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This article aims at developing a scaling-function approach for distributed optimization of unbalanced multiagent networks under convex constraints. The distinguishing feature of the algorithm is that it does not employ agents’ out-degree information, nor does it require the estimation of the left eigenvector, corresponding to the zero eigenvalue, of the Laplacian matrix. Existing approaches for unbalanced networks either demand the knowledge on agents’ out-degrees, which is impractical in applications, where an agent might not be aware of the detection and employment of its information by other agents, or require every agent to be equipped with a network-sized estimator, causing an additional $n^2$ storage and communication cost with $n$ being the network size. The results exhibit an inherent connection between the selection of the scaling factor and the convergence property of the algorithm, among other known factors such as the network topology and the boundedness of the subgradients of the local objective functions. Numerical examples are provided to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

This article studies the tracking control problem of networked multiagent systems under both multiple networks and event-triggered mechanisms. Multiple networks are to connect multiple agents and reference systems with decentralized controllers to guarantee their information transmission, whereas the event-triggered mechanisms are to reduce the information transmission via the networks. In this article, each agent has a network to communicate with its controller and reference system, and all networks are independent and asynchronous and have local event-triggered mechanisms, which are based on local measurements and determine whether the local measurements need to be transmitted via the corresponding network. To address this scenario, we first implement the emulation-based approach to develop a novel hybrid model for the tracking control of networked multiagent systems. Next, sufficient conditions are derived and decentralized event-triggered mechanisms are designed to guarantee the desired tracking performance. Furthermore, the proposed approach is applied to derive novel results for the event-triggered observer design problem of networked multiagent systems. Finally, two numerical examples are presented to illustrate the validity of the developed results. [ABSTRACT FROM AUTHOR]

Due to the nonsymmetric property of the associated Laplacian matrix and the existence of uncertainties in the agent dynamics, the leaderless consensus problem of uncertain multiagent systems under general directed graphs is challenging. Motivated by the classical model reference adaptive control, in this article, we propose a simple yet efficient scheme, called the model reference adaptive consensus, by arranging each agent a reference output to track, where the output is generated by a linear reference model with the relative state measurements as input. We consider two typical agent dynamics, namely, the general linear dynamics with matching uncertainties and the second-order dynamics with extended matching uncertainties. Different linear reference models are designed for the above uncertain agent dynamics. For the first one, dynamical consensus is achieved under a fixed directed graph containing a directed spanning tree. For the latter one, position consensus is achieved under switching uniformly jointly connected graphs. In the proposed algorithms, only the relative states are interacted among the agents and there is no requirement on the communication of virtual signals. [ABSTRACT FROM AUTHOR]

This article addresses the problem of cooperative adaptive containment control for multiagent systems, which specifies the objective of jointly achieving containment control and accurate adaptive learning/identification of unknown system parameters. We consider a class of linear uncertain multiagent systems with multiple leaders subject to bounded unmeasurable inputs and multiple followers subject to unknown system dynamics. A novel cooperative adaptive containment control architecture is proposed, which consists of a discontinuous nonlinear state-feedback control law and a filter-based cooperative adaptation law. This new control architecture is compelling in the sense that exponential convergence of both containment tracking errors to zero and adaptation parameters to their true values can be achieved simultaneously under a mild cooperative finite-time excitation condition. This condition significantly relaxes existing ones (e.g., persistent excitation and finite-time excitation) for parameter identification in adaptive control systems. Effectiveness of the proposed approach has been demonstrated through both rigorous analysis and a case study. [ABSTRACT FROM AUTHOR]

