In this paper, the effect of initial state error in the iterative learning control (ILC) system is studied. First, the previous result that the performance of D-type ILC algorithm can be improved by adding a P-term of error in the algorithm is generalized to PID-type algorithm. Then, robustness is investigated against the initial state error of the generalized ILC algorithm. It is also shown that the trend of error reduction can be effectively controlled by tuning gains of the proposed controller. In order to confirm validity of the proposed ILC algorithm, several examples are presented.

In the paper, it is first observed that when a typical iterative learning control (ILC) algorithm is applied to a class of linear dynamic systems with time delay, erratic estimation of delay time may cause the control input to diverge. To resolve such a difficulty due to uncertainty of the delay time, a new ILC algorithm is proposed in which a holding mechanism is adopted to hold the control input at a constant value for the duration of the delay time uncertainty. As a consequence, the output of the system tracks a given desired trajectory at the discrete points which are spaced by the size of the uncertainty of delay time. A numerical example is given to show the effectiveness of the proposed algorithm.

An iterative learning control algorithm is presented for a class of linear discrete-time dynamic systems with unknown but periodic parameters. Then a sufficient condition for convergency of the iterative algorithm is provided. It is also shown that the result can be extended to a class of nonlinear unknown systems. To show the effectiveness of the algorithms, two examples are given.

A robust step detection method for pedestrian navigation systems is presented. The method utilises a two-axis accelerometer and is based on an acceleration pattern of a step. Experimental results show that the method gives robust performance for ground surface variation and walking behaviour.

SUMMARY: An iterative learning control method is proposed for a class of non-linear dynamic systems with uncertain parameters. The method, in which non-linear system model is used, employs the model algorithmic control concept in the iteration sequence. A sufficient condition for convergency is provided. Then the method is shown to be applicable to continuous-path control of a robot manipulator.