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This paper presents an approach and analysis for performing decentralized cooperative control of a team of decoys to achieve the Honeypot Ambush tactic. In this tactic, the threats are successfully lured into a designated region where they can be easily defeated. The decoys learn to cooperate by incorporating a game-theory-based online-learning method, known as regret minimization, to maximize the team's global reward. The decoy agents are assumed to have physical limitations and to be subject to certain stringent range constraints required for deceiving the networked threats. By employing an efficient coordination mechanism, the agents learn to be less greedy and allow weaker agents to catch up on their rewards to improve team performance. Such a coordination solution corresponds to achieving convergence to coarse correlated equilibrium. The numerical results verify the effectiveness of the proposed solution to achieve a global satisfaction outcome and to adapt to a wide spectrum of scenarios.

Addressing the leader-follower consensus control problem with time-varying output constraints for a class of second-order nonlinear multi-robot systems, we apply a unified barrier Lyapunov function to transform the constrained output state into the unconstrained one, while removing the feasibility condition existing in the traditional barrier Lyapunov function. Using the radial basis function-based neural network to approximate the unknown nonlinear function, we derive an adaptive neural network consensus controller to ensure that each robot follows the predefined trajectory of a leader. We then verify the effectiveness of the consensus control via simulation studies on a team of robotic manipulators.

In this paper, we present a decentralised online decision-making strategy for multi-agents carrying out a cooperative mission. Our solution provides the capability for agents to dynamically choose their best targets and arrive at their target locations simultaneously at pre-specified angles. Additionally, the agents are able to cope with any obstacles encountered without compromising the mission goals. The algorithm combines game-theoretic regret minimisation with current best-practice solutions to satisfy complex mission requirements. It is decentralised and readily scalable to a large number of agents for wide area operations. Simulation results show it can be applied to teams of agents in challenging environments and exhibits fast convergence and adaptability.

For practical applications of multi-agent systems, agents could be continuously subject to external disturbances, which is likely to negatively affect their tracking performances. Furthermore, the unknown or inaccurate factors in system dynamics can also bring negative effects to a system’s performance. In this paper, an uncertainty-observer-based dynamic sliding mode control scheme is proposed to deal with time-varying formation control problems of second-order nonlinear multi-agent systems. An uncertainty observer is first implemented for each agent to estimate the combination of the agent’s external and internal uncertainties and its time derivative. An observer-based dynamic sliding mode formation control law is developed for a cluster of second-order nonlinear agents to achieve time-varying formation. Finally, a numerical simulation is provided to illustrate the validity of the proposed control approach.

Device management in large networks is of growing importance to network administrators and security analysts alike. The composition of devices on a network can help forecast future traffic demand as well as identify devices that may pose a security risk. However, the sheer number and diversity of devices that comprise most modern networks have vastly increased the management complexity. Motivated by a need for an encryption-invariant device management strategy, we use affiliation graphs to develop a methodology that reveals key insights into the devices acting on a network using only the source and destination IP addresses. Through an empirical analysis of the devices on a university campus network, we provide an example methodology to infer a device’s characteristics (e.g., operating system) through the services it communicates with via the Internet.

Formation control for a team of robots is investigated in this paper. We consider the case where no communication channels are available. Only sensors with limited ranges can provide the robots wit...

This paper presents a new technique for stabilising a Takagi–Sugeno (T-S) fuzzy system with time-delay and uncertainty. A robust fuzzy functional observer is employed to design a controller when the system states are not measurable. The model uncertainty is norm bounded, and the time-delay is time-varying but bounded. The parallel distributed compensation method is applied for defining the fuzzy functional observer to design this controller. The proposed procedure reduces the observer order to the dimension of the control input. Improved stability conditions are provided for the observer compared with the existing results of functional observer-based stabilisation of T-S fuzzy models. Lyapunov–Krasovskii functionals are used to construct delay-dependent stability conditions as linear matrix inequalities. The solution of these inequalities is used for calculating the observer parameters. The sensitivity of the estimation error to the model uncertainty is reduced by minimising the $$L_2$$ gain. The new design method developed is illustrated and verified using two examples.

In this study, an optimally distributed formation control approach is proposed for mixed-order multi-agent systems to achieve formation consistency with obstacle avoidance. The systems consist of unmanned aerial vehicles and ground robots. The authors present a formation consistency control for a mixed-order system under switching topologies. An optimally distributed method is developed by matrix transformation method and linear quadratic regulator theory, which leads to an optimisation on both horizontal and vertical control. For obstacle avoidance, a non-quadratic cost function is introduced based on optimal control. The resulting closed-loop mixed-order system is validated to be stable with an optimal performance. The formation consensus and obstacle avoidance are verified by simulations to show the effectiveness and potentials.

