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Emission source microscopy (ESM) technique can be utilized for localization of electromagnetic interference sources in the electronic systems, but its accuracy is limited by the typical planar scanning mode. In order to increase the accuracy, this paper presents a novel cylinder-aperture ESM measurement system driven by 6-DOF manipulator, and investigated the control strategy to generate the maximum-area aperture and optimized scanning trajectory. Based on the multiple constraints of the cylinder-aperture ESM measurement, we proposes analyzing the impact of the constraints by steps. This can obtain the analytical solution of the manipulator workspace and support solving the maximum aperture area. Besides, a modified RRT*(Rapidly-exploring Random Trees) algorithm is addressed to optimize the manipulator trajectory. The simulation and tests have proven that this algorithm could obviously reduce the joint mutation and cumulative tracking error. In the experimental section, the near-field scanning (NFS) tests, planar-aperture ESM measurement and proposed cylinder-aperture ESM measurement were conducted to measure one benchmark emission source. The results have demonstrated that the cylinder-aperture ESM measurement has the best convergences on the radiation pattern of the emission source.

The output-based event-triggered cooperative output regulation problem is addressed for constrained linear heterogeneous multiagent system in this article. In light of the robust control theory, H∞ leader-following consensus with respect to exogenous signals, including both disturbance to be rejected and reference state of leader to be tracked, is guaranteed. Meanwhile, the system performance alleviates degradation through a model recovery anti-windup technique while encountering input saturation. Furthermore, the follower's self-state observer, the leader-state observer, and the anti-windup auxiliary system are integrated into a comprehensive system, and a unified event-triggering mechanism of full states is addressed. A fixed lower bound of sampled interval is adopted such that the frequency of data transmission gets reduced and no Zeno-behavior happens. Both the input and output of the follower's controller and anti-windup compensator hold constant, respectively, during the event-triggered intervals such that the resulting output-based event-triggered controller can be directly implemented in a digital platform. Finally, a simulation example is provided to illustrate the effectiveness.

This article investigates the task assignment problem in which multiple dispersed robots need to visit a set of target locations while trying to minimize the robots' total travel distance. Each robot initially has the position information of all the targets and of those robots that are within its limited communication range, and each target demands a robot with some specified capability to visit it. We propose a decentralized auction algorithm which first employs an information consensus procedure to merge the local information carried by each communication-connected (CC) robot subnetwork. Then, we apply a marginal-cost-based strategy to construct conflict-free target assignments for the CC robots. When the communication network of the robots is not connected, we demonstrate that the robots' total travel distance might in fact increase when their communication range grows, and more importantly, such a somewhat counterintuitive fact holds for a range of algorithms. Furthermore, the proposed algorithm guarantees that the total travel distance of the robots is at most twice of the optimal when the communication network is initially connected. Finally, Monte Carlo simulation results demonstrate the satisfying performance of the proposed algorithm.

This paper studies the adaptive fuzzy fault tolerant control (FTC) problem for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with unknown control directions in the presence of time-varying asymmetric output constraints. Our contribution includes a step forward beyond usual FTC results to exhibit that the system output of nonsquare and square MIMO systems is uniformly bounded against actuator faults by a novel FTC methodology without the fault detection unit, as well as stay in the preselected constraints. To obtain new results, an equivalent unconstrained system is established by employing an error transformation technique. Furthermore, a learning-based switching function scheme is proposed to automatically activate different groups of actuators without human's intervention for attenuating the influence of faulty actuators. By this means, no explicit fault detection and isolation units are needed to result in reducing the risk of false alarm or missed detections and expediting the responsiveness of the controller. Moreover, the obstacle caused by unknown control directions is circumvented by a novel technique combining the matrix decomposition technique and Nussbaum-type function. It is proven that the desired tracking performance with prescribed output bounds and the boundedness of all the other signals in the closed-loop system can be guaranteed via an improved average dwell time approach. Finally, simulation results demonstrate the merits of the proposed controller.

In this technical correspondence, a multilayer formation (MLF) control problem is considered and solved by a unified framework. The agents in each layer present a sort of hierarchical distinction: receive information from former layers, communicate inside the current layer, and send information to subsequent layers. With an arbitrary number of layers, we extend the previous result from undirected graphs to directed ones. The proposed controller achieves MLF without using the distributed estimators and the acceleration information. This removes the induced discontinuities and alleviates the system complexity. It is then proved that the closed-loop errors are semiglobally uniformly ultimately bounded. Simulations are presented to illustrate the effectiveness of this approach.

In this note, simultaneous-arrival-to-origin (SATO) convergence is defined --- all the elements of the system state arriving at the origin at the same time. Accordingly, a relevant sufficient condition is proposed for SATO convergence. Based on this formulation of SATO convergence, the classical sign function and the norm-normalized sign function are revisited. We investigate their differences with applications in sliding-mode control design, paying special attention to their convergence performance. It is found that both functions (expectedly when properly invoked) contribute to system stability, while the norm-normalized sign function additionally enables the system to achieve SATO convergence. This finding shows the distinctive merit of the norm-normalized sign function in achieving more than finite-time stability for a sliding-mode control system. Extensions to the scenario with a networked system are studied, where with the utilization of the norm-normalized sign function, the networked system (now with the SATO convergence property) drives all the agents to reach consensus simultaneously. Additionally, for double integrator systems and Euler-Lagrange systems, singularity-fr

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), where at the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also a norm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

This article addresses the resilient relative pose estimation problem for multiple mobile robot systems against abnormal sensor measurements. Motivated by the fact that in real implementations, sensors used for neighboring robot detection, such as stereo camera, laser range finder, etc., may suffer from unpredictable anomalies, a resilient relative pose estimation approach is proposed such that each robot can obtain satisfactory relative pose estimates of its neighbors for further coordination algorithm design. In the proposed approach, the optimal Kalman estimate is decomposed as a weighted sum of local state estimates, based on which a convex optimization problem is formulated to generate the resilient estimation. Unlike most of the existing approaches investigating similar problems, which assume that the statistics or the bound of anomalies is known in advance, our proposed approach is not limited by this assumption. The effectiveness of the proposed method has been validated by numerical simulations and real robot experiments. It has been demonstrated that the proposed approach is comparable to the Kalman filter in the absence of sensor anomalies, while boundedness of the relative pose estimation error can be guaranteed under abnormal observations.

In this article, based on polynomial differential inclusions, we propose a heuristic iterative approach for estimating the domains of attraction for nonpolynomial systems. First, we use the fuzzy model to construct a polynomial differential inclusion for the nonpolynomial system, which can be equivalently written as a time-invariant uncertain polynomial system. Then, beginning with an initial inner estimation, we present an iterative approach to enlarge this initial inner estimation by calculating common Lyapunov-like functions. Furthermore, the domains of attraction are estimated by combining this iterative approach with heuristic construction of differential inclusions. In the end, our heuristic iterative approach is implemented with linear semidefinite programming and then tested on some nonpolynomial examples with comparisons to the existing methods in the literature.

In this paper, a novel interaction control is presented to resolve the optimized robot-environment interaction control problems subject to flexible environment with unknown dynamics parameters. A cost function measuring the trajectory tracking error as well as the non-inertial interaction force is defined. A complete state space equation considering the robot desired trajectory, object dynamics and position parameters is also presented to address the optimized robot-environment interaction control problem. The improved Q-learning method is developed as the fundamental of the proposed control to deal with the challenges brought by the unknown environment dynamics and the reference position of the robot desired trajectory. Simulation and experimental studies verify the validity of the presented method.

