Stochastic MPC with applications to process control

Abstract

This paper presents a model predictive control formulation for Networked Control Systems subject to independent and identically distributed delays and packet dropouts. The design takes into account the presence of a communication network in the control loop, resorting to a buffer at the actuator side to store and consistently apply delayed control sequences when fresh control inputs are not available. The proposed approach uses a statistical description of transmissions to optimize the expected future control performance conditioned upon the current system state, previously calculated control packets and transmission acknowledgments. Experimental studies using a quadruple tank process illustrate the applicability of the method to process control.

A Comparative Study of Stochastic Model Predictive Controllers

“…Stochastic model predictive control approaches were inspired by this type of systems, in which δ and w are stochastic in nature, independent and with known probability distributions. Since this statistical information is taken into account in the solution of the OCP [18][19][20][21], stochastic predictive control has been widely accepted and has been applied in different areas such as building air conditioning [37][38][39], renewable energy management [40,41], process control [3,42], robotics and automotive [5,22,[43][44][45]. A more extensive review of these and other applications is presented in [18,19,21,25,46], where network control systems, air traffic, finance, path planning and training control are discussed.…”

“…Model Predictive Control (MPC) is a widely used strategy for the control of industrial processes [1][2][3], robotics and automation [4][5][6], energy efficiency of buildings and renewable energies [7][8][9][10][11]. This is due to its "ability to predict" the future behavior of the real process, using an explicit model of it.…”

Comprehensive Predictive Control Method for Automated Vehicles With Delays

“…The updates of the control input received by the actuator can be described as a buffer policy [34]. Defining the command buffer of the actuator is b k (b k ∈ R a×n ) at the system time step k, the indicator of receiving a new sequence is w k , and the time step when the last most recent sequence received isk, then the dynamics of the buffer can be written as…”

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

“…Operation research and finance [69], [140]- [142] [30], [143] process control [17], [24], [54], [122], [138] [33]- [35], [55], [110] Robot and vehicle path planning [89], [144], [145] [93] [64] Telecommunication network control [146] Wind turbine control [26] DECEMBER 2016 « IEEE CONTROL SYSTEMS MAGAZINE 33…”

“…At the ith stage of the control policy, the control input ui is selected as the feedback control law ( ), i $ r that is, [31], [32] automotive applications [133] [28], [29] [134] [135] Building climate control [27], [84] Microgrids [136] [ 105] networked control systems [137], [138] [139]…”