To gain a better understanding the synchronization mechanism of networked neurons, this paper studies the synchronization control for a class of reaction–diffusion FitzHugh–Nagumo systems, associated with a digraph containing at least one directed spanning tree. A novel control method adopting spatial sampling strategies is proposed, in which the control inputs are constructed directly on the spatial means of system state variables on sampling subsets. After discussing the existence and uniqueness of classical solutions, we show analytically that the synchronization of the controlled systems is equivalent to that of the corresponding FitzHugh–Nagumo systems under the corresponding control inputs. Based on the study on general algebraic connectivity, a sufficient condition for the system synchronization is given, together with case simulations to illustrate the effectiveness and potential of the new control method.

This paper studies the problem of mean square asymptotical synchronization and $$H_\infty $$ synchronization for coupled stochastic reaction–diffusion systems (SRDSs) via boundary control. Based on the deduced synchronization error dynamic, we design boundary controllers to achieve mean square asymptotical synchronization. By virtue of Lyapunov functional method and Wirtinger’s inequality, sufficient conditions are obtained for ensuring mean square asymptotical synchronization. When coupled SRDSs are subject to external disturbance, mean square $$H_\infty $$ synchronization is investigated and corresponding criterion is presented under a designed boundary controller. In addition to focusing on systems with Neumann boundary conditions, we also briefly study coupled SRDSs with mixed boundary conditions and sufficient conditions are provided to achieve the desired performance. Numerical examples are used to verify the effectiveness of our theoretical results.

This paper proposes a novel interval observer design method for discrete-time linear systems with unknown but bounded disturbance and measurement noise. The proposed interval observer has a new structure that provides more design degrees of freedom. A direct method based on H ∞ technique is used to improve the accuracy of interval estimation. The design conditions are formulated into linear matrix inequalities, which can be efficiently solved. Two numerical examples are given to illustrate the design and validate the performance of the interval observers.

This paper addresses the control problem for a class of discrete-time Markov jump linear systems with partially unknown transition probabilities using model predictive controller subject to external disturbances and input constraints. Our focus is on the design of a model predictive controller to stabilize the system with a given mixed H2/H∞ performance index. Sufficient conditions are derived in terms of a set of linear matrix inequalities. Examples are presented to demonstrate the effectiveness of the proposed controller design method.

Based on convex analysis, a novel consensus algorithm of dynamical points in a CAT(0) space is developed in this paper, in which the associated communication graph uniformly contains a directed spanning tree. The proposed algorithm provides an efficient method of solving consensus problems in a general CAT(0) space, while having certain robustness against weak communication. The application of the new algorithm to the distributed fusion of phylogenetic trees is shown with demonstrative-case simulations, together with a study on the algorithm's robustness and efficiency.

In this paper, distributed formation is studied for a team of mobile robots including leaders and followers. Followers are able to sense the relative displacements to neighbouring followers...

This paper considers the exponential stability in p th( p ≥ 1 ) moment and the almost sure exponential stability for neutral stochastic delay systems with Markovian switching. By using the generalized integral inequality and the nonnegative local martingale convergence theorem, the Lyapunov stability results on the exponential stability in p th( p ≥ 1 ) moment and the almost sure exponential stability for such systems are given when the time-varying delay is a bounded measurable function and the coefficients in the monotonicity condition are time varying. The derived results are illustrated by two examples.

This paper considers H − ∕ H ∞ fault detection observer design in the finite frequency domain for a class of linear parameter-varying (LPV) descriptor systems. We propose a novel fault detection observer with a non-singular structure. To make the residual sensitive to faults and robust against disturbances, we develop a finite frequency H − ∕ H ∞ design method based on a generalized Kalman–Yakubovich–Popov lemma for LPV descriptor systems. Design conditions for the proposed fault detection observer are derived and converted as linear matrix inequalities. Simulation studies are conducted to demonstrate the performance of the proposed method.

A signal processing method of coherent Doppler lidar (CDL) for wind measurement is proposed in this paper, which combines the H-infinity control and coherent integration. This method can improve the signal-to-noise ratio and reduce the interference; the measured wind speed will be more exact. In this paper, the basic theory of signal processing method is proposed. That includes the main idea of how to combine the two different methods and the detailed information of this two methods. Then, the simulation by MATLAB is done, and the results are given to confirm the practicality of the new idea. The wind speed deviation before and after doing the signal processing is simulated and compared. It confirms that the accuracy has improvement by using this method mentioned in this paper.

