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This article addresses the visual regulation problem of a differential-drive mobile robot with an arbitrarily installed monocular camera in the indoor environment. A three-stage controller is designed by using a novel nonadaptive switching approach, where the unknown image depth and the uncalibrated camera-to-robot translation parameters do not need to be estimated. Convergence of the error systems with the designed controller in each stage is analyzed. Moreover, the existence of the switching time instants from each stage is proved. The simulation results are presented to show the effectiveness of the proposed approach.

The consensus tracking problem means that a group of followers tracks the desired trajectory with local communication. In this article, partial components of cluster consensus have been considered. In this scenario, the p components of the followers in different clusters track the leader at different lag times, while p components of each agent in the same cluster reach a consensus, which is called p components of cluster-lag (PCCL) consensus. By using a seminorm $||x_{i}||_{2,p}$ and a Lyapunov-Krasovskii functional, PCCL consensus for second-order multiagent systems with homogeneous nonlinear systems on cooperative-competitive networks has been considered. For the case that the communication network graph is undirected, a decentralized adaptive controller, which is based on the exchanged neighbors' information from the same cluster, is designed such that all the agents reach PCCL consensus. For the directed graph case, an adaptive protocol based on the intracoupling strength is constructed for each cluster to achieve PCCL consensus. Finally, two simulation examples are illustrated to show the effectiveness of the proposed control protocols.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers. A recursive Kalman filtering algorithm is derived for the FC to periodically generate estimates of state variables describing an object by using a linear continuous-time stochastic model. Two simulation examples are presented to show the effectiveness of the proposed results.

This paper is concerned with the state estimation problem for a class of nonlinear cyber-physical systems (CPSs) where the nonlinear dynamical physical process is approximated by a Takagi–Sugeno fuzzy model. The physical plant is measured by a set of wireless sensors and the sensors communicate with the remote estimator via a communication channel. In the considered CPS, the randomly occurring sensor saturation, signal quantization, packet dropouts as well as the medium access constraint are studied in a unified framework. We develop a sufficient condition such that the filtering error system is asymptotically stable in the mean-square sense and also with a prescribed $\boldsymbol {H_\infty }$ performance level. The filter gain parameters are determined by solving a convex optimization problem. Finally, the simulation study on the networked truck-trailer system is presented to show the effectiveness of the proposed estimator design.

This paper investigates the least-squares estimation problem for networked systems with transmission constraints. A group of sensors are deployed to measure the outputs of a plant and send the measurements to an estimator through a common communication channel. Due to the transmission constraints caused by the heterogenous or long-distance deployed sensors, only one sensor is allowed to transmit its measurement over one time slot. In this regard, a stochastic competitive transmission strategy is proposed to schedule the transmission permissions. By using the least-squares estimation approach, an aperiodic multi-step estimation algorithm is proposed for the estimator to aperiodically generate the estimates. Performance analysis is presented for the estimation system with bounded noises and random noises. An upper bound is derived for the expectation of the estimation error and a sufficient condition is presented to ensure the convergence of the obtained upper bound. An illustrative example is provided to demonstrate the effectiveness of the proposed results.

This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with communication bandwidth constraints. To satisfy finite communication bandwidth, only partial components of the local vector estimation signals are transmitted to the fusion center (FC) at each time step, where multiple binary variables are introduced to model this component transmitting process. A novel compensation strategy is proposed to restructure the untransmitted components of each local estimate at the FC end, and a recursive distributed fusion kalman filter (DFKF) is designed in the linear minimum variance sense. Moreover, a simple suboptimal judgement criterion is proposed to determine a group of binary variables such that the mean square error of the designed DFKF is minimal at each time step. An illustrative example is given to show the effectiveness of the proposed methods.