G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of them., This paper has been withdrawn by the author since some statements are not right as raised by other researchers

In original Belief Functions (BF) theory, precise-valued belief structure has been widely used to represent uncertain information. However, this mentioned belief structure is difficult to effectively measure the specific hesitant situation, especially when decision makers have a set of possible values for the belief assignments of focal elements. In order to model the hesitant nature of the behavior of people to make a decision under uncertainty, we propose a Hesitant Fuzzy Belief Structure (HFBS) that is based on BF theory and the recent hesitant fuzzy sets theory. We also present the novel rule of combination of HFBS that is used and evaluated in a wearable Human Activity Recognition (HAR) system coupled with an Extreme Learning Machine (ELM). The evaluation of this new HFBS approach is done from two benchmark datasets. We clearly show its effectiveness and its superiority compared to various methods used classically for the wearable HAR.

As an extension of the conventional set‐point control problem, the dynamic region‐based tracking control scheme with obstacle avoidance is proposed for a class of uncertain strict‐feedback non‐linear systems. A novel adaptive tracking controller is designed by a fusion of artificial potential field, recursive backstepping approach, neural networks, dynamic surface control technique, calculus method, and Lyapunov stability theory. In the proposed control scheme, the objective region cannot be required to have a regular shape or a fixed size for the passibility of the system in constrained space. The region tracking error is transformed into a new virtual error variable for recursively designing a dynamic surface controller, and the dimension of neural network inputs can be greatly reduced, especially for high‐order systems. The Lyapunov theorem is used to confirm the stability and uniform boundedness of all closed‐loop signals. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

This paper addresses the dynamic positioning problem for Liquefied Natural Gas (LNG) carrier with unknown shielding effect via a sea state detector and robust control design. First, to deal with the shielding effect existing between LNG facility and LNG carrier, sea state detector is developed to estimate the variation of wave force on the LNG carrier. In addition, the wind drag coefficients can be obtained adaptively to achieve feedforward control. Then, to guarantee a safe operation, a robust constrained control with prescribed performance is proposed considering actuator dynamic. Further, once a large wave force is identified in the detector, Neural Network compensator will be activated in the controller. Lyapunov analysis is conducted to ensure the boundedness of all the closed-loop states. Finally, the feasibility of the theoretical results is demonstrated by simulation studies.

Action recognition is a critical task for social robots to meaningfully engage with their environment. 3D human skeleton-based action recognition is an attractive research area in recent years. Although, the existing approaches are good at action recognition, it is a great challenge to recognize a group of actions in an activity scene. To tackle this problem, at first, we partition the scene into several primitive actions (PAs) based upon motion attention mechanism. Then, the primitive actions are described by the trajectory vectors of corresponding joints. After that, motivated by text classification based on word embedding, we employ convolution neural network (CNN) to recognize activity scenes by considering motion of joints as "word" of activity. The experimental results on the scenes of human activity dataset show the efficiency of the proposed approach., 13 pages, 9 figures

This article concerns the theoretical analysis and mechanical verification of input-to-state stability (ISS) for nonautonomous discrete-time switched systems. To start with, based on a bounded function and the average dwell time, we successively propose less conservative sufficient conditions for uniform input-to-state stability, global uniform asymptotic input-to-state stability, and global uniform exponential input-to-state stability of nonautonomous switched nonlinear systems. Then, for systems with zero inputs, we apply our bounded function and average dwell time based method to further relax the sufficient conditions for their uniform stability, global uniform asymptotic stability, and global uniform exponential stability. Particularly, we propose a linear semidefinite programming based computable approach for mechanical verification of our current theoretical results for the rational (and even certain nonrational) nonautonomous switched systems. Note that our theoretical results and mechanical approach are both illustrated by examples.

For a class of nonlinear systems with mismatched time delays and disturbances, robust adaptive stabilization control is studied. The control law is split into two terms. One is used to cope with the delayed states, and the other is to handle the disturbance. The improved robust adaptive laws with control gain function and time-varying σ -modification are constructed to estimate the unknown integrated parameters. It is proved that the designed adaptive controller can ensure the asymptotic convergence of the closed-loop system states. Another adaptive algorithm is also proposed to ensure the asymptotic stability of the closed delayed nonlinear system without external disturbance.

This article addresses the finite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Furthermore, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in finite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.

