Packet Dropouts Research Articles

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

New insights on dissipative control technique for Takagi–Sugeno fuzzy system with variable time-delays and random packet dropouts

New insights on dissipative control technique for Takagi–Sugeno fuzzy system with variable time-delays and random packet dropouts

Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet dropouts

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design. For the stability analysis, a new Squeeze Theorem based method is developed to derive the theoretic results instead of the traditional contraction mapping principle used in model-free adaptive control. It is shown that the consensus can be achieved for both leaderless and leader-following cases if the communication topology is strongly connected. Finally, numerical simulations verifying the correctness of the theoretical results are given.

Robust Model Free Adaptive Predictive Control for Wastewater Treatment Process With Packet Dropouts.

External disturbances and packet dropouts will lead to poor control performance for the wastewater treatment process (WWTP). To address this issue, a robust model-free adaptive predictive control (RMFAPC) strategy with a packet dropout compensation mechanism (PDCM) is proposed for WWTP. First, a dynamic linearization approach (DLA), relying only on perturbed process data, is employed to approximate the system dynamics. Second, a predictive control strategy is introduced to avoid a short-sighted control decision, and an extended state observer (ESO) is used to attenuate the disturbance effectively. Furthermore, a PDCM strategy is designed to handle the packet dropout problem, and the stability of RMFAPC is rigorously analyzed. Finally, the correctness and effectiveness of RMFAPC are verified through extensive simulations. The simulation results indicate that RMFAPC can significantly reduce IAE by 0.0223 and 0.1976 in two scenarios, regardless of whether the expected value remains constant or varies. This comparison to MFAPC demonstrates the superior robustness of RMFAPC against disturbances. The ablation experiment on PDCM further confirms its capability in handling the packet dropout problem.

Performance Limitations of Network Control Systems Under Channel Delay and Packet Dropouts

Performance Limitations of Network Control Systems Under Channel Delay and Packet Dropouts

Interval Type-2 Fuzzy Sampled-Date Control for Nonlinear Systems with Packet Dropouts via a Switched System Approach

Interval Type-2 Fuzzy Sampled-Date Control for Nonlinear Systems with Packet Dropouts via a Switched System Approach

LQG control for networked control systems with packet dropouts and input delay: UDP case

LQG control for networked control systems with packet dropouts and input delay: UDP case

Cluster synchronization of complex dynamic networks under pinning control via a limited capacity communication channel

In this paper, the cluster synchronization pinning control problem is investigated for the continuous-time-directed complex dynamic networks under the bit rate constraint. Firstly, a node selection scheme based on differences in node degrees is proposed to achieve cluster synchronization. Subsequently, a coding–decoding scheme is designed during data transmission through digital communication channels where only a limited number of encoded signal sequences are sent to the controller side. Further, regarding the unreliability of the channel, the phenomenon of packet dropouts is considered during the encoding process, which is modeled by using a series of independent Bernoulli distributed random variables. The pinning controller is then designed based on the received decoding information. By means of the convex optimization method, a sufficient condition is established to ensure that the cluster synchronization error is ultimately bounded and the desired controller gain is determined based on the solution to matrix inequalities. Finally, the effectiveness of the obtained results is validated through a simulation example.

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts

This paper explores sampled-data control for Markovian switching neural networks (MSNNs) with dynamic output quantization and packet dropouts. The primary goal is to construct a multi-mode, quantized sampled-data controller that ensures stochastic stability and H∞ disturbance-reduction performance of the closed-loop MSNN. A Bernoulli-distributed random variable with uncertain probability is introduced to characterize the incidence of packet dropouts. To describe potential mode inconsistencies that may occur between the MSNN and controller, an exponential hidden Markov model is employed. Furthermore, the quantizer’s dynamic scaling factor is intentionally built as a piecewise function to avoid the potential division-by-zero problem. A sufficient condition for stochastic stability and H∞ disturbance-reduction performance is proposed, utilizing a mode- and time-dependent Lyapunov-type functional and several stochastic analysis tools. Then, through decoupling nonlinearities, a numerically efficient approach for determining the desired controller gains and parameter range associated with the dynamic scaling factor is developed. In order to facilitate comparisons, the situation with no uncertainty in the probability of packet dropouts is studied, and both analysis and design approaches are offered. Finally, two simulation examples are provided to validate the effectiveness and applicability of the developed approaches.

