This article studies the min-consensus control of continuous-time real-valued multiagent systems, with sampled information, quantized communication, and switching topologies. Due to the limited bandwidth of the digital communication network, only finite-bit binary symbolic sequence can be exchanged among the agents. In order to realize the min-consensus control with quantized communication and limited bandwidth, a novel finite-level biased quantizer and a nonstrict decreasing scaling function are designed, and correspondingly a set of switching encoders and decoders are constructed. By means of the proposed encoders and decoders, the according sampled-data min-consensus control inputs are carefully constructed, and the memory variables are introduced into the control inputs and are monotonically decreasing no matter how the communication topology is switched. The proposed encoding-decoding-based control scheme can achieve accurate min-consensus with limited bandwidth, as long as the communication graphs are jointly strongly connected. The numerical simulations show the effectiveness of the proposed control scheme.

This article studies the appointed-time attitude tracking control of the spacecraft on the special orthogonal group, with the attitude forbidden zone, the parameter uncertainties, and the external disturbances. A novel projection function is proposed, such that the normalized boresight vector of the sensitive instrument is mapped to a reduced dimensional vector in the Euclidean space. If the reduced dimensional vector is uniformly bounded, the constraint on the attitude forbidden zone will be satisfied at all the time. By virtue of the designed reduced dimensional vector and the associated auxiliary vectors, a set of vector-based error functions, the appointed-time performance constraints, and the according switching law is carefully constructed. The proposed vector-based adaptive control scheme ensures that the spacecraft attitude can satisfy the attitude constraint and appointed-time control performance simultaneously, in the presence of parameter uncertainties and external disturbances. Simulation results show the effectiveness of the designed control scheme.

This article investigates the networked multiple one-link flexible manipulators system communicating through a signed digraph, in which the dynamic of each submanipulator is described by the PDE-ODE hybrid model. A distributed boundary control protocol is proposed to realize bipartite consensus of joint angles and vibration suppression. To overcome the flexible dynamics, novel variables are introduced in the protocol to suppress the vibrations. The single zero eigenvalue brought by the Laplacian matrix is ignored for avoiding singularity, and the exponential stability of the reduced-order system is strictly proven by means of semigroup theory. Based on the estimations of integral terms in the novel variables, the necessary and sufficient conditions are finally obtained. Numerical simulations are presented to illustrate the effectiveness of the proposed protocol.

In multitarget tracking systems, it is usually assumed that each measurement is generated with respect to a single target. This is not always true for generating relative state measurements or cross-target information in a coupled fashion. This note is concerned with the problem of distributed filtering for multitarget tracking systems with coupled measurements. By representing the coupling features of the target states in the measurements as a directed graph, a modified Kalman consensus filter (KCF) is proposed for a target-dependent augmented system whose state vector consists of in-going neighborhood targets. To analyze the performance of the modified KCF in a directed graph, a sufficient condition is derived to guarantee the boundedness of the estimation errors in the mean square sense. Numerical studies are provided to verify the applicability of the KCF.

This article focuses on trajectory planning of the free-floating space manipulator (FFSM), so that the end-effector trajectory tracking and the spacecraft attitude stabilization are achieved simultaneously. A novel spacecraft attitude stabilization constraint with the time-decaying term and the time-varying gaining parameter is constructed, such that not only is the designed constraint satisfied at the initial instant but also the spacecraft attitude can converge into the small neighborhood of the desired attitude. Two constraints on joint accelerations are constructed, such that the constraints on joint angles/velocities/accelerations and the requirement on the end-effector trajectory tracking are both satisfied. Besides, for the cost function with the control efforts and the manipulability optimization, it can be equivalently converted as a strictly convex quadratic function. Correspondingly, the trajectory planning problem of the FFSM at the acceleration level can be formulated as a constrained convex quadratic programming problem. The proposed trajectory planning algorithm avoids the dynamic singularity of the FFSM. The effectiveness of the proposed algorithm is validated by the simulation results.

We provide new criterion and a class of strict Lyapunov functions (SLFs) for time-varying systems (TVSs) with zero homogeneity. The definition of homogeneous auxiliary system is given, where it is assumed that certain homogeneous functions are admitted with their derivatives, in terms of the error systems, bounded by periodic functions. Based on the homogeneous auxiliary system, sufficient conditions of uniform asymptotical stability for TVSs are formulated using the homogeneity framework. Unlike existing results, where non-SLFs or persistence of excitation condition are required, our criterion is greatly relaxed for broad classes of systems. The utility of our result is illustrated by case-study of pendulum stability with quasi-periodical frictions.

This paper studies the state estimation problem for nonlinearly coupled complex networks. A variance-constrained state estimator is developed by using the structure of the extended Kalman filter, where the gain matrix is determined by optimizing an upper bound matrix for the estimation error covariance despite the linearization errors and coupling terms. Compared with the existing estimators for linearly coupled complex networks, a distinct feature of the proposed estimator is that the gain matrix can be derived separately for each node by solving two Riccati-like difference equations. By using the stochastic analysis techniques, sufficient conditions are established which guarantees the state estimation error is bounded in mean square. A numerical example is provided to show the effectiveness and applicability of the proposed estimator.

This article studies the problem of state estimation for stochastic systems with unknown inputs. To reduce the communication cost from the sensor to the remote processor, an event‐triggered communication mechanism is proposed in terms of an event generator function for the innovation vectors. The event‐triggered estimator is developed by introducing an input term in the steady‐state Kalman filter for the corresponding nominal system. The input gain matrix is determined by treating the nominal estimator error dynamics as the desired performance. It is shown that the estimation error is bounded in mean square under certain conditions. A numerical example is provided to verify the effectiveness of the proposed estimator.

This paper addresses the adaptive synchronization problem of networked mechanical systems in task space with time-varying communication delays, where both kinematic and dynamic uncertainties are considered and the information flow in the networks is represented by a directed graph. Based on a novel coordination auxiliary system, we first extend existing feedback architecture to achieve synchronization of networked mechanical systems in task space with slow-varying delays. Given that abrupt turns arise for the delays sometimes, we then propose a delay-independent adaptive synchronization control scheme which removes the requirement of the slow-varying condition. Both of the two control schemes are established with time-domain approaches by using Lyapunov-Krasovskii functions. Simulation results are provided to demonstrate the effectiveness of the proposed control schemes.

The problem of interacting multiple model (IMM) estimation for jump Markov linear systems with unknown measurement noise covariance is studied. The system state and the unknown covariance are jointly estimated, where the unknown covariance is modelled as a random matrix according to an inverse‐Wishart distribution. For the IMM estimation with random matrices, one difficulty encountered is the combination of a set of weighted inverse‐Wishart distributions. Instead of using the moment matching approach, this difficulty is overcome by minimising the weighted Kullback–Leibler divergence for inverse‐Wishart distributions. It is shown that a closed‐form solution can be derived for the optimisation problem and the resulting solution coincides with an inverse‐Wishart distribution. Simulation results show that the proposed filter outperforms the previous work using the moment matching approach.

Plenty of practical applications require accurate emitting or reflective source localization using wireless sensor networks (WSNs). The geometric placement of WSNs is known to significantly affect the source localization results. In this letter, optimal placements of heterogeneous sensor networks (HSNs) comprised of range/bearing/received signal strength sensors are studied. The measurement noise of the sensors is set to be distance dependent. The maximization of the determinant of the Fisher information matrix is chosen as the optimality criterion. First, the sensors are assumed to be deployable in an annulus as usually done in the literature, and it is found that the bearing deployment range can be significantly reduced (even to 0) if bearing sensors are involved. Then, the HSN placements in constrained regions, specifically in a segmental arch, on a straight line, or in an external closed region, are investigated. Plentiful optimal placements are observed under the conditions of HSN, distance-dependent noise, and constrained regions.

This paper deals with finite-time consensus problems for multiagent systems that are subject to hybrid cooperative and antagonistic interactions. Two consensus protocols are constructed by employing the nearest neighbor rule. It is shown that under the presented protocols, the states of all agents can be guaranteed to reach an agreement in a finite time regarding consensus values that are the same in modulus but may not be the same in sign. In particular, the second protocol can enable all agents to reach a finite-time consensus with a settling time that is not dependent upon the initial states of agents. Simulation results are given to demonstrate the effectiveness and finite-time convergence of the proposed consensus protocols.

The problem of distributed target tracking using time of arrival and received signal strength with unknown path loss exponent (PLE) is studied. The PLE is modelled as a Markov chain with three states and an adaptive gridding strategy is adopted to adjust the PLE recursively in a bounded interval. Therefore, the target tracking model is formulated as a jump Markov non‐linear system. The interacting multiple model () estimator is applied to derive the target state estimates for each sensor and the covariance intersection approach is used to fuse sensor‐based estimates in a distributed fashion. Simulation results show a promising performance for the proposed filter.

