An Adaptive Distributed Consensus Control Algorithm Based on Continuous Terminal Sliding Model for Multiple Quad Rotors’ Formation Tracking

Abstract

Distributed consensus formation tracking problem of slow convergence and low accuracy for multiple quad rotors are solved in this paper. Continuous nonsingular terminal sliding model algorithm (CNTSMA) under Inner-Outer loop control structure is considered to deal with the control problem of nonlinear, strong coupling and underactuated characteristic of a quad rotor. For better convergence and higher tracking accuracy, an adaptive algorithm (denoted by ACNTSMA) further focused on coupling of x and y directions, with sliding model observer (SMO) to compensate system disturbances and uncertainties is proposed. Furthermore, to solve the consensus formation tracking problem, a consensus control protocol under a full-connected topology based on ACNTSMA is proposed, including formation pattern consensus controller and formation distance consensus controller. Stability theory and simulation results prove the proposed control algorithm is able to accurately achieve the first-order (position and velocity) consensus for multiple quad rotors' formation tracking. Besides, stability analysis of the proposed consensus control protocol illustrates that all sliding modes with finite-time stability applicable to second-order integral models are supposed to be established. And the proposed controller also has better convergence and faster response speed with no chattering for trajectory tracking. INDEX TERMS Distributed formation tracking, consensus, continuous terminal sliding model algorithm, adaptive algorithm, multiple quad rotors, convergence.

Adaptive Interval Type-2 Fuzzy Logic Control of a Three Degree-of-Freedom Helicopter

“…Robust second-order consensus tracking controller that achieves tracking without calculating the velocity of the target is established in [25]. In [26], a linear time-invariant controller is designed to improve tracking and validation is done with experimentally. A model-based adaptive controller with Riccati equations is investigated in [27].…”