Eduardo Camponogara

Distributed model predictive control

The article presents results for distributed model predictive control (MPC), focusing on i) the coordination of the optimization computations using iterative exchange of information and ii) the stability of the closed-loop system when information is exchanged only after each iteration. Current research is focusing on general methods for decomposing large-scale problems for distributed MPC and methods for guaranteeing stability when multiple agents are controlling systems subject to abrupt changes.

Distributed Model Predictive Control: Synchronous and Asynchronous Computation

Model predictive control (MPC) has become one of the leading technologies to control complex processes, to a great extent, as a result of its flexibility and explicit handling of constraints. Given a dynamic problem (DP), MPC converts DP into a series of static optimization problems, thereby allowing the use of standard optimization techniques to compute the control signals. The reliance of MPC on centralized computations, however, stands as a barrier to its use in the real-time operation of large dynamic networks. To this end, this paper proposes an extension to MPC by decomposing DP into a network of small but coupled subproblems and solving them with a network of asynchronous agents. The net result, after each agent applies MPC to its dynamic subproblem, is a series of sets of static subproblems. Our focus is on the simultaneous solution of these sets of static subproblems. The paper delivers a framework to carry out the decomposition and develops conditions under which the iterative synchronous processes of the agents converge to solutions. Furthermore, it proposes heuristics for asynchronous convergence and reports experimental results from prototypical dynamic networks, demonstrating the effectiveness of the proposed extension.

Collaboration strategy for autonomous highly specialized robots

The typical planning, design or operations problem has multiple objectives and constraints. Such problems can be solved using only autonomous agents, each specializing in a small and distinct subset of the overall objectives and constraints. No centralized control is necessary. Instead, agents collaborate by observing and modifying one anothers work. Convergence to good solutions for a variety of real and academic problems has been obtained by embedding a few simple rules in each agent. The paper develops these rules and illustrates their use.