PARALLEL SIMULTANEOUS STABILIZATION OF A SET OF PORT-CONTROLLED HAMILTONIAN SYSTEMS SUBJECT TO ACTUATOR SATURATION

This paper investigates parallel simultaneous stabilization （PSS） of a set of multi-input nonlinear Port-Controlled Hamiltonian （PCH） systems subject to actuator saturation （AS）, and proposes a number of results on the design of PSS controllers for the PCH systems with AS. Firstly, the case of two PCH systems with AS is studied. Exploring the special property of the saturation nonlinearity and the structural properties of dissipative Hamiltonian system, the two systems are combined to generate an augmented PCH system, with which some results on the control design are then obtained. When there are external disturbances in the two systems, a robust PSS controller is designed for the systems. Secondly, the case of more than two PCH systems with AS is investigated, and several new results are proposed for the PSS problem. Finally, two illustrative examples are presented to show that the stabilization controllers obtained in this paper work very well.

OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS

Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.

COOPERATIVE SHIFT ESTIMATION OF TARGET TRAJECTORY USING CLUSTERED SENSORS

In this paper,a mathematical model for target tracking using nonlinear scalar range sensors is formulated first.A time-shift sensor scheduling strategy is addressed on the basis of a k-barrier coverage protocol and all the sensors are divided into two classes of clusters,active cluster,and submissive cluster,for energy-saving.Then two types of time-shift nonlinear filters are proposed for both active and submissive clusters to estimate the trajectory of the moving target with disturbed dynamics.The stochastic stability of the two filters is analyzed.Finally,some numerical simulations are given to demonstrate the effectiveness of the new filters with a comparison of EKF.

CONSENSUS CONTROL DESIGN FOR MULTI-AGENT SYSTEMS USING RELATIVE OUTPUT FEEDBACK

This paper studies the consensus problem of multi-agent systems in which all agents are modeled by a general linear system. The authors consider the case where only the relative output feedback between the neighboring agents can be measured. To solve the consensus problem, the authors first construct a static relative output feedback control under some mild constraints on the system model. Then the authors use an observer based approach to design a dynamic relative output feedback control. If the adjacent graph of the system is undirected and connected or directed with a spanning tree, with the proposed control laws, the consensus can be achieved. The authors note that with the observer based approach, some information exchange between the agents is needed unless the associated adjacent graph is completely connected.

Observer-based leader-following tracking control under both fixed and switching topologies

This paper studies the tracking problem for a class of leader-follower multi-agent systems moving on the plane using observer- based cooperative control strategies. In our set-up, only a subset of the followers can obtain some relative information on the leader. We assume that the control input of the leader is not known to any of the followers while the system matrix is broadcast to all the followers. To track such a leader, an observer-based decentralized feedback controller is designed for each follower and detailed analysis for the convergence is presented for both fixed and switching interaction topologies between agents with the method of common Lyapunov function. We can also generalize the result to the higher dimension case for fixed topology and some special system matrices of the leader for switching topology.

Distributed non-cooperative robust MPC based on reduced-order models

In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts： measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm.