Tracking control for multi-agent consensus with an active leader and variable topology

In this paper, we consider the coordination control of a group of autonomous mobile agents with multiple leaders. Different interconnection topologies are investigated. At first, a necessary and sufficient condition is proved in the case of fixed interconnection topology. Then a sufficient condition is proposed when the interconnection topology is switched. With a simple first-order dynamics model by using the neighborhood rule, both results show that the group behavior of the agents will converge to the polytope formed by the leaders.Comment: 6 page

Multi-variable LQG optimal control - restricted structure control for benchmarking and tuning

The paper introduces the benchmarking of multivarialbe systems using an offline optimal LQG approach

COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION

This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice

A convenient policy control through the Macro Multiplier Approach

In this paper an attempt is made to identify a ”convenient” structure of a policy variable, final demand control, through the use of a multi-sectoral model. The method used relies on a specific spectral ecomposition which allows for the quantification of the scale-effect of each structure that the policy variable can assume on the structures of the objective ariable. This quantification is of aggregated type since the scalars obtained are valid for all sectoral components of both the policy variable and the objective variable. What is more relevant they are consistent with the multi-sectoral feature of the model, overcoming the objections put forward by the theory of aggregation. In fact the aggregation theory states that if we aggregate sectors we obtain a new model with different structural properties, while, in our case, the aggregated scalar that we obtain for each structure is perfectly consistent with the original model. We call these scalars Macroeconomic Multipliers since they say how many time the modulus of the multi-sectoral policy variable is multiplied when we compare it with the modulus of the effects observed on the multi-sectoral objective variable. Once identified the structures and the associated Macro Multipliers, the policy maker can have a complete picture of the economic structure of the objective variables that can be attained and determine a ”convenient” structure of the policy variable choosing either one structure or a combination of the structures identified.IO model,Structural Change,Multipliers Analysis

Extremum Seeking-based Iterative Learning Linear MPC

In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-variable extremum seeking (MES)-based learning MPC algorithm to learn on-line the uncertain parameters and update the MPC model. We show the effectiveness of this algorithm on a DC servo motor control example.Comment: To appear at the IEEE MSC 201

A probabilistic weak formulation of mean field games and applications

Mean field games are studied by means of the weak formulation of stochastic optimal control. This approach allows the mean field interactions to enter through both state and control processes and take a form which is general enough to include rank and nearest-neighbor effects. Moreover, the data may depend discontinuously on the state variable, and more generally its entire history. Existence and uniqueness results are proven, along with a procedure for identifying and constructing distributed strategies which provide approximate Nash equlibria for finite-player games. Our results are applied to a new class of multi-agent price impact models and a class of flocking models for which we prove existence of equilibria

Proportional-integral-plus (PIP) control of the ALSTOM gasifier problem

Although it is able to exploit the full power of optimal state variable feedback within a non-minimum state-space (NMSS) setting, the proportional-integral-plus (PIP) controller is simple to implement and provides a logical extension of conventional proportional-integral and proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically by the NMSS formulation of the problem when the process is of greater than first order or has appreciable pure time delays. The present paper applies the PIP methodology to the ALSTOM benchmark challenge, which takes the form of a highly coupled multi-variable linear model, representing the gasifier system of an integrated gasification combined cycle (IGCC) power plant. In particular, a straightforwardly tuned discrete-time PIP control system based on a reduced-order backward-shift model of the gasifier is found to yield good control of the benchmark, meeting most of the specified performance requirements at three different operating points

How to decompose arbitrary continuous-variable quantum operations

We present a general, systematic, and efficient method for decomposing any given exponential operator of bosonic mode operators, describing an arbitrary multi-mode Hamiltonian evolution, into a set of universal unitary gates. Although our approach is mainly oriented towards continuous-variable quantum computation, it may be used more generally whenever quantum states are to be transformed deterministically, e.g. in quantum control, discrete-variable quantum computation, or Hamiltonian simulation. We illustrate our scheme by presenting decompositions for various nonlinear Hamiltonians including quartic Kerr interactions. Finally, we conclude with two potential experiments utilizing offline-prepared optical cubic states and homodyne detections, in which quantum information is processed optically or in an atomic memory using quadratic light-atom interactions.Comment: Ver. 3: published version with supplementary materia