In this article, a distributed constrained optimization problem is studied with nonconvex input constraints, nonuniform convex state constraints, and nonuniform step sizes for single-integrator multiagent systems. Due to the existence of nonconvex input constraints, the edge weights between agents are equivalently multiplied with different time-varying scaling factors, and thus, the real interaction relationship cannot be kept balanced, even if the original communication graphs are kept balanced. Due to the existence of nonuniform convex state constraints and nonuniform step sizes, the system contains strong nonlinearities, which are coupled with the unbalance of the real interaction relationship, making existing analysis approaches hard to apply in this article. The main idea of the analysis approach is to fully explore the angles between the vectors from the agent states to their own projections on the intersection set of the convex state constraint sets so as to show that the distances from the agents to the intersection set diminish to zero as time evolves. By combining the analysis approaches in this article and our previous works, all agents are proved to converge to a common point and simultaneously solve the given optimization problem as long as the union of the communication graphs is strongly connected and balanced among each time interval of certain length. Numerical examples are given to show the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this article, we study the robust filtering problem for 2-D switched systems with convex-bounded uncertainties. The studied switched system is formulated by Fornasini–Marchesini local state-space model, and system matrices depend affinely on convex-bounded uncertain parameters. We first derive synthesis results for 2-D switched systems, which extends the existing results and lays a foundation for the robust filter design. To design the robust filter, we transform the 2-D switched system into an equivalent difference-algebraic representation form. Using the equivalent representation and the parameter-dependent Lyapunov function approach, sufficient conditions are established for system stability, and 2-D stationary discrete-time linear filters are designed to ensure the prescribed $\mathcal {H}_{\infty }$ or $\mathcal {H}_{2}$ performance for all admissible uncertain parameters. Finally, a numerical example is presented to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

This article studies a distributed average tracking (DAT) problem, in which a collection of agents work collaboratively, subject to local communication, to track the average of a set of reference signals, each of which is available to a single agent. Our primary objective is to seek a design methodology for DAT under possibly weight-unbalanced directed networks—the most general and thus most challenging case from the network topology perspective, which has few results in the literature. For this purpose, we propose a distributed algorithm based on a chain of two integrators that are coupled with a distributed estimator. It is found that the convergence depends on not only the network topology but also the deviations among the reference signal accelerations. Another primary interest of this article stems from the dynamics perspective—a point perceived as a main source of control design difficulty for multiagent systems. Indeed, we devise a nonlinear algorithm that is capable of achieving DAT under weight-unbalanced directed networks for agents subject to high-order integrator dynamics. The results show that the convergence to the vicinity of the average of the reference signals is guaranteed as long as the signals’ states and control inputs are all bounded. Both algorithms are robust to initialization errors, i.e., DAT is insured even if the agents are not correctly initialized, enabling the potential applications in a wider spectrum of application domains. [ABSTRACT FROM AUTHOR]

This note studies the tracking control problem of nonlinear networked and quantized control systems (NQCSs) with communication delays. The communication network is to guarantee the information transmission among the plant, the reference system, and the controller. The communication network also brings about some undesired issues, which lead to nonvanishing network-induced errors and affect the tracking performance. As a result, we first develop a hybrid system model for NQCSs under the network-induced issues. Based on the Lyapunov approach, sufficient conditions are established to guarantee the tracking performance with respect to the nonvanishing network-induced errors. The obtained conditions lead to a tradeoff between the maximally allowable transmission interval and the maximally allowable delay. Furthermore, the existence of Lyapunov functions satisfying the obtained conditions is studied. For specific time-scheduling protocols and quantizers, Lyapunov functions are constructed explicitly. Finally, a numerical example is presented to demonstrate the developed theory. [ABSTRACT FROM AUTHOR]

Modern applications of robotics typically involve a robot control system with an inner proportional–integral (PI) or proportional–integral–derivative (PID) control loop and an outer user-specified control loop for specifying the velocity (or position) command. Most existing outer loop controllers, however, do not take into consideration the dynamic effects of robots and their effectiveness relies on the ad hoc assumption that the inner PI or PID control loop is fast enough, and other torque-based controllers cannot be implemented in robots with closed architecture. This paper investigates the adaptive control of robotic systems with an inner/outer loop structure, taking into account the effects of the dynamics and uncertainties, and both the task-space control and joint-space control are considered. We propose a dynamic modularity approach to resolve this issue, and a class of adaptive outer loop control schemes is proposed and their role is to dynamically generate the joint velocity (or position) command for the low-level joint servoing loop. Without relying on the ad hoc assumption that the joint servoing loop is fast enough or modification of the low-level joint controller structure, we show that the proposed outer loop controllers can ensure stability and convergence of the closed-loop system. The formulated dynamic modularity approach is validated by the experimental results using different generations of the UR10 robots. [ABSTRACT FROM AUTHOR]