This paper considers robust fault estimation in the finite frequency domain for linear continuous-time descriptor systems. Based on state augmentation, we propose a fault estimation observer with a...

This paper presents a sliding mode control scheme for tracking control of nonlinear singularly perturbed systems in the presence of model errors and external disturbances. A dual-loop feedback control is developed to provide accurate tracking capability and sufficient robustness to system uncertainties. A sliding mode controller is proposed in the outer-loop feedback design such that the plant states are stabilised for given reference trajectories, while an additional robust controller is designed in the inner loop to increase the adaptability to uncertainties, and reduce the effect of unmodelled high-frequency dynamics on plant dynamics. An appealing feature of the control scheme is the attenuation of chattering. The effectiveness and merits of the new control scheme developed are shown via a verification example of velocity control of a quad-rotor.

This paper proposes a new adaptive controller for a class of uncertain nonlinear systems with a quantized signal. Fuzzy logic systems are utilized to approximate nonlinear terms without imposing prior matching conditions required. A hysteretic type of quantizer is incorporated to reduce chattering. A new adaptive backstepping controller is designed to guarantee that the underlying uncertain nonlinear system is semiglobally uniformly ultimately bounded. Two numerical examples are presented to demonstrate the effectiveness and potential of the proposed techniques.

A new model reduction (MR) method is developed for continuous-time switched linear parameter-varying (LPV) systems based on time-weighted gramians. Using time-weighted energy functionals and parameter-independent multiple Lyapunov functions, the time-weighted controllability and observability gramians are obtained. With a balanced transformation matrix optimally derived, the authors show that a balanced realisation can be achieved for the original switched LPV systems. A series of low-order models for different weighting factors are yielded by state truncation or singular perturbation techniques. The reduced-order models can preserve the stability. The feasibility and effectiveness of the proposed MR method are illustrated by two examples including a three spring-mass system.

This paper proposes an approach for constructing and generating chaos from a class of three-dimensional linear switching systems via a heteroclinic loop based on the Shilnikov criterion. First, the existence of a switching rule for the system is derived by utilizing the Shilnikov heteroclinic criterion. Then a general design philosophy and its procedure of switching rule are provided to ensure that the proposed approach is applicable to engineering. Two numerical examples are presented to validate the main principle and the implementability of the scheme. Theoretical analysis and numerical simulation are used to demonstrate the feasibility and effectiveness of developed techniques.

We propose an H-/L∞ fault detection observer for linear parameter varying systems with unknown disturbance and actuator fault. The fault detection observer is designed to enhance the fault sensitivity of residual and attenuate the effect of disturbance on residual such that timely and robust fault detection is achieved. The observer design method is carried out in the finite frequency domain and the design conditions are expressed as a set of linear matrix inequalities. By using L∞ performance index to measure the effect of disturbance on residual, the fault detection observer exhibits a peak-to-peak disturbance attenuation performance, which can provide a dynamic threshold for residual evaluation in fault detection. Numerical simulations are conducted to verify the effectiveness of the proposed method.

The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is an efficient multi-target tracking approach to the track-before-detect problem. A fundamental feature of the H-PMHT is the discretisation of the energy in the sensor data and the assumption of a multinomial measurement model on the resulting image. A problem with the H-PMHT is that the multinomial measurement model fails to account for fluctuations in the target amplitude, which can degrade performance in realistic sensing conditions. The authors propose an alternative measurement model based on a Poisson mixture process to allow for fluctuating target amplitudes. Simulations show that this new approach, referred to as the Poisson H-PMHT, gives more accurate signal-to-noise ratio estimates than the standard H-PMHT, particularly for scenarios featuring targets with fluctuating amplitude.

In the paper, an arclength notion of continuous curves in fuzzy metric spaces is proposed, and some arclength properties are studied, including invariance, additivity, continuity and boundedness. It turns out that these arclength properties imply the corresponding ones in metric spaces.

This paper considers the sampled-data fuzzy control of nonlinear systems in strict feedback form with disturbances and random missing input data. We propose a novel method in which a state observer and a disturbance observer are combined to construct a sampled-data fuzzy output feedback controller. The stochastic variables with a Bernoulli distributed sequence are used to model missing input data. Fuzzy logic systems are applied to approximate nonlinearities without requiring prior knowledge. The relation between observer gain and sampling period is established. The output feedback controller designed guarantees that the nonlinear system is globally stable. A simulation example of four degrees of freedom robotic arm is used to demonstrate the effectiveness and applicability of the proposed control scheme.

