This article presents a novel event and learning-based resilient formation control strategy for heterogeneous multiagent systems subjected to denial-of-service (DoS) attacks and uncertainties. It involves a decoupled cyber-layer and physical system layer design that enables a distributed and model-free approach. In the cyber-layer, the design is an event-triggered resilient observer for a reference exosystem estimation under DoS attacks using dual adaptive laws and an optimal algorithm. This approach eliminates the need for global information of the communication topology and enhances system resilience under attacks. In the physical system layer, the design is a model-free formation output controller for heterogeneous agents based on off-policy reinforcement learning. The incorporation of a new rank condition improves the convergence performance. Experiments using unmanned ground vehicles are conducted for scanning a physical area to verify the effectiveness and resilience of the proposed control strategy.

This article addresses the problem of lateral control problem for networked-based autonomous vehicle systems. A novel solution is presented for nonlinear autonomous vehicles to smoothly follow the planned path under external disturbances and network-induced issues, such as cyber-attacks, time delays, and limited bandwidths. First, a fuzzy-model-based system is established to represent the nonlinear networked vehicle systems subject to hybrid cyber-attacks. To reduce the network burden and effects of cyber-attacks, an asynchronous resilient event-triggered scheme (ETS) is proposed. A dynamic output-feedback control method is developed to address the underlying problem. Conditions are derived to obtain the output-feedback controller and resilient asynchronous ETS such that the closed-loop switched fuzzy system is globally exponentially stable. Examples are provided to demonstrate the effectiveness and merits of the proposed new control design techniques.

This article investigates the resilient control problem for nonlinear cyber-physical systems with hybrid cyberattacks and physical constraints, including aperiodic denial-of-service attacks, deception attacks, input saturation, and external disturbances. The underlying system is described by the fuzzy model by decomposing the nonlinear plant into some local linear subsystems. An input saturating integrator and an antiwindup compensator are introduced inside a dynamic output feedback fuzzy controller to handle the input saturation in both amplitude and rate. Within the resulting fuzzy-model-based control framework, a novel adaptive event-triggered-based resilient control method is proposed to ensure that the insecure and imperfect system is stochastically stable with an$H_\infty$performance level. Simulation results of an autonomous vehicle operating in a wireless network environment are provided to verify the effectiveness of the proposed control method.

This article investigates the collision-free cooperative formation control problem for second-order multiagent systems with unknown velocity, dynamics uncertainties, and limited reference information. An observer-based sliding mode control law is proposed to ensure both the convergence of the system's tracking error and the boundedness of the relative distance between each pair of agents. First, two new finite-time neural-based observer designs are introduced to estimate both the agent velocity and the system uncertainty. The sliding mode differentiator is then employed for every agent to approximate the unknown derivatives of the formation reference to further construct the limited-information-based sliding mode controller. To ensure that the system is collision-free, artificial potential fields are introduced along with a time-varying topology. An example of a multiple omnidirectional robot system is used to conduct numerical simulations, and necessary comparisons are made to justify the effectiveness of the proposed limited-information-based control scheme.

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness. Note to Practitioners—This article addresses the collaborative formation problem of multiagent systems that has potential applications in transportation and disaster relief. The design of robust and energy-saving coordination strategies is challenging in heterogeneous multivehicle systems. The proposed distributed method is suitable for large-scale uncertain heterogeneous systems, and the use of the dual adaptive event-triggered strategy reduces the data transmission rate.

In this article, we investigate the problems of robust exponential stabilization in mean square and$\mathcal {H}_{\infty }$-based integral sliding mode controller design for uncertain stochastic Takagi–Sugeno (T-S) fuzzy switched time-delay systems with both matched and unmatched uncertainties under synchronous switching and asynchronous switching, respectively. In these systems, the control input is dependent on a switching signal. Under synchronous switching and asynchronous switching, sufficient conditions are developed to guarantee that the resulting closed-loop systems are robustly exponentially stable in mean square with a prescribed$\mathcal {H}_{\infty }$-performance, respectively. The integral sliding mode surfaces and the sliding mode controllers are designed for the underlying systems under synchronous switching and asynchronous switching, respectively. By two examples stemming from the mass–spring–damper model and the single-link robot arm model under stochastic perturbation, the effectiveness of the results obtained is verified.

