Simulation and control of aggregate surface morphology in a two-stage thin film deposition process for improved light trapping

a b s t r a c t This work focuses on the development of a model predictive control algorithm to simultaneously regulate the aggregate surface slope and roughness of a thin film growth process to optimize thin film light reflectance and transmittance. Specifically, a two-stage thin film deposition process, which involves two microscopic processes: an adsorption process and a migration process, is modeled based on a one-dimensional solid-on-solid square lattice. The first stage of this process utilizes a uniform deposition rate profile to control the thickness of the thin film and the second stage of the process utilizes a spatially distributed deposition rate profile to control the surface morphology of the thin film. Kinetic Monte Carlo (kMC) methods are used to simulate this two-stage thin film deposition process. To characterize the surface morphology and to evaluate the light trapping efficiency of the thin film, aggregate surface roughness and slope corresponding to length scale of visible light are introduced as the root-mean squares of the aggregate surface height profile and aggregate surface slope profile. An Edwards-Wilkinson (EW)-type equation with appropriately computed parameters is used to describe the dynamics of the surface height profile and predict the evolution of the aggregate root-mean-square (RMS) roughness and aggregate RMS slope. A model predictive control algorithm is then developed on the basis of the EW equation model to regulate the aggregate RMS slope and the aggregate RMS roughness at desired levels. Closed-loop simulation results demonstrate the effectiveness of the proposed model predictive control algorithm in successfully regulating the aggregate RMS slope and the aggregate RMS roughness at desired levels that optimize thin film light reflectance and transmittance

DYNAMIC FEEDFORWARD/OUTPUT FEEDBACK CONTROL OF NONLINEAR PROCESSES

Abstract--This paper addresses the feedforward/output feedback control problem for single-input singleoutput minimum-phase nonlinear processes. Combination of dynamic feedforward/static state feedback laws and state observers is employed to synthesize nonlinear dynamic feedforward/output feedback controllers that completely eliminate the effect of measurable disturbances and induce a desired input/output behavior. The developed methodology in applied to an exothermic continuous chemical reactor and extensive simulations illustrate the controller performance and robustness

Integrated optimal actuator/sensor placement and robust control of uncertain transport-reaction processes.

Abstract This paper focuses on transport-reaction processes with unknown time-varying parameters and disturbances described by quasi-linear parabolic PDE systems, and addresses the problem of computing optimal actuator/sensor locations for robust nonlinear controllers. Initially, Galerkin's method is employed to derive finite-dimensional approximations of the PDE system which are used for the synthesis of robust nonlinear state feedback controllers via geometric and Lyapunov techniques and the computation of optimal actuator locations. The controllers enforce boundedness and uncertainty attenuation in the closed-loop system. The optimal actuator location problem is subsequently formulated as the one of minimizing a meaningful cost functional that includes penalty on the response of the closed-loop system and the control action. Owing to the boundedness of the state, the cost is defined over a finite-time interval (the final time is defined as the time needed for the process state to become smaller than the desired uncertainty attenuation limit), while the optimization is performed over a broad set of initial conditions and time-varying disturbance profiles. Subsequently, under the assumption that the number of measurement sensors is equal to the number of slow modes, we employ a standard procedure for obtaining estimates for the states of the approximate finite-dimensional model from the measurements. The optimal location of the measurement sensors is computed by minimizing a cost function of the estimation error in the closed-loop infinite-dimensional system. We show that the use of these estimates in the robust state feedback controller leads to a robust output feedback controller, which guarantees boundedness of the state and uncertainty attenuation in the infinite-dimensional closed-loop system, provided that the separation between the slow and the fast eigenvalues is sufficiently large. We also establish that the solution to the optimal actuator/sensor problem, which is obtained on the basis of the closed-loop finite-dimensional system, is near-optimal in the sense that it approaches the optimal solution for the infinite-dimensional system as the separation between the slow and fast eigenvalues increases. The theoretical results are successfully applied to a typical diffusion-reaction process with nonlinearites and uncertainty to design a robust nonlinear output feedback controller and compute the optimal actuator/sensor locations for robust stabilization of an unstable steady state

Nonlinear feedback control . . .

This paper proposes a methodology for the synthesis of nonlinear finite-dimensional time-varying output feedback controllers for systems of quasi-linear parabolic Ž . partial differential equations PDEs with time-dependent spatial domains, whose dynamics can be partitioned into slow and fast ones. Initially, a nonlinear model reduction scheme, similar to the one introduced in Christofides and Daoutidis, J. Ž . Math. Anal. Appl. 216 1997 , 398᎐420, which is based on combinations of Galerkin's method with the concept of approximate inertial manifold is employed Ž . for the derivation of low-order ordinary differential equation ODE systems that yield solutions which are close, up to a desired accuracy, to the ones of the PDE system, for almost all times. Then, these ODE systems are used as the basis for the explicit construction of nonlinear time-varying output feedback controllers via geometric control methods. The controllers guarantee stability and enforce the output of the closed-loop parabolic PDE system to follow, up to a desired accuracy, a prespecified response for almost all times, provided that the separation of the slow and fast dynamics is sufficiently large. Differences in the nature of the model reduction and control problems between parabolic PDE systems with fixed and moving spatial domains are identified and discussed. The proposed control method is used to stabilize an unstable steady state of a diffusion-reaction process whose spatial domain changes with time. It is shown to lead to a significant reduction on the order of the stabilizing nonlinear output feedback controller and outperform a nonlinear controller synthesis method that does not account for the variation of the spatial domain

Modeling and Control of High-Velocity Oxygen-Fuel (HVOF) Thermal Spray: A Tutorial Review

This work provides a tutorial overview of recent research efforts in modeling and control of the high-velocity oxygen-fuel (HVOF) thermal spray process. Initially, the modeling of the HVOF thermal spray, including combustion, gas dynamics, particle in-flight behavior, and coating microstructure evolution is reviewed. The influence of the process operating conditions as predicted by the fundamental models on particle characteristics and coating microstructure is then discussed and compared with experimental observations. Finally, the issues of measurement and automatic control are discussed and comments on potential future research efforts are made