An experience based iterative learning controller is proposed for a general class of robotic systems. Experience of the iterative learning controller is stored in the memory in terms of input output data and later used for the prediction of the initial control input for a new desired trajectory. It is proved in this paper that using this approach we can reduce the number of iterations to achiev...

This paper presents an iterative learning scheme for visionguided robot trajectory tracking. At first, a stability criterion for designing iterative learning controller is proposed. It can be used for a system with initial resetting error. By using the criterion, one can convert the design problem into finding a positive definite discrete matrix kernel and a more general form of learning contro...

In this paper Iterative Learning Control(ILC) algorithm is analysed for a linear-time invariant SISO model with the effect of output noise and its properties derived. If the original plant is positive, it is shown that by using a fixed learning gain algorithm, the tracking error will converge and be predicted. Finally, through computational experiments, we confirm the correctness of the propose...

The norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based normoptimal ilc algorithm where the controlled variables are not directly available as measurements. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc design is independent of the dynamics in the Kalman...

This paper deals with the application of Iterative Learning Control (ILC) to further improve the performance of teleoperation systems based on Smith predictor. The goal is to achieve robust stability and optimal transparency for these systems. The proposed control structure make the slave manipulator follow the master in spite of uncertainties in time delay in communication channel and model pa...

The area if iterative learning control (ILC) has emerged from robotics to form a new and exciting challenge for control theorists and practitioners. There is great potential for applications to systems with a naturally repetitive action where the transfer of data from repetition (trial or iteration) can lead to substantial improvements in tracking performance. Although many of the challenges me...

In iterative learning control (ILC), it is highly desirable to have a learning compensator with a unitgain for all frequencies, in order to avoid noise amplification and learning speed degradation during the learning process. In this paper, we show that the realization of a unit-gain compensator is straightforward in ILC, using both forward and backward filtering. As an illustrative example, a ...

Iterative learning control (ILC) is suitable for systems that are able to repeatedly complete several tasks over a fixed time interval. Since it was first proposed, ILC has been further developed through extensive efforts. However, there are few related results on systems with stochastic signals, where by stochastic signal we mean one that is described by a random variable. Stochastic iterative...

This paper proposes a non-model based approach to iterative learning control (ILC) via extremumseeking. Single-input–single-output discrete-time nonlinear systems are considered, where the objective is to recursively construct an input such that the corresponding system output tracks a prescribed reference trajectory as closely as possible on finite horizon. The problem is formulated in terms o...