In this article, a new robust optimization method is proposed to simultaneously optimize the expectation and variability of system performance with parametric uncertainties and fuzzy variables. The expectation-entropy model is presented to characterize the fuzzy robust optimization problem as an equivalent biobjective optimization problem. An approximate mapping method is developed to calculate the response of fuzzy variables, which improves the computational efficiency of objective functions. Then, according to the decision makers’ preference for objectives, the optimization framework based on Stackelberg game is established. A leader–follower state transition algorithm is designed to search for the equilibrium solutions. Two practical case studies are provided to show the effectiveness of the new optimization approach in both subjective judgment and objective assessment.

This article addresses the problem of dynamic hybrid-triggered control for nonlinear networked control systems. A novel resilient control method is presented to improve network resource utilization and system performance against cyberattacks for the underlying systems. First, a fuzzy-model-based system is established to describe the nonlinear networked systems subject to multiple cyberattacks, external disturbances, parameter uncertainties, and network-induced delays. Then, a resilient dynamic hybrid-triggered scheme is proposed to conserve the network bandwidth and reduce the effect of cyberattacks. A new codesign method of the observer-based fuzzy controller and the resilient dynamic hybrid-triggered scheme is developed. Sufficient conditions in terms of linear matrix inequalities are given such that the closed-loop system is global exponential stable with an$H_\infty$disturbances attenuation level. Finally, an example of lateral control in autonomous driving is given to verify the effectiveness of the developed control method.

In this article, an event-triggered control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and the control law for each agent is only updated at its event sequence based on its own minimum collision time and deviation time. The probability-driven controller is designed to prevent the stuck problem among agents. Mapping-adaptive strategy and angle-adaptive scheme based on the minimum collision distance are also developed. Two examples are presented to analyze and demonstrate the effectiveness and potential of the new event-triggered adaptive control algorithm.

In this paper, the problem of formation control is studied for second-order multi-agent systems with practical issues like mismatched uncertainties and obstacle avoidance. A reference correction algorithm based sliding mode control scheme is proposed to ensure the boundedness of each agent's position tracking error. Both disturbance observer and artificial potential field are implemented to first tackle the problem of obstacle avoidance with the existence of mismatched uncertainties. The unreachable reference scenarios are then defined to describe the passive correcting behaviours of the multi-agent system when agents are trying to avoid obstacles. A distributed reference correction algorithm is developed for each agent to attenuate the harmful effects of passive correcting behaviours. The effectiveness of obstacle avoidance and boundedness of position tracking error are both verified by Lyapunov stability theory. Finally, multi-robot system based numerical simulations and comparisons are conducted to demonstrate the effectiveness of the proposed algorithm and controller.

In this paper, a two-stage reconfiguration strategy is proposed for a group of agents to find their specially designated formation, which can be seen as a transition from the current states to the predefined formations. The specially designated formation occurs during the inter-task idle time in order to minimize the reconfiguration time. The basic specially designated formation is designed for dotted agents based on the optimal mapping relationships and current probabilities of the predefined formations. Then, the reconfiguration schemes are developed with the agents modeled as circles or ellipses to account for their physical shapes, and the problem of agent overlapping during the formation is addressed. Examples and simulation results are presented to analyze and demonstrate the effectiveness and potential of the new design techniques.

In this article, the problem of robust optimization is considered for dynamical systems with both constraints and uncertainties. Conditions are established to ensure the existence of solutions to the problem with both robust optimality and feasibility. The objective performance with respect to fuzzy uncertainties is evaluated based on the expectation-entropy model. A feasibility robustness analysis method is proposed to handle the uncertainties in the constraints. Using the hierarchy structure in robust design, the optimization framework based on Stackelberg–Nash game is developed. A leader–followers state transition algorithm is designed to search for the equilibrium solution. Two application examples are given to demonstrate that the proposed robust optimization method can accurately evaluate the robustness performance and successfully search for a compromise solution.

