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

Abstract

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.

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

“…This simplicity aids usability but necessarily degrades performance. Thus, the authors in [141], [142] and [143] used the optimization based ILC approaches to address these limitations. In [141] and [142], the author examines the performance of ILC in gradient-based that enhances a quadrotor's controllability and stability during attitude control.…”

“…Thus, the authors in [141], [142] and [143] used the optimization based ILC approaches to address these limitations. In [141] and [142], the author examines the performance of ILC in gradient-based that enhances a quadrotor's controllability and stability during attitude control. Again in [143], the optimization based ILC approach has applied by researchers at ETH Zurich to achieve quadrotor trajectory tracking while balancing an inverted pendulum.…”

An Experience Transfer Approach for the Initial Data of Iterative Learning Control

“…In this example, the driving power of the manipulator is supposed to increase 20%. The ILC experience transformation of the original system is carried according to Equation (13). The simulation results of the two kinds of ILC with amplitude adjusting are shown in Table 3 and Figure 8.…”

“…Liu proposed a flexible ILC for systems with similar desired trajectories by means of Cartesian product [12]. Foudeh proposed gradient-based norm optimal ILC for unmanned aerial vehicles to cope with exogenous disturbances caused by wind gusts [13]. Chen developed a distributed controller to solve the leader-follower consensus of multiple flexible manipulators with uncertain parameters, unknown disturbances, and actuator dead zones, in which iterative learning is used to handle the repeatable disturbances [14].…”

Context Aware Control Systems: An Engineering Applications Perspective

“…Thus, intention-awareness is achieved by counterfactual reasoning. A quadrotor UAV used for surveillance and overhead monitoring is presented in [122] based on context aware iterative learning control strategies. A two axis positioning mechanism is analyzed in [123] based on a context aware hierarchical control system approach.…”