Modelling and analysis of display terminal at a finite user resource sharing node in distributed computer systems

An intelligent display terminal system at a finite user resource sharing node in distributed computer systems is modelled as a queueing model with finite microcomputer memory, burst Erlang arrivals from the display terminal cathode-ray-tube, constant service-time, synchronous transmission and single server interrupted through a first-order Markov process. A new generalized method differs from the usual stage and summing method used by the previous research-workers is presented to calculate burst Erlang arrival probabilities. The display terminal microcomputer memory performance is studied for finite and infinite cases and the expressions for overflow probabilities and average queueing delays are derived using the recursive method developed for calculating steady state probabilities of the microcomputer memory states. An integrated voice-data system is presented as a probable application of the model at remote areas and some of its computed values are portrayed on graphs to be useful in calculating response times of remote display terminals sharing voice channels for transmission purpose.     

Event-triggered H∞ state estimation for state-saturated complex networks subject to quantization effects and distributed delays

This paper is concerned with the event-triggered H_∞ state estimation problem for a class of discrete-time complex networks subject to state saturations, quantization effects as well as randomly occurring distributed delays. A series of Bernoulli distributed random variables is utilized to model the random occurrence of distributed delays. For the energy-saving purpose, an event-triggered mechanism is proposed to decide whether the current quantized measurement should be transmitted to the estimator or not. For the state-saturated complex networks, our aim is to design event-triggered state estimators that guarantee both the exponential mean-square stability of and the H_∞ performance constraint on the error dynamics of the state estimation. Stochastic analysis is conducted, in combination with the Lyapunov functional approach, to derive sufficient conditions for the existence of the desired estimators whose gain matrices are obtained by solving a set of matrix inequalities. An illustrative example is exploited to show the usefulness of the estimator design algorithm proposed.     

Data-driven discrete terminal sliding mode decoupling control method with prescribed performance

This paper proposes a data-driven terminal sliding mode decoupling controller with prescribed performance for a class of discrete-time multi-input multi-output systems in the presence of external disturbances and uncertainties. First, utilizing a discrete-time extended state observer and a compact form dynamic linearization data model, we derive a new data-driven mothod and establish the relationship between the input and output signals of controlled plant. Moreover, the disturbances, uncertainties, and couplings are suppressed owing to the application of the terminal sliding mode technique. Combined with the principle of prescribed performance control, the terminal sliding mode law with prescribed performance is derived. With the proposed data-driven method, the tracking error is lower, and the decoupling ability is improved. Furthermore, the stability of the control system is proven. Finally, a simulation is conducted on a three-tank system to demonstrate the effectiveness of the proposed scheme.     

Event-triggered dissipative state estimation for Markov jump neural networks with random uncertainties

This paper is concerned with the problem of event-triggered dissipative state estimation for Markov jump neural networks with random uncertainties. The event-triggered mechanism is introduced to save the limited communication bandwidth resource and preserve the desired system performance. The phenomenon of randomly occurring parameter uncertainties is considered to increase utilizability of the proposed method. To describe such a randomly occurring phenomenon, some mutually independent Bernoulli distributed white sequences are adopted. A mode-dependent state estimator is designed in this paper, which ensures that the estimation error system is extended stochastically dissipative. By using the Lyapunov–Krasovskii functional approach and an optimized decoupling approach, an expected state estimator can be built by solving some sufficient conditions. Two numerical examples are presented to demonstrate the correctness and effectiveness of the proposed method.     

A Markovian system approach to distributed H∞ filtering for sensor networks with stochastic sampling

This paper is concerned with the distributed H_∞ filtering problem for a class of sensor networks with stochastic sampling. System measurements are collected through a sensor network stochastically and the phenomena such as random measurement missing and quantization are also considered. Firstly, the stochastic sampling process of the sensor network is modeled as a discrete-time Markovian system. Then, the logarithmic quantization effect is transformed into the parameter uncertainty of the filtering system, and a set of binary variables is introduced to model the random measurement missing phenomenon. Finally, the resulting augmented system is modeled as an uncertain Markovian system with multiple random variables. Based on the Lyapunov stability theory and the stochastic system analysis method, a sufficient condition is obtained such that the augmented system is stochastically stable and achieves an average H_∞ performance level γ; the design procedure of the optimal distributed filter is also provided. A numerical example is given to demonstrate the effectiveness of the proposed results.     

Moments generating function of the harmonic mean of two non-identical gamma random variables and its applications in wireless communications

In this paper, a closed-form expression for the moments generating function of the half-harmonic mean of two independent, not necessarily identically distributed gamma random variables with arbitrary parameters is presented. This statistical result is useful to the performance analysis of dual-hop wireless communication systems with amplify-and-forward relays in a Nakagami-m fading environment. The proposed mathematical analysis is substantiated by various numerically evaluated and computer simulation results.     

Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models.     