In this paper, we discuss the modeling and stabilization problems for a class of discrete-time stochastic networked systems (DSNSs) subject to successive packet losses. The system under consideration is transformed into a stochastic one with bounded stochastic delay. Considering that the stochastic delay is characterized by nonuniform distribution, a new equivalent model is then constructed that enables the DSNS's controller design to benefit from knowing the probability characteristic of packet losses. Specially, to verify this feature, the probabilities of the delay can be explicitly obtained by utilizing the formula of total probability, which is critical to model transformation and analyze practical problems that exist in DSNSs. Based on the proposed model, sufficient conditions are established under which the globally mean-square asymptotic stability of resulting stochastic delay closed-loop system is guaranteed, and a delay-distribution-dependent design procedure is then proposed. A numerical example with simulation is provided to validate the analytical results and demonstrate the effectiveness of the design procedure.

This article studies the issue of stability for complex network control systems, which comprise multisystems and multicontrollers. These systems and controllers are coupled with each other and linked by communication networks with random delays. The system and controller network topologies are subject to jump, and the dynamic is captured by two models, as: 1) dual-Markov model and 2) hidden Markov model. Correspondingly, the stability conditions based on the Lyapunov-functional method are given for closed-loop stability. Then, the stability conditions are derived by transforming into the linear matrix inequality-based constraint minimization problem for solving controller gains. An illustration is carried out to testify the validation and potentials of the proposed new design techniques.

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.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H∞estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the 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 considers the problems on the mean square exponential synchronization and the almost surely exponential synchronization for neutral stochastic dynamical delay networks with Markovian switching via the single pinning impulsive control. By using a unified approach incorporating the concept of average impulsive interval and Lyapunov function, and the Borel-Cantelli lemma, two criteria on the mean square exponential stability and the almost surely exponential stability for impulsive neutral stochastic delay systems with Markovian switching are derived. It is shown that the impulsive control with a suitable impulsive strength can stochastically stabilize the underlying systems. With the symmetry and irreducibility of the switching topologies, and the obtained stochastic stability criteria, sufficient conditions are derived to determine the mean square exponential synchronization of the underlaying networks via the single pinning impulsive controller, which are given with algebraic inequalities and M -matrix. The almost surely exponential synchronization is also analyzed. Two examples are given to verify the validity of the theoretic results presented.

Many assignment problems cannot be solved to optimality in real-time. The existing literature tends to focus on the development of handcrafted heuristics that exploit the structure of a particular assignment problem. We instead seek a general-purpose approach that can automatically learn such heuristics. In this work, we present a deep learning approach that is designed to automatically learn heuristics for a large class of assignment problems. We demonstrate the effectiveness of our approach on the weapon-target assignment (WTA) problem, which is nonlinear and NP-complete. We take non-trivial WTA problem instances and show that our approach reliably finds near-optimal solutions in milliseconds.

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.

This paper presents a reliable control strategy for quadrotors to achieve a satisfactory tracking performance and provide a stable yaw angle control in the presence of wind disturbances. Based on the prelinearizing transformation and singular perturbation techniques, a quadrotor flight system is decomposed into two interconnected subsystems in different time-scales: 1) the angular rotations and 2) the linear translations. For the fast subsystem running as the inner loop, finite frequency H∞ control is used to enhance robustness and provide adequate decoupling such that each translational dynamic is controlled independently. For the slow subsystem running as the outer loop, H∞ loop shaping control via proportion-integration-differentiation control is used to achieve a desired servo performance even in the presence of wind disturbance. Simulations are performed under different wind conditions to validate the effectiveness of the new control strategy.

This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi-Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an -order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint.

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

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.

