Round-Robin Protocol Research Articles

Aperiodic event‐triggered robust model predictive control for linear parameter‐varying system with round‐robin protocol

AbstractThis article investigates the robust model predictive control (MPC) problem for networked control systems represented by the linear parameter‐varying model, in which an event‐triggered strategy and the round‐robin (RR) protocol scheduling locate at the sensor‐to‐controller and controller‐to‐actuator channels, respectively. By considering the problems of system state immeasurable and communication burden in engineering application, an output feedback controller that combines the aperiodic event‐triggered strategy is applied, where the triggering condition is designed in a time‐varying fashion. In addition, in order to avoid unexpected data collisions, the RR protocol is utilized to schedule a shared network and guarantee the efficiency of the control system. The controller parameters are obtained by solving an online convex robust MPC optimization problem, and the feasibility of the optimization problem and closed‐loop stability are also addressed. The effectiveness of the proposed theoretical results is illustrated by a numerical simulation example.

Sliding mode control design under multiple nodes round-robin-like protocol and packet length-dependent lossy network

In wireless network applications, packet length is a key factor in affecting packet loss probability, and channel scheduling protocol can effectively reduce packet length by permitting only a few nodes to update their data at each instant. This paper focuses on the sliding mode control (SMC) under the packet length-dependent lossy network. By analyzing the relation between the transmitted nodes number and the packet loss probability, the standard round-robin protocol (RRP) is extended to the multi-node case. Firstly, the multi-node RRP (MN-RRP) is designed under Bernoulli lossy network. After marking the first access node as the token, we construct the token-dependent SMC law to ensure the stability of the closed-loop system and the reachability of the specified sliding surface. Furthermore, we design the network mode-dependent round-robin-like protocol (NMD-RRLP) under Markov lossy network, in which more sensor nodes will be selected under the good network condition, vise versa. Finally, the proposed static output-feedback SMC approach is applied to the RLC circuit system.

Probabilistic-constrained tracking control for stochastic time-varying systems under deception attacks: A Round-Robin protocol

The probabilistic-constrained tracking control issue is investigated for a class of time-varying nonlinear stochastic systems with sensor saturation, deception attacks and limited bandwidth in an unified framework. The saturation of sensors is quantified by a sector-bound-based function satisfying certain conditions, and the random deception attacks are considered and modeled by a random indicator variable. To gain more efficient utilization of communication channels, a Round-Robin (RR) protocol is utilized to orchestrate the transmission order of measurements. The main purposes of this study aim to plan an observer-based tracking controller to achieve the following goals: (1) the related performance indicators of the estimation error is less than given bound at each time step; and (2) the violation probability of the tracking error confined in a predefined scope is supposed to be higher than a prescribed scalar and the area is minimized at each instant. In order to reach these requirements, a group of recursive linear matrix inequalities (RLMIs) are developed to estimate the state and design the tracking controller at the same time. Finally, two simulation examples are exploited to illustrate the availability and flexibility of the proposed scheme.

Distributed Fault Detection and Control for Markov Jump Systems Over Sensor Networks With Round-Robin Protocol

This paper addresses the problem of simultaneous fault detection and control (SFDC) for Markov jump systems (MJSs) over sensor networks. In order to save the bandwidth of the network, round-Robin protocol is adopted for the communication between the sensors and fault detection filter. First, to fully utilize the data of sensor networks, a new distributed system is developed especially for SFDC, and a residual system, which integrates the MJSs with the distributed SFDC system, is also established. Then, by constructing a mode-dependent Lyapunov functional, the conditions for distributed SFDC are derived, which can make the residual system asymptotically mean-square stable and meanwhile satisfy the prescribed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance requirements. Finally, comparison analyses are made by examples to illustrate the effectiveness and superiority of the proposed new design approach.