This paper studies the distributed time-varying convex optimization problem for a class of nonlinear multiagent systems. An auxiliary system is first designed to estimate the global optimal state and its high-order derivatives by means of a distributed algorithm. Relying on this auxiliary system, the solution of the distributed time-varying convex optimization problem is converted into that of a tracking problem for a class of nonlinear systems. A tracking control scheme is then proposed based on the back-stepping strategy. Simulations are provided to validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is devoted to distributed continuous-time and discrete-time optimization problems with nonuniform convex constraint sets and nonuniform stepsizes for general differentiable convex objective functions. The communication graphs are not required to be strongly connected at any time, the gradients of the local objective functions are not required to be bounded when their independent variables tend to infinity, and the constraint sets are not required to be bounded. For continuous-time multiagent systems, a distributed continuous algorithm is first introduced where the stepsizes and the convex constraint sets are both nonuniform. It is shown that all agents reach a consensus while minimizing the team objective function even when the constraint sets are unbounded. After that, the obtained results are extended to discrete-time multiagent systems and then the case where each agent remains in a corresponding convex constraint set is studied. To ensure all agents to remain in a bounded region, a switching mechanism is introduced in the algorithms. It is shown that the distributed optimization problems can be solved, even though the discretization of the algorithms might deviate the convergence of the agents from the minimum of the objective functions. [ABSTRACT FROM AUTHOR]

This paper studies input-to-state stability for hybrid systems with memory, which models hybrid dynamics affected by time delays. Using both Lyapunov–Razumikhin functions and Lyapunov–Krasovskii functionals, Lyapunov-based sufficient conditions are established for input-to-state stability. In addition, further extensions and relaxations are proposed for special cases, such as the stable flow/jump cases and the cases that Lyapunov functions do not decrease strictly during flow/jumps. Finally, two examples are used to illustrate the developed results [ABSTRACT FROM AUTHOR]

This paper studies the stability for impulsive switched time-delay systems with state-dependent impulses. Since the impulses and the switches are not necessarily synchronous, we start from a stability analysis of impulsive switched time-delay systems with time-dependent impulses. Sufficient conditions are derived to guarantee the stability property, which extends the previous results for the synchronous switch and impulse case. For the state-dependent impulse case, using the B-equivalent method, impulsive switched time-delay systems with state-dependent impulses are transformed into impulsive switched time-delay systems with time-dependent impulses. The equivalence between the original system and the transformed system is established, and stability conditions are obtained for impulsive switched time-delay systems with state-dependent impulses. Finally, a numerical example is given to demonstrate the obtained results. [ABSTRACT FROM AUTHOR]

This technical note addresses the distributed specified-time consensus protocol design problem for multiagent systems with general linear dynamics over directed graphs. By using motion planning approaches, a novel class of distributed consensus protocols are developed. With a prespecified settling time, the proposed protocols solve the consensus problem of linear multiagent systems over directed graphs containing a directed spanning tree. In particular, the settling time can be offline prespecified, according to task requirements. Compared with the existing results for multiagent systems, to our best knowledge, this is the first time specified-time consensus problems are raised and solved for general linear multiagent systems over directed graphs. Extensions to the specified-time formation flying are further studied for multiple satellites described by Hill equations. [ABSTRACT FROM AUTHOR]

This note is devoted to the distributed optimization problem of multi-agent systems with nonconvex velocity constraints, nonuniform position constraints, and nonuniform stepsizes. Two distributed constrained algorithms with nonconvex velocity constraints and nonuniform stepsizes are proposed in the absence and the presence of nonuniform position constraints by introducing a switching mechanism to guarantee all agents’ position states to remain in a bounded region. The algorithm gains need not to be predesigned and can be selected by each agent using its own and neighbors’ information. By a model transformation, the original nonlinear time-varying system is converted into a linear time-varying one with a nonlinear error term. Based on the properties of stochastic matrices, it is shown that the optimization problem can be solved as long as the communication topologies are jointly strongly connected and balanced. One numerical example is given to show the obtained theoretical results. [ABSTRACT FROM AUTHOR]