In this paper, we investigate the adaptive exponential synchronization in both the mean square and the almost sure senses for an array of N identical Markovian stochastic coupled neural networks of neutral-type with time-varying delay and random coupling strength. The generalized Lyapunov theorem of the exponential stability in the mean square for the neutral stochastic Markov system with the time-varying delay is first established. The time-varying delay in the system is assumed to be a bounded measurable function. Then, sufficient conditions to guarantee the exponential synchronization in the mean square for the underlying system are developed under an adaptive feedback controller, which are given in terms of the M -matrix and the algebraic inequalities. Under the same conditions, the almost sure exponential synchronization is also presented. A numerical example is given to show the effectiveness and potential of the proposed theoretical results.

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.

Delay compensation for a class of discrete-time nonlinear networked control systems with bounded controls while imposing positivity in closed-loop systems is considered in the paper. Under the state feedback and the output feedback control, compensation for the network induced delays is designed using the Lyapunov stability theory and matrix transformation with nonnegative control and bounded control. All the results are presented in linear programming (LP) forms. An example is given to show the effectiveness of the proposed methods.

This paper investigates the adaptively adjusted event-triggering mechanism-based fault detection for a discrete-time networked control system (NCS). The fault occurrence detection progress, the fault occurrence probability, and an adaptively adjusted event-triggering parameter are taken into account to construct a novel event-triggering mechanism. The event-triggered sensor and the event-triggered control station are used simultaneously to establish new network-based closed-loop models for the NCS subject to an actuator fault, and the sensor-to-control station and the control station-to-actuator network-induced delays. Based on the established models, the event-triggered simultaneous design of a fault detection filter and a controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. We analyse the performance of the simultaneous design of a fault detection filter and a controller to verify its effectiveness.

In this paper, the exponential stability in p th( p > 1 )-moment for neutral stochastic Markov systems with time-varying delay is studied. The derived stability conditions comprise two forms: 1) the delay-independent stability criteria which are obtained by establishing an integral inequality and 2) the delay-dependent stability criteria which are captured by using the theory of the functional differential equations. As its applications, the obtained stability results are used to investigate the exponential stability in p th( p > 1 )-moment for the neutral stochastic neural networks with time-varying delay and Markov switching, and the globally exponential adaptive synchronization for the neutral stochastic complex dynamical systems with time-varying delay and Markov switching, respectively. On the delay-independent criteria, sufficient conditions are given in terms of M -matrix and thus are easy to check. The delay-dependent criteria are presented in the forms of the algebraic inequalities, and the least upper bound of the time-varying delay is also provided. The primary advantages of these obtained results over some recent and similar works are that the differentiability or continuity of the delay function is not required, and that the difficulty stemming from the presence of the neutral item and the Markov switching is overcome. Three numerical examples are provided to examine the effectiveness and potential of the theoretic results obtained.

This paper studies the problem of event-triggered fault detection filter (FDF) and controller coordinated design for a continuous-time networked control system (NCS) with biased sensor faults. By considering sensor-to-FDF network-induced delays and packet dropouts, which do not impose a constraint on the event-triggering mechanism, and proposing the simultaneous network bandwidth utilization ratio and fault occurrence probability-based event-triggering mechanism, a new closed-loop model for the considered NCS is established. Based on the established model, the event-triggered H∞performance analysis, and FDF and controller coordinated design are presented. The combined mutually exclusive distribution and Wirtinger-based integral inequality approach is proposed for the first time to deal with integral inequalities for products of vectors. This approach is proved to be less conservative than the existing Wirtinger-based integral inequality approach. The designed FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the NCS to external disturbances. The simulation results verify the effectiveness of the proposed event-triggering mechanism, and the FDF and controller coordinated design.

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.

The Histogram-Probabilistic Multi-Hypothesis Tracker (H-PMHT) is an efficient multi-target tracking approach to the Track-Before-Detect (TkBD) problem. However, it cannot adequately deal with fluctuating targets and this can degrade track management performance. By assuming an alternative measurement model based on a Poisson distribution, the H-PMHT algorithm can be re-derived to incorporate a time-correlated estimate of the component mixing terms, allowing for an improved measure for track quality.

