Estimation-based iterative learning control

Abstract: In many  applications industrial robots perform the same motion  repeatedly. One way of compensating the repetitive part of the error  is by using iterative learning control (ILC). The ILC algorithm  makes use of the measured errors and iteratively calculates a  correction signal that is applied to the system.The main topic of the thesis is to apply an ILC algorithm to a  dynamic system where the controlled variable is not measured. A  remedy for handling this difficulty is to use additional sensors in  combination with signal processing algorithms to obtain estimates of  the controlled variable. A framework for analysis of ILC algorithms  is proposed for the situation when an ILC algorithm uses an estimate  of the controlled variable. This is a relevant research problem in  for example industrial robot applications, where normally only the  motor angular positions are measured while the control objective is  to follow a desired tool path. Additionally, the dynamic model of  the flexible robot structure suffers from uncertainties. The  behaviour when a system having these difficulties is controlled by  an ILC algorithm using measured variables directly is illustrated  experimentally, on both a serial and a parallel robot, and in  simulations of a flexible two-mass model. It is shown that the  correction of the tool-position error is limited by the accuracy of  the robot model.The benefits of estimation-based ILC is illustrated for cases when  fusing measurements of the robot motor angular positions with  measurements from an additional accelerometer mounted on the robot  tool to form a tool-position estimate. Estimation-based ILC is  studied in simulations on a flexible two-mass model and on a  flexible nonlinear two-link robot model, as well as in experiments  on a parallel robot. The results show that it is possible to improve  the tool performance when a tool-position estimate is used in the  ILC algorithm, compared to when the original measurements available  are used directly in the algorithm. Furthermore, the resulting  performance relies on the quality of the estimate, as expected.In the last part of the thesis, some implementation aspects of ILC  are discussed. Since the ILC algorithm involves filtering of signals  over finite-time intervals, often using non-causal filters, it is  important that the boundary effects of the filtering operations are  appropriately handled when implementing the algorithm. It is  illustrated by theoretical analysis and in simulations that the  method of implementation can have large influence over stability and  convergence properties of the algorithm.