Saturated D-type ILC for Multicopter Trajectory Tracking Based on Additive State Decomposition

Ke, Chenxu; Ren, Jinrui; Quan, Quan

doi:10.1109/ddcls.2018.8516095

Cited by 1 publication

(1 citation statement)

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“…ILC has been applied to quadrotors using various approaches. These include derivative-type ILC [17], Proportional-Interactive-Derivative (PID-type) ILC [18], and basic optimal ILC approaches [19] that assume the reference (r) is specified over the entire finite time horizon, [0, T], and they also do not require a model. On the other hand, ILC has been applied to some complicated systems, such as precise speed-tracking control of a robotic fish [20], for multi-agent systems in [21], and recently in [22] for the distributed ILC of multiple flexible manipulators in the presence of uncertain disturbances and actuator dead zones.…”

Section: Introductionmentioning

confidence: 99%

Application of Norm Optimal Iterative Learning Control to Quadrotor Unmanned Aerial Vehicle for Monitoring Overhead Power System

2020

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Wind disturbances and noise severely affect Unmanned Aerial Vehicles (UAV) when monitoring and finding faults in overhead power lines. Accordingly, we propose repetitive learning as a new solution for the problem. In particular, the performance of Iterative Learning Control (ILC) that are based on optimal approaches are examined, namely (i) Gradient-based ILC and (ii) Norm Optimal ILC. When considering the repetitive nature of fault-finding tasks for electrical overhead power lines, this study develops, implements and evaluates optimal ILC algorithms for a UAV model. Moreover, we suggest attempting a learning gain variation on the standard optimal algorithms instead of heuristically selecting from the previous range. The results of both simulations and experiments of gradient-based norm optimal control reveal that the proposed ILC algorithm has not only contributed to good trajectory tracking, but also good convergence speed and the ability to cope with exogenous disturbances such as wind gusts.

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