In this article, tracking control is considered for a class of uncertain multi-input–multi-output (MIMO) nonlinear systems, where the time-varying parameters, the time-varying control coefficient and the time-varying disturbance are assumed to be unknown but to be bounded. Three stable adaptive tracking schemes for a given reference signal are proposed by devising different control algorithms. In the first scheme, bounded-error tracking is achieved in the sense that the tracking error converges exponentially to an adjustable region around the origin, where the $\sigma $ -modification adaptive laws are used to ensure the boundedness of all closed-loop signals. In the second scheme, asymptotic tracking is obtained in the sense that the tracking error converges to zero asymptotically, where the strictly positive and integral functions are employed in the control law to ensure the signal boundedness and zero-error tracking. In the third scheme, exponential tracking is gotten in the sense that the tracking error exponentially converges to zero with a given convergence speed, where exponential functions are incorporated into control law and adaptive laws to ensure system stability and the faster convergence. Three adaptive tracking schemes are, respectively, applied to nonlinear chaotic Chua’s circuit with control inputs. The parametric model is developed for Chua’s circuit with uncertain parameters and external disturbances. The effectiveness of the proposed control algorithms is demonstrated by comparative simulation studies.

In this paper, optimized interaction control is investigated for human-multi-robot collaboration control problems, which cannot be described by the traditional impedance controller. To realize global optimized interaction performance, the multi-player non-zero sum game theory is employed to obtain the optimized interaction control of each robot agent. Regarding the game strategies, Nash equilibrium strategy is utilized in this paper. In human-multi-robot collaboration problems, the dynamics parameters of the human arm and the manipulated object are usually unknown. To obviate the dependence on these parameters, the multi-player Q-learning method is employed. Moreover, for the human-multi-robot collaboration problem, the optimized solution is difficult to resolve due to the existence of the desired reference position. A multi-player Nash Q-learning algorithm considering the desired reference position is proposed to deal with the problem. The validity of the proposed method is verified through simulation studies.

Continued great efforts have been dedicated toward high-quality trajectory generation based on optimization methods; however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and incorporate the presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained and, thus, a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently, a nonlinear model predictive control problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and, thus, based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.

In this article, a globally adaptive neural-network tracking control strategy based on the dynamic gain observer is proposed for a class of uncertain output-feedback systems with unknown time-varying delays. A reduced-order observer with novel dynamic gain is proposed. An n th-order continuously differentiable switching function is constructed to achieve the continuous switching control of the system, thus further ensuring that all the closed-loop signals are globally uniformly ultimately bounded (GUUB). It is proved that by adjusting the designed parameters, the tracking error converges to a region which can be adjusted to be small enough. The effectiveness of the control scheme is demonstrated by two simulation examples.

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler-Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.

In this brief, impedance adaptation is investigated for robots interacting with unknown environments, subject to unknown environment dynamics and position parameters. A cost function that measures the tracking error and interaction force is defined, and a complete state-space function considering the desired trajectory, environment dynamics, and position parameters is presented. The unknown environment dynamics and the unobservable environment position lead to unknown part of the system function as well as the system states in the complete system model. To this end, the output feedback adaptive dynamic programming (OPFB ADP) method is selected to realize the optimized impedance adaptation. Moreover, when considering the environment position/trajectory, the optimal impedance solution is difficult to obtain due to the existence of the arbitrary endpoint. An adaptive dynamic programming algorithm considering the trajectory tracking problem with arbitrary endpoint is proposed to deal with the problem. The convergence speed is accelerated by adding a discount factor. The validity of the proposed method is verified through simulation and experimental studies.

Multisensor fusion strategies have been widely applied in human activity recognition (HAR) in body sensor networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this article, Dezert–Smarandache theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the kernel density estimation (KDE)-based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of basic belief assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to deal with the conflict between the obtained BBAs, proportional conflict redistribution-6 (PCR6) is applied to fuse the acquired BBAs. Moreover, the missing data of the involved sensors are addressed as ignorance in the framework of the DSmT without manual interpolation or intervention. Experimental studies on two real-world activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) are conducted, and the results shows the superiority of our proposed method over some state-of-the-art approaches proposed in the literature.