Multi-sensor Kalman filtering over packet-dropping networks subject to Round-Robin protocol scheduling

This paper considers the filtering problem for a class of linear cyber-physical systems (CPSs) subject to the Round-Robin protocol (RRP) scheduling, where the RRP is adopted to efficiently avoid data collisions in multi-sensor application scenarios. Unlike most of the existing results concerning the scheduling effects of the RRP under reliable communication channels, the filtering problem over packet-dropping networks is investigated. In such a framework, an optimal Kalman-type recursive filter is derived in the minimum mean square error (MMSE) sense, which is different from the suboptimal filters with bounded error covariances proposed in the previous results. Due to the protocol-induced behaviors and the unreliability of the channels, the estimator may be unstable. Thus, the stability problem of the filter is mainly discussed. It can be proved that the filter is stable when the arrival rate of the measurements exceeds a certain threshold, where the threshold can be obtained by solving a quasi-convex optimization problem. Furthermore, a sufficient condition for the existence of the steady-state error covariance is presented and can be transferred into the feasibility of a certain linear matrix inequality (LMI). Finally, a simulation example is provided to demonstrate the developed results.

Output feedback [formula omitted]-gain control of networked control systems subject to round-robin protocol

This paper considers the output feedback L2-gain control problem for continuous-time networked control systems subject to round-robin protocol. The sensors of the considered system are distributed over a network. Round-robin protocol is introduced to schedule the measurement information transmitted from sensors to the controller. Then the closed-loop system is modeled as a kind of time-delay system. According to Lyapunov–Krasovskii method, sufficient conditions are derived to guarantee the exponential stability and the prescribed L2-gain of the closed-loop system. The output feedback controller gains are determined efficiently by solving linear matrix inequalities. Finally, one example is reported to illustrate the advantages of the proposed design scheme.

Recursive State Estimation for Stochastic Complex Networks Under Round-Robin Communication Protocol: Handling Packet Disorders

This paper investigates the recursive state estimation problemfor a class of discrete-time stochastic complex networks with packet disorders under Round-Robin (RR) communication protocols. The phenomenon of packet disorders results from the random transmission delays during the signal propagation process due to the unpredictable fluctuations of the network load, and such random delays are modeled by a set of random variables satisfying certain known probability distributions. For the sake of lessening the communication burden and abating the data collisions, the RR protocol is introduced to govern the order of the nodes for data transmission. Under the scheduling of the RR protocol, only one node is allowed to gain the access to the network at each time instant. Then, a recursive estimator is devised to guarantee an upper bound for the estimation error covariance, and then the obtained upper bound is locally minimized by adequately choosing the estimator parameters. Furthermore, the boundedness of estimation error is analyzed in the sense of mean square with the help of stochastic analysis techniques. At last, a simulation example is presented to show the applicability of the proposed estimator design scheme.

Quantized Sliding Mode Control for Networked Markovian Jump Systems under Round-robin Protocol: The Output Feedback Case

Quantized Sliding Mode Control for Networked Markovian Jump Systems under Round-robin Protocol: The Output Feedback Case

Neural‐network‐based distributed security filtering for networked switched systems

AbstractIn this paper, distributed security filtering is investigated for networked switched systems under the round‐robin protocol (RRP). In order to fully take the practical situation into account, a cyber attack without additional bounded constraint is considered by introducing the neural network approximate technique. Moreover, the scattered sensor nodes are universally utilized to transmit and collect system information. The distributed filtering is proposed for the networked switched systems to estimate the system output. In order to ease data collisions, RRP is taken to schedule signal communication through the shared network. By constructing a token‐dependent and mode‐dependent Lyapunov function, sufficient conditions are given to ensure the filtering error system under cyber attack is finite‐time bounded. The filtering gains are determined by solving a set of linear matrix inequalities. Finally, the availability of the acquired methods is verified by a PWM‐driven boost converter model.

$H_{\infty }$ State Estimation for Coupled Stochastic Complex Networks With Periodical Communication Protocol and Intermittent Nonlinearity Switching

In this paper, an $H_{\infty }$ estimation approach is given for an array of coupled stochastic complex networks with intermittent nonlinearity switching. A set of binary random variables are adopted to characterize the intermittent switching behavior of the involved nonlinearities. To effectively alleviate data collisions and save energy, the Round-Robin protocol is utilized to curb network congestions in data communication. For the coupled stochastic complex networks, we design a protocol-based $H_{\infty}$ estimator that not only resists stochastic disturbances, but also ensures the exponential mean square stability of the desired error system under a given disturbance attenuation level. With the help of the Lyapunov stability and convex optimization theories, sufficient conditions are provided for the expected estimator. Simulations are provided to illustrate the reasonability of our $H_{\infty }$ approach.