This paper presents a hierarchical controller for an autonomous vehicle to track a reference path in the presence of uncertainties in both tire-road condition and external disturbance. The hierarchical control architecture consists of three layers: high, low, and intermediate levels. The upper-layer module deals with the vehicle motion control objective, which generates the desired longitudinal/lateral forces and yaw moment. The low-level module handles the braking control for each wheel based on the wheel slip dynamics. The intermediate-level controller generates the longitudinal slip reference for the low-level brake control module and the front-wheel steering angles. To cope with the unknown and nonuniform road condition parameters appearing in the actuator models, an adaptive law is designed for each wheel, and the convergence of the adaptive parameters is guaranteed under a certain persistency-of-excitation condition. The stability of the integrated control system is analyzed by utilizing a Lyapunov function approach. Simulation results are included to illustrate the proposed control scheme.

This paper deals with the consensus tracking control issues of multiagent systems and aims to solve them as accurately as possible over a finite time interval through an iterative learning approach. Based on the iterative rule, distributed algorithms are proposed for every agent using its nearest neighbor knowledge, for which the robustness problem is addressed against initial state shifts, disturbances, and switching topologies. These uncertainties are dynamically changing not only along the time axis but also the iteration axis. It is shown that the matrix norm conditions can be developed to achieve the convergence of the considered consensus tracking objectives, for which necessary and sufficient conditions are presented in terms of linear matrix inequalities to guarantee their feasibility in the sense of the spectral norm. Furthermore, simulation examples are given to illustrate the effectiveness and robustness of the obtained consensus tracking results.

In this study, an effective robust transcale estimation algorithm is proposed for discrete‐time systems, which are observed by a single sensor at the finest resolution or by two sensors at the finest and coarsest resolutions. The discrete‐time state‐space models of approximation and detail coefficients at each resolution are established by using Haar wavelet decomposition, respectively. The algorithm is developed based on the standard H ∞ filtering scheme, and hence preserves the merits of the H ∞ filter for random signal estimation in the sense that it minimises the effect of the worst possible disturbances on the estimation errors. The proposed algorithm is demonstrated through Monte Carlo simulations involving tracking of a target in CV model.

This study is concerned with the problem of filtering and fixed‐lag smoothing for jump Markov non‐linear systems when the mode information can be extracted from an image sensor. Based on the idea of Rao–Blackwellisation, the authors present a general theoretical framework to derive the recursive estimates by employing the particle filtering method. A suboptimal image‐enhanced Rao–Blackwellised particle filter is proposed, in which the mode state is estimated by using random sampling and the continuous state as well as the relevant likelihood function are approximated as Gaussian distributions. The one‐step fixed‐lag smoothing result is also obtained for such systems with lagged mode observations. Performance comparison of the proposed algorithms with the existing methods is provided through a manoeuvring target tracking simulation study.

This paper is devoted to the consensus tracking issue on multiagent systems. Instead of enabling the networked agents to reach an agreement asymptotically as the time tends to infinity, the consensus tracking between agents is considered to be derived on a finite time interval as accurately as possible. We thus propose a learning algorithm with a gain operator to be determined. If the gain operator is designed in the form of a polynomial expression, a necessary and sufficient condition is obtained for the networked agents to accomplish the consensus tracking objective, regardless of the relative degree of the system model of agents. Moreover, the H∞ analysis approach is introduced to help establish conditions in terms of linear matrix inequalities (LMIs) such that the resulting processes of the presented learning algorithm can be guaranteed to monotonically converge in an iterative manner. The established LMI conditions can also enable the iterative learning processes to converge with an exponentially fast speed. In addition, we extend the learning algorithm to address the relative formation problem for multiagent systems. Numerical simulations are performed to demonstrate the effectiveness of learning algorithms in achieving both consensus tracking and relative formation objectives for the networked agents.

This paper is devoted to solving formation problems of multi-agent systems with higher-order dynamics. By using the iterative learning control (ILC) approaches, effective distributed algorithms are developed to enable all agents in directed graphs to achieve the desired relative formations perfectly over a finite-time interval. It is shown that the graph theory can be combined to develop conditions for both asymptotic stability and monotonic convergence of multi-agent formation ILC. Simulation results are finally given to verify our theoretical study.

Iterative learning control (ILC) is a kind of effective data-driven method that is developed based on online and/or offline input/output data. The main purpose of this paper is to supply a unified 2-D analysis approach for both continuous-time and discrete-time ILC systems with relative degree. It is shown that the 2-D Roesser system framework can be established for general ILC systems regardless of relative degree, under which convergence conditions can be provided to guarantee both asymptotic stability and monotonic convergence of the ILC processes. In particular, conditions for the monotonic convergence of ILC can be given in terms of linear matrix inequalities, and formulas for the updating law can be derived simultaneously. Simulation results are presented to illustrate the effectiveness of ILC determined through the 2-D design approach in dealing with the higher order relative degree problem of ILC systems, as well as the robustness of such ILC against uncertainties.