Formation-containment tracking for general linear multi-agent systems with a tracking-leader of unknown control input

In this paper, distributed containment control problems of general linear multi-agent systems are investigated. The objective is to make the followers in a multi-agent network converge to the convex hull spanned by some leaders whose control inputs are nonzero and not available to any followers. Sliding mode surfaces are defined for the cases of reduced order and non-reduced order, respectively. For each case, fast sliding mode controllers are designed. It is shown that all the error trajectories exponentially reach the sliding mode surfaces in a finite time if for each follower, there exists at least one of the leaders who has a directed path to the follower, and the leaders' control inputs are bounded. The control Lyapunov function for exponential finite time stability, motivated by the fast terminal sliding mode control, is used to prove reachability of the sliding mode surfaces. Simulation examples are given to illustrate the theoretical results.

This paper studies the distributed time-varying formation tracking problems for high-order multi-agent systems with a leader of unknown input and switching topologies. The followers can realize the given formation configuration and track the trajectory of the leader at the same time. By assigning the adaptive gains and the sliding mode terms to each edge of the graph, a novel edge-based adaptive formation tracking protocol is presented to compensate for the leader's unknown input. The proposed approach is fully distributed with no need for the global information of the topology and the upper bound of the leader's input. Based on the common Lyapunov theory, it is proved that the expected formation tracking can be realized by high-order multi-agent systems with arbitrary switching graphs. A simulation example is given to verify the effectiveness of the theoretical results.

The topic of this paper is the backstepping-based solution of the robust state feedback regulation problem for general linear heterodirectional hyperbolic systems with spatially varying coefficients. Thereby, a large class of outputs is considered, i.e., mixed distributed and pointwise in-domain or boundary outputs. The state feedback regulator design is based on the $\boldsymbol {p}$ -copy internal model principle. This results in a stabilization problem for a cascade of $\boldsymbol {m+p}$ boundary controlled transport partial differential equations (PDEs) with $\boldsymbol {p}$ ordinary differential equations (ODEs). For this, a systematic backstepping approach is presented, in which all design equations are explicitly solvable in backstepping coordinates. Furthermore, the solvability conditions for the considered output regulation problem can be evaluated in closed form utilizing the plant transfer behavior. Robust output regulation is verified for nondestabilizing model uncertainties and unknown disturbance input locations. A $\boldsymbol {4 \times 4}$ hyperbolic system with uncertain transport velocities and couplings in the transport PDEs illustrates the results of this paper.

In this paper, the leader following consensus problem of general linear multi-agent systems is investigated, where undirected communication topology is considered. To solve the tracking consensus problem, a distributed event-triggered strategy is designed with state dependent threshold. It is shown that the tracking consensus can be achieved asymptotically without continuous communication in both controller updates and triggering condition monitoring. The feasibility of this proposed event triggered control strategy is further investigated by excluding the Zeno behavior for each agent. Finally, a simulation example is given to validate the theoretical results.

Collective behaviors of multi-agent systems over signed graphs find applications in a variety of scenarios including social networks, predator-prey dynamics, which however have not been adequately addressed as their counterparts with nonnegative graphs. This paper studies bipartite consensus problem of general linear multi-agent systems over signed digraphs. First, we show that for general linear agents, bipartite consensus over signed graphs and ordinary consensus over nonnegative graphs are equivalent. This indicates that prevailing consensus controllers for nonnegative graphs can be adopted to solve bipartite consensus problems. Based on this observation, an existing Riccati equation based cooperative tracking controller is extended to solve the bipartite consensus problem for general linear systems.

The Dynamic Regulation of Linear Discrete-time Systems with Unknown Input (A Design Method Using Adaptive State Observer)

This paper addresses the time-varying formation-containment (FC) problem for general linear multi-agent systems based on an observer viewpoint under the directed communication graph which contains a spanning tree. A new format of time-varying formation shapes with the properties of translation, scalability and rotation is proposed. Then the time-varying formation tracking problem where the real leader's input is unknown to all other agents is solved independently. After that, the time-varying FC problem where the challenge is how to deal with the formation-achieving agents' unknown nonzero inputs for containment-achieving agents, is firstly addressed. All the designed controllers, which need no eigenvalue information of the whole graph, are fully distributed and so can be applied to large-scale systems. The influence of communication graph structure to the control convergence rate is further analysed. The above theoretical results are verified by multiple nonholonomic vehicles in simulations.