$H_{\infty }$ PID Control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delays Under Round-Robin Communication Protocol

This article is concerned with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

Quasisynchronization for Neural Networks With Partial Constrained State Information via Intermittent Control Approach.

This work addresses quasisynchronization (QS) of the master-slave (MS) neural networks (NNs) with mismatched parameters. The logarithmic quantizer and the round-robin protocol (RRP) are used to deal with the limited communication channel (CC) capacity, then the intermittent control strategy is employed to improve the efficiency of CC and the controller. A transmission-dependent controller is designed, and the synchronization error system (SES) is established. The QS with a boundary is ensured for the MS NNs by a developed sufficient condition, and the controller design method is given. A numerical simulation is given to show the effectiveness of the obtained method.

Optimal DoS attack schedules on remote state estimation under multi-sensor round-robin protocol

In this paper, we focus on the design of optimal schedules for denial-of-service (DoS) attacks to degrade the performance of a remote state estimator of a networked system, where measurements of un-computational sensors are transmitted through a band-limited wireless channel under the round-robin protocol. Since attacker only has limited energy to jam the network, the attacker has to determine whether to jam the channel or not at each time step. In this paper, we propose a general error cost function in which average error and terminal error only become its particular cases. Then we analyze the properties of evolution of estimation error covariance under different schedules and further determine optimal attack schedules with respect to our proposed error cost. Examples are provided to illustrate the design of the optimal attack schedules.

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

AbstractIn this article, the Tobit Kalman filtering problem is investigated for a class of discrete time‐varying fractional‐order systems in the presence of measurement censoring and stochastic nonlinearities under the Round‐Robin protocol (RRP). The fractional‐order dynamic model is described by the Grunwald–Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state‐dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol‐based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off‐line. Simulation results are provided to demonstrate the usefulness of the proposed method.

Protocol-Based Tobit Kalman Filter Under Integral Measurements and Probabilistic Sensor Failures

This paper is concerned with the Tobit Kalman filtering problem for a class of discrete time-varying systems subject to censored observations, integral measurements and probabilistic sensor failures under the Round-Robin protocol (RRP). The censored observations are characterized by the Tobit observation model, the integral measurements are described as functions of system states over a certain time interval required for data acquisition, and the sensor failures are governed by a set of uncorrelated random variables. The RRP is employed to decide the transmission sequence of sensors in order to alleviate undesirable data collisions. By resorting to the augmentation technique and the orthogonality projection principle, a protocol-based Tobit Kalman filter (TKF) is developed with the coexistence of integral measurements and sensor failures that lead to a couple of augmentation-induced terms. Moreover, the performance of the proposed filter is analyzed through examining the statistical property of the error covariance of the state estimation. Further analysis shows the existence of self-propagating upper and lower bounds on the estimation error covariance. A case study on ballistic roll rate estimation is presented to illustrate the efficacy of the developed filter.

Federated Tobit Kalman Filtering Fusion With Dead-Zone-Like Censoring and Dynamical Bias Under the Round-Robin Protocol

This paper is concerned with the multi-sensor filtering fusion problem subject to stochastic uncertainties under the Round-Robin protocol (RRP). The uncertainties originate from three sources, namely, censored observations, dynamical biases and additive white noises. To reflect the dead-zone-like censoring phenomenon, the measurement observation is described by the Tobit model where the censored region is constrained by prescribed left- and right-censoring thresholds. The bias is modeled as a dynamical stochastic process driven by a white noise in order to reflect the random behavior of possible ambient disturbances. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. The filtering fusion is conducted via two stages: 1) the sensor observations arriving at its corresponding estimator are first leveraged to generate a local estimate, and 2) the local estimates are then gathered together at the fusion center in order to form the fused estimate. The local estimator implements a Tobit Kalman filtering algorithm on the basis of an enhanced Tobit regression model, whilst the fusion center realizes a filtering fusion algorithm in accordance with the well-known federated fusion principle. The validity of the fusion approach is finally shown via a simulation example.

