Passification based signal-parametric adaptive controller for agents in formation∗∗The work was performed in the IPME RAS, supported by RSF (grant 14-29-00142).

Abstract

In the paper the novel method for ensuring an overall formation stability for the case of significant variations of the agents parameters in formation is proposed. The local agent controller is designed by employment of the signal-parametric adaptive strategy, based on the passification method. The approach is demonstrated by an example of altitude control of the quadrocopter formation. The simulation results for a group of 25 quadrocopters are given, showing efficiency of the suggested method.

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

“…V = e x1 ėx1 + my x ẏx + e y1 ėy1 + my y ẏy + e h1 ėh1 + my h ẏh . (28) Grouping the terms for the x, y, and h axes in (28) results in…”

“…Therefore, to improve formation positioning accuracy, a consensus algorithm (protocol) can be applied for multi-agent formation control. Unlike [28,31,32], in which the reference position r(t) is known only to a single UAV in the formation ("the leading UAV"), here we assume, as above, that each UAV has information about r(t). This makes it possible to employ the consensus-based approach described below instead of relying on the leader's position tracking.…”

“…In [28], a method was proposed to ensure the overall stability of a quadrotor-type UAV formation with significant variations in the parameters of individual UAVs. It was presumed that the desired trajectory is known only to a single UAV in the formation ("the leading UAV").…”

Historical Overview of the Passification Method and its Applications to Nonlinear and Adaptive Control Problems

“…In [71], the problem of synchronization by states in the network of identical linear agents is considered by applying consensus output feedback. In the series of papers [72]- [74] the IRM adaptive controller is used for control of quadrotors. Decentralized adaptive synchronization of nonlinear dynamic networks with delay is described in [75].…”