This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

System Identification I

Convex bodies: the brunn–minkowski theory

Chapter 8 establishes the relationship between the input and output power spectral densities of a linear system. Limitations on results are carefully detailed and the case of oscillator noise is considered. The theory is extended to multiple input-multiple output systems.

Linear System Theory

Stochastic linear Model Predictive Control with chance constraints – A review

Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems

This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensors to the correct state even when constraints on noise and state variables are taken into account in the estimation process. Simulation examples are provided in order to show the main features of the proposed method.

/pdf/distributed-moving-horizon-estimation-for-linear-constrained-383zpt4kcg.pdf

Distributed Moving Horizon Estimation for Linear Constrained Systems

In this technical note, we consider a linear system structured into physically coupled subsystems and propose a decentralized control scheme capable to guarantee asymptotic stability and satisfaction of constraints on system inputs and states. The design procedure is totally decentralized, since the synthesis of a local controller uses only information on a subsystem and its neighbors, i.e. subsystems coupled to it. We show how to automatize the design of local controllers so that it can be carried out in parallel by smart actuators equipped with computational resources and capable to exchange information with neighboring subsystems. In particular, local controllers exploit tube-based Model Predictive Control (MPC) in order to guarantee robustness with respect to physical coupling among subsystems. Finally, an application of the proposed control design procedure to frequency control in power networks is presented.

/pdf/plug-and-play-decentralized-model-predictive-control-for-2t1642y0sg.pdf

Marcello Farina

Papers

Stochastic linear Model Predictive Control with chance constraints – A review

Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems

Distributed Moving Horizon Estimation for Linear Constrained Systems

Plug-and-Play Decentralized Model Predictive Control for Linear Systems

Forecasting peak air pollution levels using NARX models