Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach

The effective control of the COVID-19 pandemic is one the most challenging issues of recent years. The design of optimal control policies is challenging due to a variety of social, political, economical and epidemiological factors. Here, based on epidemiological data reported in recent studies for the Italian region of Lombardy, which experienced one of the largest and most devastating outbreaks in Europe during the first wave of the pandemic, we present a probabilistic model predictive control (PMPC) approach for the systematic study of what if scenarios of social distancing in a retrospective analysis for the first wave of the pandemic in Lombardy. The performance of the proposed PMPC was assessed based on simulations of a compartmental model that was developed to quantify the uncertainty in the level of the asymptomatic cases in the population, and the synergistic effect of social distancing during various activities, and public awareness campaign prompting people to adopt cautious behaviors to reduce the risk of disease transmission. The PMPC takes into account the social mixing effect, i.e. the effect of the various activities in the potential transmission of the disease. The proposed approach demonstrates the utility of a PMPC approach in addressing COVID-19 transmission and implementing public relaxation policies

Plasma-enhanced chemical vapor deposition: modeling and control.

Abstract This paper focuses on modeling and control of a single-wafer parallel electrode plasma-enhanced chemical vapor deposition process with showerhead arrangement used to deposit a 500 A s amorphous silicon thin "lm on an 8 cm wafer. Initially, a twodimensional unsteady-state model is developed for the process that accounts for di!usive and convective mass transfer, bulk and deposition reactions, and nonuniform #uid #ow and plasma electron density pro"les. The model is solved using "nite-di!erence techniques and the radial nonuniformity of the "nal "lm thickness is computed to be almost 19%. Then, a feedback control system is designed and implemented on the process to reduce the "lm thickness nonuniformity. The control system consists of three spatially distributed proportional integral controllers that use measurements of the deposition rate at several locations across the wafer, to manipulate the inlet concentration of silane in the showerhead and achieve a uniform deposition rate across the wafer. The implementation of the proposed control system is shown to reduce the "lm thickness radial nonuniformity to 3.8%

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

Study on ADRC Parameter Optimization Using CPSO for Clamping Force Control System

Clamping force control system is essential for clamping tasks that require high precision. In this paper, Active Disturbance Rejection Controller (ADRC) is applied for clamping force control system, aiming to achieve higher control precision. Furthermore, the CPSO-ADRC system is proposed and implemented by optimizing the critical parameters of ordinary ADRC using chaos particle swarm optimization (CPSO) algorithm. To verify the effectiveness of CPSO-ADRC, Particle Swarm Optimization- (PSO-) ADRC is introduced as a comparison. The simulation results show that the CPSO-ADRC can effectively improve the control quality with faster dynamic response and better command tracking performance compared to ordinary ADRC and PSO-ADRC