Active vibration control of an elastically connected double-string continuous system

The paper deals with the optimal control of a distributed host structure consisting of two elastically connected complex continuous double-string system and subjected to certain excitation load. Investigation of the behavior of such system is of great theoretical and practical importance. A technique is proposed to actively damp out the undesired vibrations in the structures by a combination of applied actuators and displacement feedback gains. Two performance measures, involving energies at the terminal time as well as applied and feedback control efforts, are introduced. The optimality conditions of the applied actuators are derived by using the method of eigenfunction expansion and calculus of variations. The feedback parameters are numerically determined from the solution of a minimization problem. The proposed approach is illustrated by a numerical example involving a system which consists of two strings subjected to a continuous load.     

Massive parallelism on the hybrid text-retrieval machine

The design of a high-performance, cost-effective, machine for retrieving textual data is discussed in this paper. High performance and cost effectiveness are achieved by a combination of low-cost hard disks, software filtering techniques, and a large amount of main memory. The discussion focuses on the signature processor, which is based on the partitioned signature file technique, and the mass storage system, which is based on a disk array. A performance evaluation on the individual system components, namely, the signature processor and the mass storage system, as well as the entire system is presented.     

Finite-horizon H∞-consensus control for multi-agent systems with random parameters: The local condition case

This paper is concerned with the distributed H_∞-consensus control problem over the finite horizon for a class of discrete time-varying multi-agent systems with random parameters. First, by utilizing the proposed information matrix, a new formula is established to calculate the weighted covariance matrix of random matrix. Next, by allowing every agent to track the average of the neighbor agents, a novel local H_∞-consensus performance constraint is presented to cater to the local performance analysis. Then, by means of the proposed definition of the stochastic vector dissipativity-like over the finite horizon, a set of sufficient conditions for every agent is obtained such that the controlled outputs of the closed-loop multi-agent systems satisfy the proposed H_∞-consensus performance constraint. As a result, the proposed consensus control algorithm can be executed on each agent in an indeed distributed manner. Finally, a simulation example is employed to verify the effectiveness of the proposed algorithm.     

Generating model of a sub-Rayleigh fading

The generating model of a narrow band random process with the envelope probability density function left shifted relative to the Rayleigh law is presented. The model is based on the interpretation of the process as a stationary output of a nonlinear dissipative system, excited by white Gaussian noise. This representation may be considered as an effective tool for simulating a wireless communication channel with essentially severe fading.     

Distributed event-triggered control for networked control systems with stochastic cyber-attacks

This paper is concerned with the problem of distributed event-triggered controller design for networked control systems (NCSs) with stochastic cyber-attacks. A decentralized event-triggered scheme is introduced to save the energy consumption and alleviate the transmission load of the network. Each sensor can make its own decision to determine whether the sampled data is delivered to the network or not. By taking two kinds of random cyber-attacks into consideration, a novel mathematical model is constructed for distributed event-triggered NCSs. Sufficient conditions which can guarantee the stability of the control system are obtained by applying Lyapunov stability theory, and the design method of the controller gain is presented in an exact expression. Finally, an example is given to demonstrate the effectiveness of the proposed method.     

An exact closed-form formula of collision probability in diverse multiple access communication systems with frame slotted aloha protocol

For diverse multiple access communication systems based on frame slotted aloha (FSA) protocol, it is important to analyze collision probability for the system performance evaluation. As shown in the literature, for general settings, it is difficult to derive an exact and closed-form solution for collision probability without approximation. Recently, an exact solution based on generic analytical approach (GAA) [31] has been proposed, yet its numerical computation will become difficult when the number of slots is larger than 16. In this paper, we develop an exact closed-form formula (ECFF) for collision probability that can not only overcome the computational deficiency of GAA in the presence of a large number of slots, but also reduce the computation complexity of collision probability. Surprisingly, by introducing a differentiation operator to form a hybrid recursive equation and applying various algebraic properties of Laplace transform and Z transform, the final collision probability can be represented by a compact double summation. Accuracy of the ECFF and comparison with the GAA have been studied by Monte Carlo simulation.     

An event-triggered approach to distributed H∞ state estimation for state-saturated systems with randomly occurring mixed delays

In this paper, the event-triggered distributed H_∞ state estimation problem is investigated for a class of state-saturated systems with randomly occurring mixed delays over sensor networks. The mixed delays, which comprise both discrete and distributed delays, are allowed to occur in a random manner governed by two mutually independent Bernoulli distributed random variables. In order to alleviate the communication burden, an event-triggered mechanism is utilized for each sensor node to decide whether or not its current information should be broadcasted to its neighbors. The aim of this paper is to design event-triggered state estimators such that the error dynamics of state estimation is exponentially mean-square stable with a prescribed H_∞ performance index. By resorting to intensive stochastic analysis, sufficient conditions are first derived to guarantee the existence of the desired estimators, and the parameters of the desired distributed estimators are then obtained in light of the feasibility of a certain set of matrix inequalities. A numerical example is employed to illustrate the usefulness of the proposed distributed estimation algorithm.     