This paper addresses the trajectory analysis, mission design, and control law for multiple microsatellites to cooperatively circumnavigate a host spacecraft. This cooperative circumnavigation (CCN) problem is defined to drive a group of networked microsatellites to a predefined planar ellipse concerning a host spacecraft while maintaining a geometric formation configuration. We first design several potential functions to guide the microsatellites to the given planar elliptical orbit with a proper radius. Next, the affine Laplacian matrix is introduced to characterize the desired formation shape of microsatellites. Based on the potential functions and the Laplacian matrix, a CCN control law is finally proposed. Then, the simulation results of eight microsatellites with earth-orbiting mission scenarios are given, where the natural trajectory motion is incorporated which consumes nearly zero-fuel.

In this paper, a global finite-time cooperative control is first time proposed for cooperative multiple manipulators. The proposed control scheme is developed based on an integration between a finite-time disturbance observer (FTDO) and a finite-time integral sliding mode controller (FTISMC) to get a high robustness against the effects of the model uncertainties and disturbances in the system. The switching term of the integral sliding mode controller is reconstructed such that the desired sliding manifold can be convergent in a finite time. The nominal controller of the integral sliding mode control is developed based on an advanced backstepping control, namely, finite-time backstepping control, which also provides a finite time convergence. The integration of the finite-time disturbance observer, finite-time switching term, and the finite-time backstepping controller forms a new global finite-time integral sliding mode control. The effectiveness of the proposed approach is demonstrated based on a cooperative control of a dual two-link manipulators.

This paper investigates the exponential attracting sets of discrete-time Markovian jump delay systems (MJDSs) with stochastic disturbances (SDs) via impulsive control (IC). By virtue of the Lyapunov-Razumikhin technique, we derive several sufficient criteria on the pth moment exponential attracting sets of the impulsive controlled discrete-time MJDSs with SDs and the estimation of the attracting sets is also given. It is shown that IC could make both unbounded and bounded impulsive-free discrete-time MJDSs with SDs into the bounded ones. Examples are presented to illustrate the effectiveness of the proposed theoretical results.

This article addresses the dissipativity-based asynchronous repetitive control problem for networked Markovian jump systems subject to time delays and partly accessible mode detection probabilities. A hidden Markov model is introduced to describe the asynchronous phenomenon between the system modes and controller modes. Based on this, an asynchronous repetitive controller is proposed. By utilizing the lifting technique, the 1-D delayed Markovian jump system and controller governing equations are converted into a stochastic and closed-loop 2-D delayed model to describe the control and learning actions. Utilizing a stochastic Lyapunov functional, sufficient conditions in terms of matrix inequalities are derived such that the closed-loop system is mean-square stable and achieves the specified $(\mathscr {W}_1,\mathscr {W}_2,\mathscr {W}_3)$ - $\gamma$ -dissipative performance. Under the assumption that the input matrix is full column rank in all modes of operation, a set of feasible sufficient conditions described by linear matrix inequalities is established by making use of the Schur complement. A procedure for synthesizing the controller parameters is also provided. A detailed numerical example with simulation results is presented to validate the proposed dissipativity-based asynchronous repetitive control design scheme.

This paper considers the problem of asynchronous ${H}_{{\infty }}$ control for networked Markovian jump systems subject to probabilistic packet dropouts and communication delays in the measurement channel. A new dynamic output-feedback-based asynchronous controller is proposed wherein the dynamic output-feedback controller modes need not synchronize with the system modes. By utilizing results from stochastic Lyapunov–Krasovskii stability theory, sufficient conditions in terms of matrix inequalities are derived such that the closed-loop networked Markovian jump system is stochastically stable and achieves the prescribed ${H}_{{\infty }}$ performance. Using the Schur complement technique and under the assumption that the input matrix is full rank, the sufficient condition is reduced to a linear matrix inequality and the dynamic output-feedback-based asynchronous controller is synthesized. A detailed numerical example with simulation results are presented to evaluate the proposed controller design scheme.