In this article, stability analysis for neutral stochastic neural networks with time-varying delay and Markovian jumping parameters is studied. By utilizing the theory of functional differential equations, some results about exponential stability criteria in p (p > 1)-moment, which are delay-dependent, are given. The primary advantages of these obtained results over some recent and similar works are that the differentiability or continuity of the delay function is not required, and that the difficulty stemming from the existence of the neutral item is overcome. A numerical example is given to examine the correctness of the derived result.

This paper is concerned with formation control and stability analysis for multi-agent systems. The individual sensing range is taken as limited and agents are assumed not able to communicate. The concept of extendible periodic formation is established to represent a class of large-scale systems and later used in the proof of stability. In the control strategy, the periodic formation is first partially reached with some misalignments. Then, in order to eliminate the misalignments, a multi-shape matching algorithm is designed to identify them and reach the predefined formation. The concept of group control edge is proposed, whose different types are analyzed. It is shown that under optimal sizes, the convergence rate could be maximized. Moreover, a premature stop strategy is presented that could reduce computation burden. Analysis is made on numerical examples to demonstrate the performance and merits of the proposed method.

This paper addresses the problem of robust fuzzy L2-L∞ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering error dynamic system is stochastically stable and that it has a prescribed L2-L∞ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.

In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

This paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure-feedback nonlinear systems. Using the implicit function theorem and backstepping technique, a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function is constructed for the development of control law and learning algorithms. Differing from the existing results in the literature, the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures. Furthermore, the scheme requires the desired trajectory and its first derivative rather than its first n derivatives. In addition, the useful property of the basis function of the radial basis function, which will be used in control design, is explored. Simulation results illustrate the effectiveness of the proposed techniques.

Abstract Medium access control (MAC) protocols making use of multipacket reception (MPR) capability achieve better throughput than conventional MAC protocols. When a wireless network operates with MPR capable nodes and non‐MPR nodes, the MAC protocols must not only utilise the MPR capability to maximise throughput, but must also enable the co‐existence with these two types of nodes. We propose a new MPR MAC protocol to achieve the co‐existence requirement by adopting a request‐to‐send (RTS)/clear‐to‐send (CTS) mechanism in IEEE 802.11 MAC standards. This MPR MAC protocol also improves throughput by allowing additional data transmissions to use the MPR capability fully. We analyse the system throughput of the co‐existence of different link characteristics of nodes, and optimise its throughput by adjusting contention window sizes with respect to certain throughput requirements of the nodes. Copyright © 2010 John Wiley & Sons, Ltd.

The interrupt mechanism in a system-on-chip (SoC) joins the SoCs hardware and software behaviors. We model interrupts as logical rather than physical events and accordingly provides guidelines to compose software components including interrupt-service-routines. As a benefit, classical indeterministic behaviors (due to the parallelism) in the software domain, such as preemption and nesting, can be constructed as early as raw hardware components are being integrated. In effect, while the interrupt mechanism itself is under rigorous stress, it is simultaneously driving the exercise of the entire SoC. This effect can be observed through software profiling at the hardware integration stage.

The verification of system-on-chip is challenging due to its high level of integration. Multiple components in a system can behave concurrently and compete for resources. Hence, for simulation-based verification, we need a methodology that allows one to automatically generate test cases for testing concurrent and resource-competing behaviors. We introduce the use of a transfer-resource graph (TRG) as the model for test generation. From a high abstraction level, TRG is able to model the parallelism between heterogeneous interaction forms in a system. We show how TRG is used in generating test cases of resource competitions and how these test cases are structured in event-driven test programs. For coverage, TRG can be converted to a Petri net, allowing one to measure the completeness of concurrency in simulation.

A new implementation for minimally redundant radix-4 SRT division with the recurrence in the signed-digit format is introduced. The implementation is developed based on the comparison multiples idea. In the proposed approach, the quotient digit's magnitude is calculated by comparing the truncated partial remainder with two limited precision multiples of the divisor. The sign is determined by investigating the polarity of the truncated partial remainder. A timing evaluation using logical synthesis shows a latency of 2.34 ns for the recurrence of the proposed divider. It is ∼22% less than the conventional implementation.