Summary This paper is concerned with the reliable H∞ static output control of linear time-varying delay systems with sensor faults. Time-varying delay is tackled by the input–output transformation and the resulting closed-loop system lies in the framework of scaled small gain. Some techniques are developed to separate the coupling among the Lyapunov matrix, input matrix, control gain matrix, and output matrix. Based on a relaxed Lyapunov–Krasovskii functional, sufficient conditions for the desired static output controller design with the required H∞ performance level are proposed by means of linear matrix inequalities. The effectiveness of the proposed method is validated by two examples. Copyright © 2016 John Wiley & Sons, Ltd.

In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.

In this paper, a controller is designed for a group of cooperative robots to achieve the stable displacements. Communication network among the robots is assumed unavailable and each robot can only measure the displacements relative to their neighbours. The controller will enable the robots to move to a predefined formation or a special formation under a suitable protocol which we will design. We will discuss the effect of different sizes of the multi-vehicle system and conduct simulations to verify the effectiveness of the proposed controller and the formation protocols.

In this paper, the predictive and optimal control problem of energy consumption is investigated for hybrid vehicle systems. In order to make the energy usage more efficient (minimizing the energy demand), observations of drivers' behavior are taken into consideration in analysis and predictive control design. Realworld driving experiments are conducted to illustrate the effective and accuracy of the proposed new design techniques.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

This paper studies the problem of cluster synchronization at exponential rates in both the mean square and almost sure senses for neutral stochastic coupled neural networks with time-varying delay via a periodically intermittent pinning adaptive control strategy. The network topology can be symmetric or asymmetric, with each network node being described by neutral stochastic delayed neural networks. When considering the exponential stabilization in the mean square sense for neutral stochastic delay system, the delay integral inequality approach is used to circumvent the obstacle arising from the coexistence of random disturbance, neutral item, and time-varying delay. The almost surely exponential stabilization is also analyzed with the nonnegative semimartingale convergence theorem. Sufficient criteria on cluster synchronization at exponential rates in both the mean square and almost sure senses of the underlying networks under the designed control scheme are derived. The effectiveness of the obtained theoretical results is illustrated by two examples.

As the reliance on the Internet and its constituent applications increase, so too does the value in exploiting these networking systems. Methods to detect and mitigate these threats can no longer rely on singular facets of information, they must be able to adapt to new threats by learning from a diverse range of information. For its ability to learn complex inferences from large data sources, deep learning has become one of the most publicised techniques of machine learning in recent years. This chapter aims to investigate a deep learning technique typically used for image classification, the convolutional neural network (CNN), and how its methodology can be adapted to detect and classify malicious network traffic.

Self-adapting variable step size (SAVSS) normalized least mean square (NLMS) algorithms are derived and analysed for the active noise control systems with acoustic feedback path. The objective of the proposed SAVSS scheme is to resolve the conflicting requirements of rapid convergence and low mis-adjustment. A tuningless power scheduling scheme is proposed that varies the gain according to the output of the predictor filter, and switches to a low gain on the basis of a minimum criteria. In the steady state, a low gain auxiliary noise significantly improves noise reduction performance, and the update of the feedback compensation filter and predictor filter is discontinued to reduce computations. Simulations are performed with narrowband noises to validate the improved performance of the proposed method in comparison with established state-of-the-art methods.

Complex nonlinearity and strong coupling in the pitch-yaw-roll of a rotorcraft make it difficult for the attitude control of unmanned aerial vehicles, especially in the presence of wind disturbances and plant uncertainties. To confront these problems, this paper presents a control scheme of two sequential steps, structural parameter determination and robust controller design, in order to achieve the desired attitude regulation performance and disturbance attenuation capability. For the structural parameter design, key structural parameters are specified such that the open-loop transfer functions, from torque inputs to angular velocities, exhibit positive-real properties in particular frequency ranges to incorporate robustness into the controller design. Then, a dual-loop structured controller is designed using dynamic inversion approaches and singular perturbation techniques. The controlled inner loop provides adequate decoupling such that the outer loop may control each Euler angle independently, and proportional-derivative control is used in each outer loop to achieve a desired attitude tracking performance. The effectiveness of the control scheme is verified via an attitude tracking example.

We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.