ℓ2–ℓ∞ proportional–integral observer design for systems with mixed time‐delays under round–robin protocol

AbstractIn this article, the design problem of ℓ2–ℓ∞ proportional–integral observer (PIO) is investigated for a class of discrete‐time systems with mixed time‐delays. The mixed time‐delays comprise both the discrete time‐varying delays and infinitely distributed delays. The round–robin protocol (RRP) is employed to schedule the data transmissions from the sensors to the observer so as to mitigate the communication burden and prevent the data collisions. A novel PIO is developed whose observer gain is dependent on the data transmission order as a reflection of the effects induced by the RRP scheduling. By resorting to the token‐dependent Lyapunov functional and the matrix inequality technique, the desired PIO is designed with exponentially stable error dynamics of the state estimation and guaranteed ℓ2–ℓ∞ disturbance attenuation/resistance capacity. Finally, a simulation example is exploited to verify the validity of the proposed observer design method.

Scalable consensus filtering for uncertain systems over sensor networks with Round‐Robin protocol

AbstractThis article is concerned with the scalable distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems over sensor networks with the Round‐Robin protocol (RRP). The challenge comes from the fact that the time‐varying parameters of the network are subject to randomly occurring norm‐bounded uncertainties and the measurement outputs of the sensor nodes are saturated due to the sector nonlinearities. For preventing data collisions and saving energy, the RRP determines which neighboring node can access the shared network for information transmission at each time step. An H∞ performance index is proposed to characterize the disturbance attenuation level of the resulting filtering error dynamics. By stochastic analysis in combination with the recursive matrix inequality approach, a distributed filtering algorithm is developed for each individual sensor node to ensure the prespecified estimation performance. Finally, an illustrative simulation example is shown to verify the effectiveness and applicability of the theoretical results.

Dwell-time-based energy scheduling and distributed control for large-scale nonlinear systems under Round-Robin protocol

In this paper, a distributed control problem is investigated for large-scale nonlinear systems under Round-Robin (RR) protocol and the energy constraint. The large-scale system is composed of several interconnected nonlinear subsystems that are equipped with independent sensors. RR protocol is adapted to coordinate the data transmission of each sensor from the viewpoint of fairness. Moreover, an energy scheduling scheme is employed in response to the energy constraint by distributing high energy or low energy to sensors. The transmissions (between sensors and controllers) with high energy can be accurate. Correspondingly, the accessed data may be lost with a certain probability when sensors use low energy. This paper aims to design the scheduling scheme and distributed controllers jointly such that the stochastic finite-time boundedness (FTB) can be guaranteed for large-scale nonlinear systems with limited energy and communication protocols over a finite horizon. In light of the energy allocations and the induced packet dropout, novel distributed controllers are established for large-scale systems. Furthermore, the explicit expressions of the controller for each subsystem and the energy scheduling scheme with average dwell time (ADT) are derived by using recursive design methods combined with stochastic analysis. Finally, two simulation examples are given to demonstrate the effectiveness of the designed scheme and controllers.

Efficient Model-Predictive Control for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form Under Round-Robin Protocol

This article is concerned with the efficient model-predictive control (EMPC) problem for interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy systems with hard constraints. By applying the round-robin (RR) protocol, all controller nodes are activated in a pregiven order such that the occurrence of network congestion or data collisions can be effectively reduced. The aim of the proposed problem is to design a fuzzy controller in the framework of EMPC so as to obtain a good balance among the computation burden, the initial feasible region, and the control performance. With respect to the RR protocol and IT2 T-S fuzzy nonlinearities, a unified representation is modeled for the underlying system, and then, an augmentation state comprising of the system state, the token-dependent perturbation, and the previous input under the RR protocol is put forward; correspondingly, objective functions are constructed for the controller design. Subsequently, by using the min–max strategy, some token-dependent optimizations are established to facilitate the formation of the EMPC algorithm, where the feedback gain is designed offline, and the perturbation is calculated by solving an online optimization dependent of the token. Moreover, with the help of the matrix partition technique, the feasibility of the proposed EMPC algorithm and the stability of the underlying IT2 T-S fuzzy system are rigidly guaranteed. Finally, two numerical examples are utilized to illustrate the validity of the proposed EMPC strategy.