This paper presents the design of a low-voltage operational amplifier suitable for sigma-delta (/spl Sigma//spl Delta/) modulator applications. The circuit technique is proposed to enhance the slew-rate performance and correspondingly can help to save the power of the amplifier. The simple circuitry of a current mirror is included with a degenerate resistor that can be selectively shunted. High-speed 2nd-order /spl Sigma//spl Delta/ modulators incorporating this technique is designed in a 0.18-/spl mu/m CMOS technology and power consumption can be saved more than 20% for a power supply voltage of 1.2V.

This paper presents a novel modelling methodology for system-on-chip (SoC) verification based on software techniques. This methodology facilitates the automation of test generation; it also enables the focuses being placed on system-level behaviors such as concurrency and resource-contentions. We have demonstrated the feasibility to generalize heterogeneous interactions systematically and use them as the building blocks to generate complex test-cases of real-world concurrency

In this paper, Rayleigh fading channel with an OFDMA system is modeled as a finite-state Markov channel (FSMC) by partitioning the received signal envelope into several intervals. With the aid of sub-band formation and property of lumpability, the size of feedback information can be reduced. This approach involves reducing an exponentially increased states of Markov channel to multiple lumpable Markov channels. The corresponding state transition probabilities and steady-state probabilities are used to predict channel states information in multiple symbol durations ahead. Some simulation examples are presented to illustrate the capability of the lumpable FSMC in channel prediction.

A new implementation for decimal floating-point (DFP) division is introduced. The algorithm is based on high-radix SRT division The SRT division algorithm is named after D. Sweeney [46], J. E. Robertson [47], and T. D. Tocher [48]. with the recurrence in a new decimal signed-digit format. Quotient digits are selected using comparison multiples, where the magnitude of the quotient digit is calculated by comparing the truncated partial remainder with limited precision multiples of the divisor. The sign is determined concurrently by investigating the polarity of the truncated partial remainder. A timing evaluation using a logic synthesis shows a significant decrease in the division execution time in contrast with one of the fastest DFP dividers reported in the open literature.

We present a hand and face segmentation methodology using color and motion cues for the content-based representation of sign language video sequences. The methodology consists of three stages: skin-color segmentation; change detection; face and hand segmentation mask generation. In skin-color segmentation, a universal color-model is derived and image pixels are classified as skin or nonskin based on their Mahalanobis distance. We derive a segmentation threshold for the classifier. The aim of change detection is to localize moving objects in a video sequences. The change detection technique is based on the F test and block-based motion estimation. Finally, the results from skin-color segmentation and change detection are analyzed to segment the face and hands. The performance of the algorithm is illustrated by simulations carried out on standard test sequences.

For the code division multiple access (CDMA) downlink channel, we investigate the decision feedback equalizer (DFE) at chip-level to effectively suppress multiple-access interference (MAI) when the spreading sequences are orthogonal. The structure of the receiver with the chip-level DFE has been investigated and the minimum mean-square error solution has been derived. Due to the inherent structure of the chip-level DFE, some iterative techniques with hard and soft decisions have been proposed. It is shown that the proposed receivers with the chip-level DFE can provide satisfactory performance. In comparison with the adaptive chip-level linear equalizer, the number of users can be doubled by using adaptive chip-level DFE at a bit-error rate of 10/sup -3/. Throughout the paper, we assume that scrambled orthogonal codes are used for spreading sequences.

In commercial wideband code division multiple access (W-CDMA), the transmitted signal in the downlink channel is spread by orthogonal codes. However, frequency selective fading destroys the orthogonality, which causes multiple access interference (MAI). Although the RAKE receiver can exploit path diversity, it does not restore the orthogonality and suppress the MAI. As a result, CDMA becomes an interference limited system. The equalization followed by despreader can be adopted to restore the orthogonality and then to suppress the MAI, without significantly increasing the complexity. In this paper, we propose to apply a chip level decision feedback equalizer (DFE) with the Alamouti transmit diversity scheme to restore the orthogonality of the received spreading sequences. Theoretical and simulation results show significant performance gains compared to the chip level linear equalizer (LE) and the RAKE receiver.

