Performance Evaluation of Run-to-Run Control Methods in Semiconductor Processes

Run-to-Run (RtR) control plays an important role in semiconductormanufacturing processes. In this paper, RtR control methods are classified and evaluated. The set-valued RtR controllers with ellipsoid approximation are compared with two typical RtR controllers: the Exponentially Weighted Moving Average (EWMA) controller and the Optimizing Adaptive Quality Controller (OAQC) by simulations according to the following criteria: A good RtR controller should be able to compensate for various disturbances, such as small drifts, step disturbances and model errors; moreover, it should be able to deal with bounds, cost requirement and multipletargets that are often encountered in semiconductor processes. Based on our simulation results, suggestions on selection of a proper RtR controller for a semiconductor process are given as conclusions

Extended Ellipsoidal Outer-Bounding Set-Membership Estimation for Nonlinear Discrete-Time Systems with Unknown-but-Bounded Disturbances

This paper develops an extended ellipsoidal outer-bounding set-membership estimation (EEOB-SME) algorithm with high accuracy and efficiency for nonlinear discrete-time systems under unknown-but-bounded (UBB) disturbances. The EEOB-SME linearizes the first-order terms about the current state estimations and bounds the linearization errors by ellipsoids using interval analysis for nonlinear equations of process and measurement equations, respectively. It has been demonstrated that the EEOB-SME algorithm is stable and the estimation errors of the EEOB-SME are bounded when the nonlinear system is observable. The EEOB-SME decreases the computation load and the feasible sets of EEOB-SME contain more true states. The efficiency of the EEOB-SME algorithm has been shown by a numerical simulation under UBB disturbances

Data-Driven Retrospective Cost Adaptive Control

This dissertation develops data-driven retrospective cost adaptive control (DDRCAC) and applies it to flight control. DDRCAC combines retrospective cost adaptive control (RCAC), a direct adaptive control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). DDRCAC uses elements of the identified model to construct the target model, which defines the retrospective performance variable. Using RLS-VRF, optimization of the retrospective performance variable updates the controller coefficients. This dissertation investigates the ability of RLS-VRF to provide the modeling information needed to construct the target model, especially nonminimum-phase (NMP) zeros, which are needed to prevent NMP-zero cancellation. A decomposition of the retrospective performance variable is derived and used to assess target-model matching and closed-loop performance. These results are illustrated by single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) examples with a priori unknown dynamics. Finally, DDRCAC is applied to several simulated flight control problems, including an aircraft that transitions from minimum-phase to NMP lateral dynamics, an aircraft with flexible modes, aeroelastic wing flutter, and a nonlinear planar missile.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169972/1/aseemisl_1.pd

Integrated motion planning and model learning for mobile robots with application to marine vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 269-275).Robust motion planning algorithms for mobile robots consider stochasticity in the dynamic model of the vehicle and the environment. A practical robust planning approach balances the duration of the motion plan with the probability of colliding with obstacles. This thesis develops fast analytic algorithms for predicting the collision probability due to model uncertainty and random disturbances in the environment for a planar holonomic vehicle such as a marine surface vessel. These predictions lead to a robust motion planning algorithm that nds the optimal motion plan quickly and efficiently. By incorporating model learning into the predictions, the integrated algorithm exhibits emergent active learning strategies to autonomously acquire the model data needed to safely and eectively complete the mission. The motion planner constructs plans through a known environment by concatenating maneuvers based upon speed controller setpoints. A model-based feedforward/ feedback controller is used to track the resulting reference trajectory, and the model parameters are learned online with a least squares regression algorithm. The path-following performance of the vehicle depends on the effects of unknown environmental disturbances and modeling error. The convergence rate of the parameter estimates depends on the motion plan, as different plans excite different modes of the system.(cont.) By predicting how the collision probability is affected by the parameter covariance evolution, the motion planner automatically incorporates active learning strategies into the motion plans. In particular, the vehicle will practice maneuvers in the open regions of the configuration space before using them in the constrained regions to ensure that the collision risk due to modeling error is low. High-level feedback across missions allows the system to recognize configuration changes and quickly learn new model parameters as necessary. Simulations and experimental results using an autonomous marine surface vessel are presented.by Matthew Greytak.Ph.D