Neighbor Learning Control: Learning Control for Multiple Subsystems

2007 ◽  
Author(s):  
Manas C. Menon ◽  
H. Harry Asada
Keyword(s):  
Control Systems ◽  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Control Algorithms ◽  
Good Convergence ◽  
Control Techniques ◽  
Difficult Time ◽  
Control Learning ◽  
Build Systems

With the rise of smart material actuators, it has become possible to design and build systems with a large number of small actuators. Many of these actuators exhibit a host of nonlinearities including hysteresis. Learning control algorithms can be used to guarantee good convergence of these systems even in the presence of the nonlinearities. However, they have a difficult time dealing with certain classes of noise or disturbances. We present a neighbor learning algorithm to control systems of this type with multiple identical actuators. In addition, we present a neighbor learning algorithm to control these systems for a certain class of non-identical actuators. We prove that in certain situations these algorithms provide improved convergence when compared to traditional iterative learning control techniques. Simulations results are presented that corroborate our expectations from the proofs.

Eliminating Structural Error in Real-Time Adjustable Four-Bar Path Generators Using Iterative Learning Control

2004 ◽  
Author(s):  
Hong-Jen Chen ◽  
Richard W. Longman ◽  
Meng-Sang Chew
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Path Generation ◽  
Structural Error ◽  
Four Bar Linkage

Fundamental concepts of Iterative Learning Control (ILC) are applied to path generating problems in mechanisms. As an illustration to such class of problems, an adjustable four-bar linkage is used. The coupler point of a four-bar traces a coupler curve that will in general deviate from the desired coupler path. Except at the precision points, the coupler curve will exhibit some structural error, which is the deviation from the specified curve. The structural error will repeat itself every cycle at exactly the same points over the range of interest. Since ILC is a methodology that was developed to handle similar repetitive errors in control systems, it is believed that it will be well served to apply it to this class of problems. Results show that ILC can be simple to implement, and it is found to be very well suited for such path generation problems.

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

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3223
Author(s):  
Husam A. Foudeh ◽  
Patrick Luk ◽  
James Whidborne
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Power Lines ◽  
Learning Gain ◽  
Good Convergence ◽  
Gain Variation ◽  
Exogenous Disturbances ◽  
Gradient Based ◽  
Overhead Power Lines

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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