed control method.

In this paper, the output feedback based periodic event-triggered control problem is considered for a class of nonminimum-phase nonlinear systems via dynamic event-triggering mechanism. When only the sampled-data output is known, a new periodic event-triggered control method is proposed via output feedback and the control input updates based on a discrete-time dynamic event-triggering condition. Despite the unstable zero dynamics, the proposed control method can asymptotically stabilize the hybrid control systems by closing the loop only when it is necessary. In contrast to the continuous-time static one, the proposed dynamic triggering condition has several advantages including the easier digital implementation and the larger average inter-event time interval. The delicate analysis gives the explicit expression of the maximum allowable sampling period, and the global asymptotic stability can be achieved for the hybrid control systems. Finally, the effectiveness of the proposed periodic event-triggered control method is verified by a numerical simulation.

This article considers the problem of periodic event-triggered control design for dual-rate networked control systems subject to nonvanishing disturbance. The plant considered in this article is a kind of dual-rate networked control system, where the sensor samples the measurement output at a slow rate and the actuator updates the control input at a fast rate. Despite the slow-rate sampling of the sensor, a new output predictor-based observer is proposed to accurately estimate system state and disturbance in the intersample time interval, and an active anti-disturbance controller that updates at a fast rate is accordingly proposed, such that the desirable control performance and disturbance rejection performance can be achieved. At each fast-rate updating time instant, we use the prediction technique to generate a data packet, including the computed current control input and the predicted values of the control inputs for the future finite steps, and design a new periodic event-triggered mechanism to determine whether to transmit the data packet via a communication network or not. The proposed control method is easily implemented in digital platform since it has a discrete-time form. To verify the effectiveness of the proposed control method, we finally present the simulation results of a practical speed control system.

This paper develops a new estimate-based dynamic event-triggered output feedback controller for networked control systems subject to nonlinear uncertainties. Specifically, based on the sampled-data, a discrete-time output feedback controller and a discrete-time dynamic event-triggering condition are proposed by the virtue of feedback domination technique. The proposed event-triggered control method is easy to implement in digital computers due to the form of discrete time. Under the proposed dynamic event-triggered control method, the selection regions of the sampling period and the scaling gain are explicitly given to guarantee the global practical/asymptotic stability of the closed-loop system. Finally, two examples are employed to verify the efficiency of the proposed dynamic event-triggered control approach.

This paper proposes a new output-based dynamic event-triggered mechanism (ETM) for disturbance rejection control of a class of networked nonlinear uncertain systems subject to additive time-varying disturbance. In the proposed control method, a new robust output feedback controller is first designed based on a generalized proportional-integral observer to attenuate/compensate the undesirable influence of nonlinear uncertainties and disturbances. Different from the static ETM, two new dynamic variables are defined, and thereafter, two kinds of different discrete-time dynamic ETMs are developed only using the sampled-data output signal, such that a better tradeoff between the communication properties and the control properties can be obtained. It is shown that under the proposed control methods, the global bounded stability of the closed-loop hybrid system can be guaranteed by choosing some appropriate parameters. Finally, the numerical simulations of a single link robot arm are conducted to demonstrate the feasibility and efficacy of the proposed control approach.

This paper develops a methodology on sampled-data-based event-triggered active disturbance rejection control (ET-ADRC) for disturbed systems in networked environment when only using measurable outputs. By using disturbance/uncertainty estimation and attenuation technique, an event-based sampled-data composite controller is proposed together with a discrete-time extended state observer. Under the presented new framework, the newest state and disturbance estimates as well as the control signals are not transmitted via the common sensor-controller network, but instead communicated and calculated until a discrete-time event-triggering condition is violated. Compared with the periodic updates in the traditional time-triggered active disturbance rejection control, the proposed ET-ADRC scheme can remarkably reduce the communication frequency while maintaining a satisfactory closed-loop system performance. The proposed discrete-time control scheme provides the engineers with a manner of direct and easier implementation via networked digital computers. It is shown that the bounded stability of the closed-loop system can be guaranteed. Finally, an application design example of a dc-dc buck converter with experimental results is conducted to illustrate the efficiency of the proposed control scheme.

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

In this paper, the decentralized robust output-feedback tracking control problem is investigated for a class of uncertain nonlinear interconnected systems subject to high order time varying disturbances. Firstly, generalized-proportional-integral observers (GPIOs) are designed for every subsystems such that the disturbances and unmeasured states can be recovered. Then, based on the estimation information of GPIOs and the output-feedback domination approach, a composite decentralized output-feedback controller is constructed. The proposed decentralized control scheme can not only handle the uncertain nonlinear interconnected terms, but also remove the influences the disturbances effectively via feedforward compensation manner. Finally, a practical example is conducted to demonstrate the effectiveness of the proposed control approach.

In this paper, we propose an event-driven output feedback control strategy for a class of nonlinear systems subject to disturbances. Different from the time-driven control, the event-driven control can be regarded as a more reactive approach where the control actions are taken only when an event is triggered, thus the event-driven control has a better balance between the control performance and other system aspects (such as processor load, communication load, and system cost price). Based on the extended state observer (ESO), we propose a composite event-driven controller, which is asynchronously updated only when an intolerable effect on the closed-loop performance is produced. It is proved that the closed-loop system is globally uniformly bounded, and has a good robustness against disturbances. Meanwhile, the closed-loop system considered in this paper is a hybrid system, thus we need to consider the problem of Zeno behavior, which is a phenomenon unique to hybrid systems, and describes the situation where a hybrid system undergoes an unbounded number of discrete transitions in a finite and bounded length of time. Fortunately, it is proved that the system under the event-driven controller can avoids the Zeno behavior of the sampling, and has the significantly reduced sampling frequency compared with the time-driven controller. Finally, a simulation of DC-DC buck converter is conducted to demonstrate the efficiency of the new scheme.