Iterative learning control applies to systems that repeatedly execute the same finite duration task. The distinguishing feature of this form action is all data generated on a previous execution task available compute for subsequent execution. This paper uses repetitive process stability analysis and optimization techniques design dynamic controller that, in contrast designs process/2D setting, ...

This paper proposes a necessary and sufficient condition of convergence in the 2 L norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance co...

Empirical results have shown that many spoken language identification systems based on hand-coded features perform poorly on small speech samples where a human would be successful. A hypothesis for this low performance is that the set of extracted features is insufficient. A deep architecture that learns features automatically is implemented and evaluated on several datasets.

This paper presents two techniques in iterative learning identification (ILI) when the zero initial state condition is not achieved. One is to obtain acceptable impulse responses. The other is to measure the response error to the exclusion of non-zero initial state factor. This paper proposes an estimation technique using the least-squares (LS) method for the former and introduces discarded dat...

Abduction is the process of generating and choosing models, hypotheses and data analyzed in response to surprising findings. All good empirical economists abduct. Explanations usually evolve as studies evolve. The abductive approach challenges economists to step outside the framework of received notions about the "identification problem" that rigidly separates the act of model and hypothesis cr...

This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition based least squares iterative algorithm is presented for estimating the parameter vectors in each subsystem. Moreover, a data filtering based decomposition least squares iterative algorithm is proposed. The simulation...

Iterative learning control is a control technique used for the tracking of a finite duration trajectory. Iterative learning control (ILC) with focus on speed of tracking specific points and tracking error on these points is analyzed in this paper. A technique is introduced which employs the receding horizon optimization to track the points along with the iterative learning control is introduced...

Iterative learning control (ILC) is used to control systems that operate in a repetitive mode, improving tracking accuracy of the control by transferring data from one repetition of a task, to the next. In this paper an optimal iterative learning algorithm for discrete linear systems is designed and implemented. The design and implementation that have been done using Matlab® 7 and Simulink are ...

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...