![lc smith college of engineering and computer science lc smith college of engineering and computer science](https://www.pdffiller.com/preview/252/770/252770602.png)
![lc smith college of engineering and computer science lc smith college of engineering and computer science](http://docplayer.net/docs-images/48/18409777/images/page_5.jpg)
In an iterative algorithm is proposed by which distributed controllers determine the optimal localized feedback gains using gradient methods. Over the years a body of literature has been developed that addresses the problem of distributed control of interconnected systems –.
![lc smith college of engineering and computer science lc smith college of engineering and computer science](https://img.yumpu.com/32655550/1/190x245/pdf-file-lc-smith-college-of-engineering-and-computer-science-.jpg)
A strong motivation for use of such inherently local controllers comes from, where it was demonstrated that the dependence of a controller on information coming from other parts of the system decays exponentially as one moves away from that controller. A preferred alternative is to have control signals computed using only local communication among neighboring subsystems. This is an undesirable scenario for large networks of dynamical systems, owing to its excessive communication requirements. This means that even if the subsystems interact locally, the optimal controller will need global information in order to produce the feedback signal.
LC SMITH COLLEGE OF ENGINEERING AND COMPUTER SCIENCE FULL
INTRODUCTION It is well-known that the feedback gain obtained from solving an LQR problem for a system with banded statespace matrices is, in general, a full matrix. Index Terms- Distributed optimization, dual decomposition, subgradient algorithm, localized cooperative control, multivehicle systems. Convergence properties of the latter algorithm are improved by employing a relaxed version of the augmented Lagrangian method, and numerical examples are provided to demonstrate the utility of our results. We develop an algorithm whereby vehicles can compute structured feedback gains in a localized manner. We then assume a structured feedback gain relationship between the state and actuation signals, and reformulate the optimization problem to find the optimal feedback gains. In particular, we demonstrate that each vehicle only needs to receive the primal variable of the vehicle ahead and the dual variable of the vehicle behind. This produces the optimal control law in a localized manner, in the sense that vehicles can iteratively compute their primal and dual variables by only communicating with their immediate neighbors. Jovanović Abstract- We use the dual decomposition method along with the dual subgradient algorithm to decouple the linear quadratic optimal control problem for a system of single-integrator vehicles. On the Dual Decomposition of Linear Quadratic Optimal Control Problems for Vehicular Formations Makan Fardad, Fu Lin, and Mihailo R.