This study investigates the signal estimation problem in noisy sensor networks with quantised communications. The sensors are subject to random sensor failures, and synchronously take noisy measurements to produce local estimates by using a Kalman filtering scheme at each sampling instant. A quantiser is considered to be embedded in each sensor, and the probabilistic quantisation strategy is adopted to reduce the energy consumption. In between two sampling instants, each sensor collects quantised local estimates from its neighbours and runs a consensus-based fusion algorithm to generate a fused estimate. The process noises and measurement noises are considered to be spatially uncorrelated, a recursive equation is presented to calculate the estimation error covariance matrix and an upper bound is derived for the estimation performance index. Moreover, a sufficient condition for the convergence of the upper bound of the estimation performance index is also presented. Two types of optimisation problems are constructed for cases of infinite and finite recursions, respectively, where the former one focuses on minimising the derived upper bound of the estimation performance index, and the latter one aims to minimise the energy consumption subject to a constraint on the estimation performance. Illustrative examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This study investigates the multi-sensor-based centralised estimation problem in heterogeneous sensor networks with a common communication channel. Owing to the heterogeneity of the distributed sensors, it is usually impossible to package the measurements into one packet and transmit them to the fusion centre (FC) together, which implies that the measurements should be forwarded to the FC asynchronously. In view of this, a novel stochastic competitive transmission strategy is proposed to stagger the sensors’ transmissions. By using the asynchronous sampled information from the sensors, an H ∞ filter is designed for the FC to periodically generate estimates. The filter parameters are determined by solving a linear-matrix inequality. An illustrative example is provided to demonstrate the effectiveness of the proposed theoretical results.

This paper is concerned with the fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements where there are significant uncertainties on information. A unified model is proposed to capture four sources of these uncertainties, namely, the sensor saturation, the signal quantization, the general medium access constraint, and the multiple packet dropouts. A simultaneous consideration of these issues reflects the practical networked systems much more closely than the existing works. The goal of this paper is to design a fault detector such that, for all unknown input, control input, and uncertain information, the estimation error between the residual and the fault is minimized. Using the switched system approach and some stochastic analyses, a sufficient condition for the existence of desired fault detector is established and the fault detector gains are computed by solving an optimization problem. Two numerical examples are given to show the effectiveness of the proposed design.

This paper investigates a group consensus problem with discontinuous information transmissions among different groups of dynamic agents. In the group consensus problem, the agents reach more than one consistent state asymptotically. We consider that the communication topology of these agents, represented by a network, is undirected. Then a novel group consensus protocol, called hybrid protocol, is proposed to solve the couple-group average-consensus problem. The convergence analysis is presented and the algebraic criterions are established. Furthermore, the multi-group consensus is discussed as an extension of the couple-group consensus. By similar techniques, some analysis results are presented. The analysis tools developed in this paper are based on algebraic graph theory, matrix theory, and control theory. Finally, the simulations are provided to demonstrate the effectiveness of the proposed theoretical results.

This paper is concerned with the finite-time quantized H∞ control problem for a class of discrete-time switched time-delay systems with time-varying exogenous disturbances. By using the sector bound approach and the average dwell time method, sufficient conditions are derived for the switched system to be finite-time bounded and ensure a prescribed H∞ disturbance attenuation level, and a mode-dependent quantized state feedback controller is designed by solving an optimization problem. Two illustrative examples are provided to demonstrate the effectiveness of the proposed theoretical results.

We propose a new discrete-time model for genetic regulatory networks (GRNs) in this paper. The new model includes time-varying degradation rates, translation rates of mRNA, and protein as well as time-delay and bounded external noise, which better approximates the practical GRNs. The aim is to estimate the concentrations of mRNA and protein in the proposed model. Due to the unknown external noise and time-varying parameters in this model, the set-values filtering is adopted. The so-called set-values filtering problem is to find an upper bound for the quadratic estimation error of the filtering dynamics and minimize this bound at each time step provided that the external noise is bounded. A recursive linear matrix inequality (RLMI) based optimization approach is developed to compute the filter gains. The effectiveness of the design method is illustrated via two numerical examples.

This paper is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) under the assumptions that (i) distributed sensors have computation capabilities, (ii) the communication between the sensors and the fusion center (FC) is subject to finite communication bandwidth. The communication bandwidth constraint considered is that only partial components of the local vector estimation signals are allowed to be transmitted to the FC at a particular time, and multiple binary variables are introduced to model this component transmitting process. A novel compensation strategy is proposed to restructure the local estimation signal at the FC end, and a recursively distributed fusion kalman filter (DFKF) is designed in the linear minimum variance sense from the restructured local unbiased-estimators. It is shown that the mean-square error (MSE) of the designed DFKF is dependent on the introduced binary variables, and a simply suboptimal judgement criterion is proposed to determine a group of binary variables such that MSE of the designed DFKF is minimal at each time step.

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example.