The distributed convex optimization problem is studied in this paper for any fixed and connected network with general constraints. To solve such an optimization problem, a new type of continuous-time distributed subgradient optimization algorithm is proposed based on the Karuch–Kuhn–Tucker condition. By using tools from nonsmooth analysis and set-valued function theory, it is proved that the distributed convex optimization problem is solved on a network of agents equipped with the designed algorithm. For the case that the objective function is convex but not strictly convex, it is proved that the states of the agents associated with optimal variables could converge to an optimal solution of the optimization problem. For the case that the objective function is strictly convex, it is further shown that the states of agents associated with optimal variables could converge to the unique optimal solution. Finally, some simulations are performed to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper addresses distributed average tracking of physical second-order agents with heterogeneous unknown nonlinear dynamics, where there is no constraint on input signals and there is no need for correct initialization. The unknown nonlinear terms in agents’ dynamics are heterogeneous, satisfying a Lipschitz-like condition that will be defined later and is more general than the Lipschitz condition. In the proposed algorithm, a control input and a filter are designed for each agent. Each agent's filter has two outputs and the idea is that the first output estimates the average of the input signals, and the second output estimates the average of the input velocities asymptotically. In parallel, each agent's position and velocity are driven to track, respectively, the first and the second outputs. Having an unknown term in agents’ dynamics necessitates designing the filters for agents. Since the nonlinear terms in agents’ dynamics can be unbounded and the input signals are arbitrary, novel time-varying gains are employed in agents’ filters and control inputs to overcome these unboundedness effects. Finally, the results are improved to achieve the distributed average tracking for a group of double-integrator agents, where there is no constraint on input signals and the filter is not required anymore. Numerical simulations are also presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the input-to-state stability is studied for stochastic impulsive switched time-delay systems. Using the vector Lyapunov function, average dwell time, and the properties of $M$ -matrix, different types of sufficient conditions are established. Both the case that the continuous dynamics is stable and the case that the discrete dynamics is stable are addressed, and the stability conditions are obtained. In the obtained stability conditions, different components of the vector Lyapunov function are allowed to be coupled; the information in consecutive impulsive switching intervals is also allowed to be coupled. Therefore, the magnification on the corresponding coupling items is avoided and the obtained results are more general and less conservative than the existing results. Furthermore, we investigate the relationships among the vector Lyapunov function approach, the approach based on the comparison principle and the scalar Lyapunov function approach. According to the vector Lyapunov function, the comparison system is constructed and the scalar-Lyapunov-function-based stability conditions are established. Finally, the applicability of our results is illustrated through two examples from neural systems and the synchronization problem of chaos-based secure communication systems. [ABSTRACT FROM AUTHOR]

This paper studies the quantized feedback stabilization problem for nonlinear systems with external disturbance. A dynamic quantizer with a quantization parameter is implemented in this paper. To extract more information, the quantization parameter is updated according to an updating protocol at the discrete-time instants. Three quantization cases are studied in this paper, i.e., state quantization case, input quantization case, and output quantization case. Based on the updating protocol and Lyapunov approach, sufficient conditions are developed for quantized feedback stabilization of the closed-loop system. [ABSTRACT FROM AUTHOR]

We study synchronization of coupled linear systems over networks with weak connectivity and nonuniform time-varying delays. We focus on the case where the internal dynamics are time-varying but nonexpansive (stable dynamics with a quadratic Lyapunov function). Both uniformly jointly connected and infinitely jointly connected communication topologies are considered. A new concept of quadratic synchronization is introduced. We first show that global asymptotic quadratic synchronization can be achieved over directed networks with uniform joint connectivity and arbitrarily bounded delays. We then study the case of infinitely jointly connected communication topology. In particular, for the undirected communication topologies, it turns out that the existence of a uniform time interval for the jointly connected communication topology is not necessary and quadratic synchronization can be achieved when the time-varying nonuniform delays are arbitrarily bounded. Simulation results are provided to validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is devoted to a distributed consensus problem for second-order multiagent systems with nonconvex velocity and control input constraints. A distributed control algorithm is introduced using local information. It is shown that consensus can be achieved as long as the union of the communication graphs has directed spanning trees among each time interval of certain bounded length in the presence of arbitrarily bounded communication delays. Finally, a numerical example is included to illustrate the theoretical results. [ABSTRACT FROM PUBLISHER]

In this technical note, a distributed velocity-constrained consensus problem is studied for discrete-time multi-agent systems, where each agent's velocity is constrained to lie in a nonconvex set. A distributed constrained control algorithm is proposed to enable all agents to converge to a common point using only local information. The gains of the algorithm for all agents need not to be the same or predesigned and can be adjusted by each agent itself based on its own and neighbors' information. It is shown that the algorithm is robust to arbitrarily bounded communication delays and arbitrarily switching communication graphs provided that the union of the graphs has directed spanning trees among each certain time interval. The analysis approach is based on multiple novel model transformations, proper control parameter selections, boundedness analysis of state-dependent stochastic matrices1, exploitation of the convexity of stochastic matrices, and the joint connectivity of the communication graphs. Numerical examples are included to illustrate the theoretical results. [ABSTRACT FROM PUBLISHER]