The paper introduces two innovations in the design of prefix adder carry trees: use of high-valency prefix cells to achieve low logical depth and end-around carry adders with reduced fan-out loading (compared with the carry select and flagged prefix adders). An algorithm for generating parallel prefix carry trees suitable for use in a VLSI synthesis tool is presented with variable parameters including carry tree width, prefix cell valency, and the spacing of repeated carry trees. The area-delay design space is mapped for a 0.25 /spl mu/m CMOS technology for a range of adder widths as a comparative study.

Support vector machines (SVMs) have been successfully applied to classification problems. The difficulty in selecting the most effective error penalty has been partly resolved with /spl nu/-SVM. However, the use of uneven training class sizes, which occurs frequently with target detection problems, results in machines with biases towards the class with the larger training set. We propose an extended /spl nu/-SVM to counter the effects of the unbalanced training class sizes. The resulting dual /spl nu/-SVM provides the facility to counter these effects, as well as to adjust the error penalties of each class separately. The parameter /spl nu/ of each class provides a lower bound to the fraction of support vector of that class, and the upper bound to the fraction of bounded support vector of that class. These bounds allow the control on the error rates allowed for each class, and enable the training of machines with specific error rate requirements.

© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

While asynchronous circuits offer potential advantages over synchronous circuits, particularly in the form of low power and low noise properties, it is widely held that they are more difficult to test. The self-checking properties of semi-modular asynchronous circuits with respect to certain stuck-at faults have been known for many years, but the restrictions have been such that it has not been feasible to make use of this property to enhance testability. In this paper we demonstrate the feasibility of a technique to determine whether a proposed asynchronous circuit implementation is totally self-checking with respect to all output stuck-at-faults. This test can he incorporated into the design process to select a self-checking implementation when several alternatives are available.

The design and implementation of a double precision floating-point IEEE-754 standard adder is described which uses "flagged prefix addition" to merge rounding with the significand addition. The floating-point adder is implemented in 0.5 /spl mu/m CMOS, measures 1.8 mm/sup 2/, has a 3-cycle latency and implements all rounding modes. A modified version of this floating-point adder can perform accumulation in 2-cycles with a small amount of extra hardware for use in a parallel processor node. This is achieved by feeding back the previous un-normalised but correctly rounded result together with the normalisation distance. A 2-cycle latency floating-point adder architecture with potentially the same cycle time that also employs flagged prefix addition is described. It also incorporates a fast prediction scheme for the true subtraction of significands with an exponent difference of 1, with one less adder.

This paper presents a biologically inspired neural model for detecting, locating and recognising all known objects in the visual scene automatically. In particular, this model employs bottom-up segmentation to achieve shifts in spatial attention, for selecting potential regions of interest across the visual scene.

This paper describes the use of attentional modulation of neuronal signals in elementary feature detection. In particular, motion detection can be selectively tuned to favour movements in the desired direction by providing a suitable directional bias in the detection system. Computer simulation results of apparent motion demonstrating the effects of selectively biasing in elementary motion detection are presented.

Support vector training requires the evaluation of a quadratic programming (QP) problem which is computationally intensive. In addition, the size of the QP is dependent on the number of training samples and may exceed the memory size. This paper presents a fast parallel implementation of the SVM on an array processor which is optimised for matrix operations. A decomposition algorithm is used to break large scale support vector problems into a fixed size block for efficient processing in the array.

When developing distributed scheduling algorithms such as communication protocols, issues in achieving optimal policy and minimizing overhead must be addressed. This problem is examined in the context of a specific distributed system-the token ring communication network. Three token ring protocols are considered which are representative of many existing ones in the sense that they incorporate message time constraints at different levels and implement the earliest deadline first transmission (scheduling) policy at different degrees with different overheads. Through a worst-case analysis, the performance of these three token ring protocols is compared. It is concluded that to evaluate a distributed scheduling algorithm such as a communication protocol, it is necessary to not only consider the scheduling policy employed but also to take into account the overhead incurred due to the implementation of the scheduling policy.>