Fault detection for saturated nonlinear system subject to redundant channel and additive time‐varying delays

AbstractUnder the influence of sensor saturation and packet dropout, this paper is concerned with the fault detection (FD) problem for a class of nonlinear systems with two additive time‐varying delay components. In the addressed measurement model, the sensor is assumed to have two communication channels, which can contribute to improving the probability of successfully delivering the measurements. A FD filter is designed to generate residual signals, and the sufficient criteria are established to ensure the stochastic finite‐time stability (SFTS) of the residual system with performance constraint by constructing new Lyapunov‐Krasovskii functionals. Based on the established criteria, the explicit expression of the desired FD filter is obtained via solving a set of linear matrix inequalities (LMIs). Finally, the usefulness of the proposed FD scheme is verified by a simulation example.

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Consensus control and initialization region optimization for leader‐following multi‐agent systems under time‐varying communication delay and consecutive packet dropouts

Consensus control and initialization region optimization for leader‐following multi‐agent systems under time‐varying communication delay and consecutive packet dropouts

Fault Tolerant Control of Fuzzy Stochastic Distribution Systems With Packet Dropout and Time Delay

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

$H_\infty$ Filtering of Fuzzy Impulsive Switched Systems With Multipath Quantizations and Packet Dropouts via Multiple Hybrid Strategies

$H_\infty$ Filtering of Fuzzy Impulsive Switched Systems With Multipath Quantizations and Packet Dropouts via Multiple Hybrid Strategies

Observer-Based Reachable Set Synthesis for Discrete-Time Periodic Piecewise Systems Subject to Multiple Packet Dropouts

Observer-Based Reachable Set Synthesis for Discrete-Time Periodic Piecewise Systems Subject to Multiple Packet Dropouts

Recursive state estimation for two-dimensional systems over decode-and-forward relay channels: A local minimum-variance approach

The paper is concerned with the recursive state estimation problem for a class of two-dimensional systems over decode-and-forward relay channels subject to packet dropouts. Taking into account the sensor's limited transmission ability, a decode-and-forward relay is deployed between the sensor and the remote estimator to enlarge the propagation distance and improve the communication quality. The packet dropout phenomenon is described by two sequences of mutually uncorrelated random variables that obey Bernoulli distributions. The main objective of this paper is to design a recursive state estimator for the underlying system with guaranteed estimation performance. An upper bound on the estimation error variance is firstly derived by resorting to some coupled difference equations, and then such an upper bound is locally optimized at each iteration through proper design of the estimator gain. Furthermore, the impacts of the packet dropouts and the decode-and-forward relay on the estimation performance are evaluated with rigorously theoretical analysis. Finally, an illustrative example is presented to validate the effectiveness of the proposed estimation scheme.

A switched system approach to exponential stabilization of interval type-2 fuzzy nonlinear systems with packet dropouts

This paper investigates the exponential stabilization problem for interval type-2 (IT2) fuzzy system with parameter uncertainties and packet dropouts. The sample-and-hold characteristic is simulated by employing an input delay methodology, and we present a model for the packet dropout phenomenon using switched approach. Some novel two-side delay-dependent looped-functionals are proposed by considering state information in the intervals from tr to t and t to tr+1 which relaxes the monotonic constraint of the Lyapunov–Krasovskii functional (LKF) and enhances the utilization of the actual sampling pattern. Additionally, some free-weighting matrices, slack matrices, the bound information in membership functions (MFs) and the signs of the first-order time derivative of fuzzy MFs are used to obtain a novel relaxed sufficient conditions for exponential stabilization of interval type-2 fuzzy systems (IT2 FS). Based on the LKF theory, new relaxed sufficient conditions with linear matrix inequalities (LMIs) constraints are derived. According to the criterion, sampled-data controller is devised to ensure that the closed-loop system is exponential stabilization. Finally, an illustrative example is presented to demonstrate the practicality and effectiveness of the method proposed. Comparative analysis highlights the simplicity and practicality of the suggested algorithm.

Recursive Bayesian Estimation for Discrete-Time Systems With State-Dependent Packet Dropouts: A Cross-Coupled Method

Recursive Bayesian Estimation for Discrete-Time Systems With State-Dependent Packet Dropouts: A Cross-Coupled Method

Energy management for object tracking under the energy-harvesting: Hierarchical reinforcement learning method

This paper explores the target localization problem with signal transmitters which are powered by energy harvesting (EH) devices. Due to the remote transmission and random energy harvesting, the energy supplied to transmitters to transmit information is often insufficient, resulting in packet dropout. The rate of packet dropouts is influenced mainly by the distance from the target to the transmitter and transmission energy. Therefore, this paper aims to investigate the energy allocation policies to ensure the desired positioning performance using a two-level hierarchical framework. Initially, a lower-level policy works on a specified time scale and aims at minimize error covariances with prescribed distance to meet the step objective. Subsequently, a higher-level policy plans the step time scale and calls for the desired step objectives for the lower-level policy. Both policy levels are trained through deep reinforcement learning. Finally, an example is presented to illustrate the efficiency of the designed strategy.