This paper studies stochastic input-to-state stability and stochastic global stability for impulsive stochastic nonlinear systems. Using fixed dwell-time condition and Lyapunov-based approach, sufficient conditions are established for the stability properties. Two cases are studied: 1) the case that the continuous dynamics is stable and 2) the case that the impulsive effects are stable. Furthermore, the relations among different dwell-time conditions are studied. Finally, two examples are used to illustrate the developed theory. [ABSTRACT FROM AUTHOR]

In this paper, a distributed optimization problem with general differentiable convex objective functions is studied for continuous-time multi-agent systems with single-integrator dynamics. The objective is for multiple agents to cooperatively optimize a team objective function formed by a sum of local objective functions with only local interaction and information while explicitly taking into account nonuniform gradient gains, finite-time convergence, and a common convex constraint set. First, a distributed nonsmooth algorithm is introduced for a special class of convex objective functions that have a quadratic-like form. It is shown that all agents reach a consensus in finite time while minimizing the team objective function asymptotically. Second, a distributed algorithm is presented for general differentiable convex objective functions, in which the interaction gains of each agent can be self-adjusted based on local states. A corresponding condition is then given to guarantee that all agents reach a consensus in finite time while minimizing the team objective function asymptotically. Third, a distributed optimization algorithm with state-dependent gradient gains is given for general differentiable convex objective functions. It is shown that the distributed continuous-time optimization problem can be solved even though the gradient gains are not identical. Fourth, a distributed tracking algorithm combined with a distributed estimation algorithm is given for general differentiable convex objective functions. It is shown that all agents reach a consensus while minimizing the team objective function in finite time. Fifth, as an extension of the previous results, a distributed constrained optimization algorithm with nonuniform gradient gains and a distributed constrained finite-time optimization algorithm are given. It is shown that both algorithms can be used to solve a distributed continuous-time optimization problem with a common convex constraint set. Numerical examples are included to illustrate the obtained theoretical results. [ABSTRACT FROM PUBLISHER]

In this paper, a time-varying distributed convex optimization problem is studied for continuous-time multi-agent systems. The objective is to minimize the sum of local time-varying cost functions, each of which is known to only an individual agent, through local interaction. Here, the optimal point is time varying and creates an optimal trajectory. Control algorithms are designed for the cases of single-integrator and double-integrator dynamics. In both cases, a centralized approach is first introduced to solve the optimization problem. Then, this problem is solved in a distributed manner and a discontinuous algorithm based on the signum function is proposed in each case. In the case of single-integrator (respectively, double-integrator) dynamics, each agent relies only on its own position and the relative positions (respectively, positions and velocities) between itself and its neighbors. A gain adaption scheme is introduced in both algorithms to eliminate certain global information requirement. To relax the restricted assumption imposed on feasible cost functions, an estimator based algorithm using the signum function is proposed, where each agent uses dynamic average tracking as a tool to estimate the centralized control input. As a tradeoff, the estimator-based algorithm necessitates communication between neighbors. Then, in the case of double-integrator dynamics, the proposed algorithms are further extended. Two continuous algorithms based on, respectively, a time-varying and a fixed boundary layer are proposed as continuous approximations of the signum function. To account for interagent collision for physical agents, a distributed convex optimization problem with swarm tracking behavior is introduced for both single-integrator and double-integrator dynamics. It is shown that the center of the agents tracks the optimal trajectory, the connectivity of the agents is maintained, and interagent collision is avoided. [ABSTRACT FROM AUTHOR]

This note further studies the previously proposed consensus protocol for linear multi-agent systems with communication noises. Each agent is allowed to have its own time-varying gain to attenuate the effect of communication noises. Therefore, the common assumption that all agents have the same noise-attenuation gain is not necessary. It has been proved that if all noise-attenuation gains are infinitesimal of the same order, then the mean square leader-following consensus can be reached. Furthermore, the convergence rate of the multi-agent system has been investigated. If the noise-attenuation gains belong to a class of functions which are bounded above and below by t^-\beta $(\beta\in(0,1))$ asymptotically, then the second-moment of the relative state between each follower agent and the leader agent is characterized by a function bounded above by t^-\beta asymptotically. [ABSTRACT FROM PUBLISHER]

