Decentralized optimization, with application to multiple aircraft coordination

Abstract
We present a decentralized optimization method for solving the coordination problem of interconnected non- linear discrete-time dynamic systems with multiple deci- sion makers. The optimization framework embeds the in- herent structure in which each decision maker has a math- ematical model that captures only the local dynamics and the associated interconnecting global constraints. A glob- ally convergent algorithm based on sequential local op- timizations is presented. Under assumptions of difieren- tiability and linear independence constraint qualiflcation, we show that the method results in global convergence to †-feasible Nash solutions that satisfy the Karush-Kuhn- Tucker necessary conditions for Pareto-optimality. We apply this methodology to a multiple unmanned air vehi- cle system, with kinematic aircraft models, coordinating in a common airspace with separation requirements be- tween the aircraft.

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