Publications

Proceedings of the American Control Conference

Driessen, Brian D.; Sadegh, N.; Kwok, Kwan S.

In this paper an optimization-based method of drift prevention is presented for learning control of underdetermined linear and weakly nonlinear time-varying dynamic systems. By defining a fictitious cost function and the associated model-based sub-optimality conditions, a new set of equations results, whose solution is unique, thus preventing large drifts from the initial input. Moreover, in the limiting case where the modeling error approaches zero, the input that the proposed method converges to is the unique feasible (zero error) input that minimizes the fictitious cost function, in the linear case, and locally minimizes it in the (weakly) nonlinear case. Otherwise, under mild restrictions on the modeling error, the method converges to a feasible sub-optimal input.

Conference Proceedings - IEEE SOUTHEASTCON

Driessen, Brian D.; Sadegh, N.; Kwok, Kwan S.

In this paper we present a very simple modification of the iterative learning control algorithm of Arimoto et al [1] to the case where the inputs are bounded. The Jacobian condition presented in Avrachenkov [2] is specified instead of the usual condition specified by Arimoto et al [1]. (See also Moore [11].) In particular, the former is a condition for monotonicity in the distance to the solution instead of monotonicity in the output error. This observation allows for a simple extension of the methods of Arimoto et al [1] to the case of bounded inputs since the process of moving an input back to a bound if it exceeds it does not affect the contraction mapping property; in fact, the distance to the solution, if anything, can only decrease even further. The usual Jacobian error condition, on the other hand, is not sufficient to guarantee the chopping rule will converge to the solution, as proved herein. To the best of our knowledge, these facts have not been previously pointed out in the iterative learning control literature.

Kwok, Kwan S.; McDonald, Michael J.

The Intelligent Systems and Robotics Center (ISRC) at Sandia National Laboratories (SNL) is developing technologies for glovebox size reduction in the DOE nuclear complex. A study was performed for Kaiser-Hill (KH) at the Rocky Flats Environmental Technology Site (RFETS) on the available technologies for size reducing the glovebox lines that require size reduction in place. Currently, the baseline approach to these glovebox lines is manual operations using conventional mechanical cutting methods. The study has been completed and resulted in a concept of the robotic system for in-situ size reduction. The concept makes use of commercially available robots that are used in the automotive industry. The commercially available industrial robots provide high reliability and availability that are required for environmental remediation in the DOE complex. Additionally, the costs of commercial robots are about one-fourth that of the custom made robots for environmental remediation. The reason for the lower costs and the higher reliability is that there are thousands of commercial robots made annually, whereas there are only a few custom robots made for environmental remediation every year. This paper will describe the engineering analysis approach used in the design of the robotic system for glovebox size reduction.