In this paper, we study the Robust Minimal Controllability and Observability Problem (rMCOP). The scenario that motivated this question is related to the design of a drone formation to execute some task, where the decision of which nodes to equip with a more expensive communication system represents a critical economic choice. Given a linear time-invariant system for each of the vehicles, this problem consists of identifying a minimal subset of state variables to be actuated and measured, ensuring that the overall formation model is both controllable and observable while tolerating a prescribed level of inputs/outputs that can fail. Based on the tools in the available literature, a naive approach would consist of enumerating separately all possible minimal solutions for the controllability and observability parts. Then, iterating over all combinations to find the maximum intersection of sensors/actuators in the independent solutions, yielding a combinatorial problem. The presented solution couples the design of both controllability and observability parts through a polynomial reformulation as a minimum set multi-covering problem under some mild assumptions. In this format, the algorithm has the following interesting attributes: (i) only requires the solution of a single covering problem; 9ii) using polynomial approximations algorithms, one can obtain close-to-optimal solutions to the rMCOP.
K E Y W O R D Scontrol applications, control design, minimal controllability and observability problem, robustness
INTRODUCTIONConsidering a Multi-Agent System (MAS) composed of vehicles interconnected by a communication network is a recurrent proposal for surveillance, exploration, and measuring tasks to be accomplished by unmanned and automatic robotic systems. Missions entailing the use of a large number of such vehicles can adopt a leader/followers approach 12 characterized by having: (i) expensive nodes (leaders) that can communicate with a ground station to receive mission commands and that might be equipped with sophisticated sensors or localization equipment: (ii) cheaper drones (followers) implementing local controllers based on onboard sensors that measure relative localization and receive a small amount of data from the leaders. In this scenario shown in Figure 1, a critical task is to minimize the number of leaders for economic reasons without compromising the controllability and observability of the overall system. In distributed environments, we should ensure these two critical properties. 3,4 We will refer to this challenge as the Minimal Controllability and Observability Problem (MCOP).