Davood Babaei Pourkargar

Kansas State University | KSU · Department of Chemical Engineering

We focus on the Lyapunov-based output feedback control problem for a class of distributed parameter systems with spatiotemporal dynamics described by input-affine linear and semilinear dissipative partial differential equations (DPDEs). The control problem is addressed via model order reduction. Galerkin projection is applied to discretize the DPDE...

Process intensification can afford considerable benefits with respect to economics, sustainability and/or safety but also presents increased decision making challenges with respect to computational efficiency and flexibility across multiple temporal and spatial scales. Distributed decision making, that is, localized yet coordinated decision making...

A combined distributed moving horizon estimation and distributed model predictive control architecture is proposed to address the distributed output-feedback control problem for nonlinear process systems. Community detection based on modularity maximization is used to generate separate optimal decompositions for the estimation and control problems...

Equation graph representations are proposed for generic diffusion-convection-reaction systems described by parabolic partial differential equations. A measure of structural input-output closeness is proposed, and its relationship with the equation graph representation is established. A graph-based algorithm to obtain network decompositions for dist...

Distributed optimization, based on a decomposition of the entire optimization problem, has been applied to many complex decision making problems in process systems engineering, including nonlinear model predictive control. While decomposition techniques have been widely adopted, it remains an open problem how to optimally decompose an optimization...

This paper addresses the plant-wide control of the amine gas sweetening plant using distributed model predictive control. The plant is fed natural gas containing sour gases (hydrogen sulphide and carbon dioxide), which are removed by absorption in monoethanolamine solution. A plant decomposition algorithm based on modularity maximization for distri...

A comprehensive study of plant decomposition effects is presented for distributed model predictive control (DMPC) of an integrated process system. Different decompositions are obtained via community detection and other methods. The closed-loop performance and computational efficiency of employing various decompositions for DMPC design are evaluated...

We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale comp...

Input-output partitioning for decentralized control has been studied extensively using various methods, including those based on relative gains and those based on relative degrees and sensitivities. These two concepts are characterizations of long-time and short-time input-output response, respectively. This work proposes a unifying new input-outpu...

Distributed optimization, based on a decomposition of the entire optimization problem, has been applied to many complex decision making problems in process systems engineering, including nonlinear model predictive control. While decomposition techniques have been widely adopted, it remains an open problem how to optimally decompose an optimization...

This paper addresses the impact of decomposition on the closed-loop performance and computational efficiency of model predictive control (MPC) of nonlinear process networks. Distributed MPC structures with different communication strategies are designed for regulation of an integrated reactor-separator process. Different system decompositions are a...

This paper investigates the impact of control architecture design on the distributed model predictive control (MPC) of nonlinear complex process networks. A sequential distributed MPC structure is synthesized to regulate a nonlinear system whose dynamics are decomposed into multiple subsystems by community detection methods. The closed-loop perform...

This paper focuses on the development of a rigorous model for isothermal CO2 adsorption columns which describes the spatiotemporal dynamics of CO2 concentrations in the bulk and solid bed by a set of partial and location-varying ordinary differential equations. By considering both dispersion and convection phenomena, the model provides the spatiote...

The adaptive output feedback control problem of chemical distributed parameter systems is investigated while the process parameters are unknown. Such systems can be usually modeled by semi-linear partial differential equations (PDEs). A combination of Galerkin's method and proper orthogonal decomposition is applied to generate a reduced order model...

We focus on shaping the long-term spatiotemporal dynamics of transport-reaction processes which can be described by semi-linear partial differential equations (PDEs). The dynamic shaping problem is addressed via error dynamics regulation between the governing PDE and a target PDE which describes the desired spatiotemporal behavior. A model order re...

We focus on adaptive wave motion suppression of fluid flows in the presence of unknown parameters. The suppression problem is addressed by low-dimensional adaptive nonlinear output feedback controller synthesis. We employed adaptive proper orthogonal decomposition to recursively compute the set of empirical basis functions needed by the Galerkin pr...

This paper focuses on adaptive output feedback control of transport-reaction processes described by semi-linear parabolic partial differential equations (PDEs) in the presence of unknown reaction parameters. Galerkin projection is applied to derive a low-dimensional reduced order model which employed as the basis for the adaptive controller design....

We consider the control problem of dissipative distributed parameter systems described by semilinear parabolic partial differential equations with unknown parameters and its application to transportreaction chemical processes. The infinite dimensional modal representation of such systems can be partitioned into finite dimensional slow and infinite...

The synthesis of a model-based control structure for general linear dissipative distributed parameter systems (DPSs) is explored in this manuscript. Discrete-time distributed state measurements (called process snapshots) are employed by a continuous-time regulator to stabilize the process. The main objective of this paper is to identify a criterion...

The output feedback control problem for a class of nonlinear distributed parameter systems with limited number of continuous measurement sensors that describes a wide range of physico-chemical systems is investigated using adaptive proper orthogonal decomposition (APOD) method. Specifically, APOD is used to initiate and recursively revise locally v...

We focus on the output tracking problem of distributed parameter systems (DPSs) which can be described by a set of nonlinear dissipative partial differential equations (PDEs). The infinite-dimensional modal representation of such systems in appropriate subspaces can be decomposed to finite-dimensional slow and probably unstable, and infinite-dimens...

We focus on model-based networked control of general linear dissipative distributed parameter systems, the infinite dimensional representation of which can be decomposed to finite-dimensional slow and infinite-dimensional fast and stable subsystems. The controller synthesis of such systems is addressed using adaptive proper orthogonal decomposition...

We consider the output tracking problem of spatially distributed processes described by nonlinear dissipative partial differential equations (DPDEs). The infinite dimensional representation of such systems can be decomposed to finite dimensional slow and infinite dimensional fast and stable subsystems. To circumvent the important issues of controll...

We focus on output feedback control of distributed processes whose infinite dimensional representation in appropriate Hilbert subspaces can be decomposed to finite dimensional slow and infinite dimensional fast subsystems. The controller synthesis issue is addressed using a refined adaptive proper orthogonal decomposition (APOD) approach to recursi...

This article focuses on output feedback control of distributed parameter systems with limited number of sensors employing adaptive proper orthogonal decomposition (APOD) methodology. The controller design issue is addressed by combining a robust state controller with a dynamic observer of the system states to reduce sensor requirements. The use of...

In this article two identical generalized Lorenz systems have been synchronized by a fuzzy controller based on mamdani approach and stability of the proposed scheme has been established by the Lyapunov stability theorem. Controller parameters have been optimized by the genetic algorithm. Effectiveness of proposed method has been demonstrated throug...