In this paper, we study the consensus problem for second-order multi-agent systems with heterogeneous unknown inertias and control gains under a general directed graph. Unlike the existing consensus algorithms for second-order multi-agent systems in which all agents are assumed to have common unit inertias or share common control gains, we allow the inertias and the control gains to be heterogeneous and time-varying for each agent. We propose fully distributed consensus algorithms over a general directed graph when there exist, respectively, absolute velocity damping and relative velocity damping. Novel integral-type Lyapunov functions are proposed to study the consensus convergence. Moreover, the adaptive $\sigma$- modification schemes for the gain adaptation are proposed, which renders smaller control gains and thus requires smaller amplitude on the control input without sacrificing consensus convergence. Furthermore, we show that one proposed algorithm also works for consensus of agents with intrinsic Lipschitz nonlinear dynamics. The control gains are varying and updated by distributed adaptive laws. As a result, the proposed algorithms require no global information and thus can be implemented in a fully distributed manner. [ABSTRACT FROM AUTHOR]

This technical note introduces a non-quadratic function that is defined in terms of multiple Laplacian quadratics, called the function of composite Laplacian quadratics. The function is mainly used for control synthesis and analysis of multi-agent systems. Some important properties of the function are studied. In particular, the one-level-set dissipation property is established, and the gradient of the function is derived. The function is further applied to solve the consensus problem of linear differential inclusions (LDIs). Finally, a numerical example is given to verify the validity of the derived results. [ABSTRACT FROM AUTHOR]

This technical note studies finite-time consensus problem for a team of networked nonlinear agents with unknown Lipschitz terms under communication constraints, where each agent has a limited sensing range. Because the induced interaction graph is typically state-dependent and dynamic, we propose a distributed nonlinear consensus algorithm that is capable of preserving the initial interaction patterns. By using tools from nonsmooth analysis, sufficient conditions are obtained such that finite-time consensus can be reached. An upper bound of the convergence time is derived via a two-step analysis. The validity of the theoretical result is shown by one simulation example. [ABSTRACT FROM AUTHOR]

The stochastic consensus problem of linear multi-input multi-output (MIMO) multi-agent systems (MASs) with communication noises and Markovian switching topologies is studied in this technical note. The agent's full state is first estimated by the state observer, and then the estimated state is exchanged with neighbor agents through a noisy communication environment. The communication topology is randomly switching and the switching law is described by a continuous-time Markovian chain. Then a consensus protocol is proposed for this MAS, and some sufficient conditions are obtained for ensuring the mean square and almost sure consensus. In addition, if the communication topology is fixed, some necessary and sufficient conditions for the mean square consensus can be obtained according to whether or not each agent in the system has parents. [ABSTRACT FROM AUTHOR]

This technical note addresses the distributed consensus protocol design problem for multi-agent systems with general linear dynamics and directed communication graphs. Existing works usually design consensus protocols using the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph, which however is global information. In this technical note, based on only the agent dynamics and the relative states of neighboring agents, a distributed adaptive consensus protocol is designed to achieve leader-follower consensus in the presence of a leader with a zero input for any communication graph containing a directed spanning tree with the leader as the root node. The proposed adaptive protocol is independent of any global information of the communication graph and thereby is fully distributed. Extensions to the case with multiple leaders are further studied. [ABSTRACT FROM AUTHOR]

In this technical note, the distributed average tracking (DAT) problem for reference signals with bounded accelerations is considered. A sliding-mode surface is designed such that trajectories on the surface will achieve DAT. A discontinuous control algorithm is then proposed to guarantee that the surface can be reached in finite time. It is shown that if the switching network topology is connected at all time, then DAT can be solved by properly choosing a gain parameter of the algorithm. [ABSTRACT FROM PUBLISHER]

Under an extended proportional-integral (PI) control scheme, a new class of distributed average tracking (DAT) algorithms has been developed for three different kinds of references: references with steady states, references with bounded derivatives, and references with a common derivative. The class of DAT algorithms are further applied to solve the DAT problem for Euler-Lagrange (EL) systems. [ABSTRACT FROM AUTHOR]