This paper presents the iterative learning control for the industrial robot manipulator that performs repeated tasks. Motivated by human learning, the basic idea of iterative learning control is to use information from previous execution of a trial in order to improve performance from trial to trial. This is an advantage, when accurate model of the systems is not available. In this paper differ...

The SNS SRF system is operated with a pulsed beam. For the SRF system to track the repetitive reference trajectory, a feedback and a feedforward controllers has been proposed. The feedback controller is to guarantee the closed loop system stability and the feedforward controller is to improve the tracking performance for the repetitive reference trajectory and to suppress the repetitive disturb...

An iterative learning based economic model predictive controller (ILEMPC) is proposed for repetitive tasks in this paper. Compared with existing works, the initial feasible trajectory of the proposed ILEMPC is not restricted to be convergent to an equilibrium so it can handle various types of control objectives: stabilization, tracking a periodic trajectory and even pure economic optimization. ...

The paper analyzes the flow calculation of power system, using iterative learning algorithm to calculation the power flow, compare with traditional improving Newton etc. algorithm, Iterative learning algorithm has fast convergence can also be to achieve a high precision tracking. In this paper convergence of the algorithm is global, and gives control of the convergence conditions and rigorous t...

Iterative learning control (ILC) is a technique to realize system inversion in a run-to-run manner. Though most of the techniques presented in the literature consider zero tracking error between the desired and achieved outputs, perfect inversion is often not feasible and in many cases not even desirable. Approximate inversion with good convergence and robustness properties (at the cost of a no...

This paper addresses the initial shift problem in iterative learning control with system relative degree. The tracking error caused by nonzero initial shift is detected when applying a conventional learning algorithm. Finite initial rectifying action is introduced in the learning algorithm and is shown e3ective in the improvement of tracking performance, in particular robustness with respect to...

In many research laboratories, Iterative Learning Control (ILC) has proven itself to be a very effective technique to reduce repetitive control errors that occur in systems that continually perform the same motion or operation. Based on errors from previous operations, the technique iteratively constructs a feedforward signal, with which extremely small tracking errors are obtained [4, 51-Howev...

In this paper, we present our progress toward designing a “smart” high-peak power microwave (HPM) tube. We use iterative learning-control (ILC) methodologies in order to control a repetitively pulsed high-power backward-wave oscillator (BWO). The learning-control algorithm is used to drive the error between the actual output and its desired value to zero. The desired output may be a given power...

At present, most of the control methods of the lower extremity exoskeleton carrying robot require to calculate the system’s dynamic equation and create the control quantity at each moment, which don’t make full use of the repeatability feature of human movement and inevitably causes the delay on the dynamic response of the system. In this paper, utilizing the dynamic repeat characteristics of h...