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In this paper, output-based event-triggered tracking control for a networked pneumatic muscle actuators system with packet disorders is investigated. An output-based event-triggered strategy is adopted to avoid the transmission of abundantly redundant data and alleviate the network congestion. Meanwhile, packet disorders frequently occur in the data transmission process in the network environment, which will damage the tracking performance of the networked pneumatic muscle actuator system. A packet reordering method is proposed to deal with the packet disorders and to select the latest control voltage signal, considering the control voltage signal update period is variable under the output-based event-triggered strategy. Besides, an active disturbance rejection controller is designed to observe and compensate the lumped disturbance composed of the nonlinear flow characteristics of the pressure proportional valves and the disturbance caused by the mechanical mechanism. Then a sufficient condition is given to ensure input-to-state stability of the closed-loop system. Finally, extensive experiments are carried out to verify the applicability of the proposed packet reordering method and the tracking performance of the system. [ABSTRACT FROM AUTHOR]

This paper is concerned with the recursive state estimation (RSE) problem under minimum mean-square error sense for a class of nonlinear complex networks (CNs) with uncertain inner coupling, random link failures and packet disorders. Firstly, a set of random variables obeying the Bernoulli distribution is adopted to characterize whether there are connections between different network units, i.e., there is no random link failure when the random variable is equal to 1, otherwise the random link failure occurs. In addition, the inner coupling strength is assumed to be varying within a given interval and the phenomenon of packet disorders caused by the random transmission delay (RTD) is also taken into account. In our study, the nonlinearity satisfies the continuously differentiable condition, which can be linearized by resorting to the Taylor expansion. The focus of the addressed RSE problem is on the design of an RSE approach in the mean-square error sense such that, for all uncertain inner coupling, random link failures and packet disorders, a suboptimal upper bound of the state estimation error covariance is obtained and minimized by parameterizing the state estimator gain with explicit expression form. Furthermore, a sufficient condition with respect to the uniform boundedness of state estimation error in mean-square sense is elaborated. Finally, a numerical experiment is introduced to demonstrate the validity of the presented RSE approach. • The phenomenon of random transmission delay induced by the packet disorders is handled. • A new optimized recursive state estimation scheme is developed against packet disorders. • A sufficient criterion is given to ensure the uniformly boundedness of state estimation error. [ABSTRACT FROM AUTHOR]

• The extended Kalman filtering problem is studied for discrete-time systems. • Random transmission delays and packet disorders are considered. • A novel filter structure is proposed that compensates the random delays. • An upper bound is obtained for the filtering error covariance. • The upper bound is minimized in the sense of trace. This paper studies the extended Kalman filtering problem for a class of nonlinear discrete-time systems with random transmission delays (RTDs) and RTD-induced packet disorders. The relationship between the RTDs and the resulting packet disorders is discussed. The bounded RTDs, which take place in the sensor-to-filter channel, are modeled as independent and identically distributed random variables obeying a certain probability distribution. A novel filter structure is proposed that utilizes an integer-valued function of the mathematical expectation of the RTDs so as to compensate the RTD-induced effects. Under the proposed extended Kalman filter, an upper bound for the filtering error covariance is derived by solving two Riccati-like difference equations, and subsequently minimized (in the sense of trace) by appropriately designing the filter gains. A numerical simulation is provided to verify the validity of the developed filter design scheme. [ABSTRACT FROM AUTHOR]

• A distributed filter is designed for time-varying systems over sensor networks. • Stochastic bias is governed by a dynamical equation. • Transmission delays are present in communication channels • A distributed yet recursive filter is constructed to account for packet disorders. • Upper bounds on error covariances are given and minimized. In this paper, a distributed filter is designed for time-varying systems corrupted by dynamic bias and packet disorders over sensor networks, where the plant under consideration includes stochastic bias which is governed by a dynamical equation. Moreover, the transmission delays are present in all sensor-to-filter communication channels, and such delays are described by using random variables that have known probability distributions. We focus on constructing a distributed yet recursive filter under the corruption of dynamic bias plus packet disorders. By means of the inductive method, upper bounds (on attained error covariances of the distributed filter) are first given and later minimized by properly parameterizing filter gains. Subsequently, a sufficient condition is presented to rigorously ensure the mean-square boundedness with respect to attained filtering errors. Finally, an example is given for effectiveness validation. [ABSTRACT FROM AUTHOR]

• A novel distributed filtering compensation algorithm is presented in terms of the available transmitted data. • The expression form of minimized upper bound of filtering error covariance matrix and the desired filter gain are established. • The filtering algorithm performances are analyzed by providing the theoretical proofs regarding the boundedness. This paper investigates the distributed filtering (DF) issue for a class of time-varying state-saturated systems with packet disorders (PDs) via the event-triggered communication mechanism (ETCM). The random transmission delays, which can be characterized by a set of random variables obeying certain probability distribution, cause the phenomenon of PDs. In addition, for the sake of decreasing the consumption of network resources, the ETCM is used to arrange the data transmission. On the basis of the measurable data information, a novel distributed filter is constructed that can ensure the existence of the upper bound matrix (UBM) regarding the filtering error (FE) covariance and then the trace of the presented UBM is minimized via constructing the suitable filter gain. Afterwards, the boundedness analysis of FE dynamics is provided to evaluate the filtering algorithm performance. Finally, the validity of the designed DF scheme is illustrated through a numerical experiment. [ABSTRACT FROM AUTHOR]

• The recursive filtering problem is studied for stochastic parameter systems. • Quantization effects and packet disorders are considered. • Desired upper bounds on the resultant filtering error covariances are guaranteed. • The obtained upper bounds are minimized by acquiring appropriate filter gains. • Sufficient conditions are established to ensure the mean-square boundedness of filtering errors. In this paper, the recursive filtering problem is put forward for stochastic parameter systems subject to quantization effects and packet disorders. Before entering communication networks, measurement outputs are quantized by logarithmic quantizers. The packet disorders result from transmission delays which are provoked by communication constraints and occur randomly in the sensor-to-filter channel. In case of measurement quantizations and packet disorders, the objective of this paper is to devise a novel recursive filter approach that is capable of 1) guaranteeing desired upper bounds on the resultant filtering error covariances; and 2) minimizing such upper bounds by acquiring appropriate filter gains. Furthermore, sufficient conditions are established to ensure the mean-square boundedness of filtering errors by means of stochastic analysis techniques. At last, simulations are given to validate the applicability of our designed approach. [ABSTRACT FROM AUTHOR]