This paper studies the state estimation problem of linear discrete-time systems with stochastic unknown inputs. The unknown input is a wide-sense stationary process while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm (ERA). An augmented state system is constructed and the standard Kalman filter is applied for state estimation. A reduced order model (ROM) filter is also introduced to reduce the computational cost of the Kalman filter. One numerical example is given to illustrate the procedure.

Adaptive distributed observer for general linear leader systems over periodic switching digraphs

This paper focuses on the joint input and state estimation for linear stochastic discrete-time systems. To obtain unique estimators that are optimal in the sense of being both least-squares and minimum-variance unbiased (MVU) and their recursive solution, an objective function of the sum of squared errors of outputs is first minimized with respect to an extended state vector including states and unknown inputs. A recursive solution of the extended state vector is then derived with the aid of matrix manipulations. Finally, appropriate matrix decomposition and transformation are used to extract the recursive solutions only involving the current states and unknown inputs from the recursive solution obtained in the previous step. This approach avoids excessive computation due to the dimensions of the matrices that increase with time. As a result, the resulting recursive solution is referred to as a general Kalman filter with unknown inputs (GKF-UI), which covers the most general case of unknown inputs so far in the literature without resorting to transforming the outputs and/or unknown inputs. The unknown inputs to be estimated by the proposed GKF-UI could be arbitrary signals without any prior information and the properties of the proposed GKF-UI are also examined. A numerical example with partial feedthrough from three unknown inputs is used to demonstrate the effectiveness and accuracy of the proposed algorithm.

The Time-Varying First Order Plus Dead Time (TV-FOPDT) model is an extension of the conventional FOPDT by allowing the system parameters, which are primarily defined on the transfer function description, i.e., the DC-gain, time constant and time delay, to be time dependent. The TV-FOPDT identification problem turns to estimate these time-varying parameters based on measured control input and system output. This work considers a TV-FOPDT identification problem in the presence of an unknown disturbance input. By regarding the unknown input as one extra system parameter, the considered identification problem is formulated as a Stochastic Mixed Integral Programming (SMIP) problem after discretizing the original problem. The sliding window technique with forgetting factor is employed to cope with time resolution issue, and the Least Mean Square (LMS) method is used to obtain the optimal solution of each individual optimization problem based on different time delay assumptions. The proposed method is firstly tested through a number of numerical examples, and then it is applied to estimate a TV-FOPDT model of the superheat dynamic of a supermarket refrigeration system.

Robust finite-time containment control of general linear multi-agent systems under directed communication graphs

In this paper, a robust state estimation problem for discrete-time systems with unknown exogenous inputs is investigated utilizing a risk-sensitive filtering approach. By proposing a Radon-Nikodym derivative, we introduce a reference measure under which the measurement and system state become independent. Based on this independent property and by treating the unknown inputs as a process modeled by a non-informative prior, we derive the reformulated risk-sensitive cost criterion under the reference measure and further propose a recursive algorithm for the risk-sensitive state estimate. A simulation example is provided to validate the theoretical results, where the proposed estimator is shown to outperform the MMSE estimator for unknown input case under the scenario subject to system parameter uncertainties.

Joint unknown input observer for descriptor system based on interval observer

Designing delay-dependent functional observers for LTI systems with multiple known time-varying state delays and unknown time-varying input delays is studied. The input delays are arbitrary, but the state delays should be upper-bounded. In addition, two scenarios of slow-varying and fast-varying state delays are investigated. The results of the paper can also be considered as one of the first contributions considering unknown-input functional observer design for linear systems with multiple time-varying state delays. Based on the Lyapunov Krasovskii approach, delay-dependent sufficient conditions of the exponential stability of the observer in each scenario are established in terms of linear matrix inequalities. Because of using effective techniques, such as the descriptor transformation and an advanced weighted integral inequality, the proposed stability criteria can result in larger stability regions compared with the other papers that study functional observers for time-varying delay systems. Furthermore, to help with the design procedure, a genetic algorithm-based scheme is proposed to adjust a weighting matrix in the established linear matrix inequalities. Two numerical examples illustrate the design procedure and demonstrate the efficacy of the proposed observer in each scenario. Copyright © 2016 John Wiley & Sons, Ltd.