Summary This paper considers a class of stochastic systems referred to as stochastic switched systems of neutral type with time-varying delay, which combines switched systems with neutral stochastic systems. The systems consist of subsystems of two forms: (i) only stable subsystems and (ii) both stable subsystems and unstable subsystems. By establishing an integral inequality, the exponential stability in pth(p≥1)-moment for such systems with only stable subsystems is first considered. Then, by using an average dwell time approach, the exponential stability in pth(p≥1)-moment for the second form is addressed. An important finding of this study is that when the average dwell time is chosen to be sufficiently large and the total activation time of unstable subsystems is relatively small compared with that of stable subsystems, the exponential stability in pth(p≥1)-moment for such systems can be guaranteed. Two major advantages of these new results are that the differentiability or continuity of the delay function is not required compared with the existing results in the literature, and the proposed approaches can be used to consider the case when the neutral item and the stochastic perturbation are simultaneously presented. An example is provided to verify the effectiveness and potential of the theoretic results obtained. Copyright © 2016 John Wiley & Sons, Ltd.

Summary In this paper, a protocol and a control law are designed for a single robot so that a team of such robots can interact and cooperate to reach the displacements from an eligible reference formation. Each robot is equipped with displacement sensors of limited sensing ranges. Communication channels are assumed to be unavailable to the team, and each robot works in stealth mode. The team is scalable such that new robots can be recruited, and existing robots can be dismissed. In order for the team size to be scalable, the extended formation based on relative displacement is established as the reference formation. Thus, using the extended formation as a reference, the control law and the protocol could be flexible. As potential conflicts deflect the robot team from the desired formation, the control law is designed to expose the conflicts to the involved neighboring robots such that the protocol can resolve them. A numerical example is given to illustrate how an extended formation is designed, and a simulation example is conducted to demonstrate the performance and merits of the proposed techniques. Copyright © 2016 John Wiley & Sons, Ltd.

Y-junctions are often adopted to design diplexers since their characteristics can conveniently satisfy the constraints for optimum performance of diplexer design. In order to decrease the size of the Y-junction diplexer, the two branches of the junction can be bent to yield parallel outputs. However, this parallel configuration can degrade the characteristic of the Y-junction. In this letter, two improved novel configurations of Y-junction diplexers are introduced, analyzed with an efficient mode-matching based approach and optimized for best performance. For illustration, the method is applied to two diplexers in substrate-integrated waveguide technology realized using band-pass post filters. For each diplexer, the fractional bandwidths of operation for the up and down channels are 2.70% and 2.44% at 7.4 GHz and 8.2 GHz respectively. The viability of the design approach is validated by full-wave finite-element simulations using Ansys HFSS and measurements on the two fabricated diplexers. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1384–1388, 2016

In situations where robots need to keep electromagnetic silent in a formation, communication channels become unavailable. Moreover, as passive displacement sensors are used, limited sensing ranges are inevitable due to power insufficiency and limited noise reduction. To address the formation control problem for a scalable team of robots subject to the above restrictions, a flexible strategy is necessary. In this paper, under the assumption that the data transmission among the robots is not available, a novel controller and a protocol are designed that do not rely on communication. As the controller only drives the robots to a partially desired formation, a distributed coordination protocol is proposed to resolve the imperfections. It is shown that the effectiveness of the controller and the protocol rely on the formation connectivity, and a condition is given on the sensing range. Simulations are conducted to illustrate the feasibility and advantages of the new design scheme developed.

This paper investigates the problem of event-triggered $H_{\infty}$ control for a networked singular system with both state and input subject to quantizations. First, a discrete event-triggered scheme, which activates only at each sampling instance, is presented. Next, two new sector bound conditions of quantizers are proposed to provide a more intuitive stability analysis and controller design. Then, network conditions, quantizations, and the event-triggered scheme are modeled as a time-delay system. With this model, the criteria are derived for $H_{\infty}$ performance analysis, and codesigning methods are developed for the event trigger and the quantized state feedback controller. An inverted pendulum controlled through the network is given to demonstrate the effectiveness and potential of the new design techniques.

In this paper, an adaptive neural fault-tolerant control scheme is proposed for the three degrees of freedom model helicopter, subject to system uncertainties, unknown external disturbances, and actuator faults. To tackle system uncertainty and nonlinear actuator faults, a neural network disturbance observer is developed based on the radial basis function neural network. The unknown external disturbance and the unknown neural network approximation errors are treated as a compound disturbance that is estimated by another nonlinear disturbance observer. A disturbance observer-based adaptive neural fault-tolerant control scheme is then developed to track the desired system output in the presence of system uncertainty, external disturbance, and actuator faults. The stability of the whole closed-loop system is analyzed using the Lyapunov method, which guarantees the convergence of all closed-loop signals. Finally, the simulation results are presented to illustrate the effectiveness of the new control design techniques.

This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.