Event-triggered passive control for Markovian jump discrete-time systems with incomplete transition probability and unreliable channels

This paper investigates the passivity of Markovian jump discrete-time systems (MJDTSs) with channel fading via event-triggered state feedback control. First, the concerned MJDTSs contain infinitely distributed delays and switching rules with partially known transition probability (TP) information. Next, the fading channel, as an unreliable channel, is introduced into MJDTSs to better reflect the engineering practice in networked environment. Due to the present of channel fading, a series of random variables satisfying some certain probability density functions (PDFs) will be obstacles in the process of proof. Then, an event-triggered controller is designed for MJDTSs with channel fading and incomplete transition probability (ITP) for the first time. Thanks to this event-triggered mechanism, the state feedback control could greatly reduce energy consumption during transmission. Subsequently, under the above controller, we obtain some novel sufficient criteria in the form of linear matrix inequalities (LMIs) to ensure the passivity of closed-loop system. Finally, some simulation results are provided to demonstrate the feasibility and effectiveness of the proposed theoretical method.     

Adaptive fuzzy CMAC-based nonlinear control with dynamic memory architecture

An adaptive fuzzy cerebellar model articulation controller-based (CMAC) nonlinear control with the advantage of architecture learning is proposed. To cope with the tradeoff between the complexity of CMAC architecture and the quality of system convergence, a dynamic architecture learning scheme is introduced, where the associative memory reinforcement and the associative memory reorganization are involved. In the memory reinforcement process, new associative memories will be generated when the memory cells in the current architecture are found insufficient. On the other hand, the inefficient memories will be detected and reorganized in the memory reorganization process. With the proposed approach, the task of fuzzy CMAC architecture determination by preliminary knowledge or trials can be freed when a well-organized and well-parameterized CMAC is represented to achieve desired approximation performance. Thus, with the proposed CMAC, a dynamic control approach is presented. In this paper, according to the adaptive control theory, the fuzzy CMAC (FCMAC) is utilized as the main controller to mimic the ideal computation controller and a supervisory controller is designed to compensate the approximation error. In the FCMAC, all the controller parameters are online tuned based on the Lyapunov stability theorem such that the stability of closed-loop system can be guaranteed. Simulation results and comparisons are presented for verification.     

Distributed event-triggered state estimation and fault detection of nonlinear stochastic systems

This paper is devoted to investigate the designs of the event-based distributed state estimation and fault detection of the nonlinear stochastic systems over wireless sensor networks (WSNs). The nonlinear stochastic systems as well as the filters corresponding to the multiple sensors are represented by interval type-2 Takagi–Sugeno (T–S) fuzzy models. (1) A new type of fuzzy distributed filters based on event-triggered mechanism is established corresponding to the nodes of the WSN. (2) The overall stability and performance, that is mean-square asymptotic stability in H_∞ sense, of the event-driven fault detection system is analyzed based on Lyapunov stability theory. (3) New techniques are developed to cope with the problem of parametric matrix decoupling for solving the distributed filter gains. (4) Finally, the desired event-based distributed filter matrices are designed subject to the numbers of the fuzzy rules and a series of matrix inequalities. A simulation case is detailed to show the effectiveness of the presented event-based distributed fault detection filtering scheme.     

Cochannel interference effect on BEP performance of SSC receiver in correlated Rician fading

We study the performance of dual branch switch and stay combining (SSC) diversity receivers operating over correlated Rician fading channels, in the presence of correlated Rayleigh distributed cochannel interference (CCI). Fast convergent infinite series representations for both the joint probability density function (PDF) of the SSC input signal-to-interference ratios (SIRs) and the PDF of the SSC output SIR are derived. The last one is applied to semi-analytically study the average bit error probability (ABEP) of differential binary phase shift keying (DBPSK). Furthermore, the optimum switching threshold in the sense of minimum ABEP is obtained numerically and the effects of fading severity and branch correlation on both ABEP and optimum switching threshold are investigated. Numerical and simulation results are presented to support the mathematical analysis.     

Exponential stability of switched positive systems with unstable modes and distributed delays

This paper studies the exponential stability of switched positive system consisting of unstable subsystems with distributed time-varying delay. Unlike the existing results concerning delays, switching behaviors dominating the system can be either stabilizing or destabilizing. The distributed delay is supposed to be slowly varying and upper-bounded. To tackle the difficulties brought by both the switching behaviors with mixed effects and the distributed delay, a multiple discretized Lyapunov–Krasovskii functional is employed to derive sufficient conditions for the exponential stability of the system. Specifically, by adjusting the ratio of the stabilizing switching behaviors, the state divergence caused by unstable subsystems and destabilizing switching behaviors can be compensated. Simulation examples demonstrate the effectiveness of the results.     

Distributed Kalman filtering over sensor networks with fading measurements and random link failures

This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method.