Emmanuel Fernandez

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This paper presents the application of a framework, proposed by the National Institute of Standards and Technology (NIST), for standard modular simulation in semiconductor wafer fabrication facilities (<i>fabs</i>). The application of the proposed framework resulted in the identification and specification of four different elements in the context of(More)
— This paper presents the application of an approximate dynamic programming (ADP) algorithm to the problem of job releasing and sequencing of a benchmark reentrant manufacturing line (RML). The ADP approach is based on the SARSA(λ) algorithm with linear approximation structures that are tuned through a gradient-descent approach. The optimization is(More)
2123 gorithm, and with an overall computation time of less than 10 s, the value of min = 0:729 is obtained with the following controller: K 2 (z) = 0:802(z 0 0:6347)(z 0 0:1887) (z 0 1)(z + 1:156) : (17) V. CONCLUSIONS A new convex parameterization of all H 1 stabilizing controllers for SISO-LTI systems is given based on a new concept of Common Lya-punov(More)
— This paper presents an optimal policy for the problems of job releasing and sequencing in an adapted version of a benchmark Reentrant Manufacturing Line (RML). We consider a finite state space and an infinite horizon discounted cost optimization criteria. The resulting optimal policy provides a trade-off between throughput maximization (i.e., profits) and(More)
This paper presents the application of a reinforcement learning (RL) approach for the near-optimal control of a re-entrant line manufacturing (RLM) model. The RL approach utilizes an algorithm based on a gradient-descent TD(λ) method to obtain both estimates of the optimal cost function and the control actions. Numerical experiments demonstrated the(More)
SUMMARY In this paper, we present robust adaptive controller design for SISO linear systems with zero relative degree under noisy output measurements. We formulate the robust adaptive control problem as a nonlinear H ∞-optimal control problem under imperfect state measurements, and then solve it using game theory. By using the a priori knowledge of the(More)
— This paper presents initial results on the application of a simulation-based Approximate Dynamic Programming (ADP) approach for the optimization of Preventive Maintenance (PM) scheduling decisions in semiconductor manufacturing systems. In particular, the so-called Intel Mini-Fab benchmark is used as an illustrative example. Our approach is based on an(More)
This paper presents initial results on the application of a simulation-based Approximate Dynamic Programming (ADP) for the control of the benchmark model of a semiconductor fab denominated the Intel Mini-Fab. The ADP approach utilized is based on an Average Cost Temporal-Difference TD(&#955;) learning algorithm and under an Actor-Critic architecture.(More)
The sequential structure of complex actions is apparently learned at an abstract " cognitive " level in several regions of the frontal cortex, independent of the control of the immediate effectors by the motor system. At this level, actions are represented in terms of kinematic parameters – especially direction of end effector movement – and encoded using(More)