Data-Driven Approximated Optimal Control for Chemical Processes with State and Input Constraints

Mingfang He

doi:10.1155/2019/1396913

Abstract

Input and state constraints widely exist in chemical processes. The optimal control of chemical processes under the coexistence of inequality constraints on input and state is challenging, especially when the process model is only partially known. The objective of this paper is to design an applicable optimal control for chemical processes with known model structure and unknown model parameters. To eliminate the barriers caused by the hybrid constraints and unknown model parameters, the inequality state constraints are first transformed into equality state constraints by using the slack function method. Then, adaptive dynamic programming (ADP) with nonquadratic performance integrand is adopted to handle the augmented system with input constraints. The proposed approach requires only partial knowledge of the system, i.e., the model structure. The value information of the model parameters is not required. The feasibility and performance of the proposed approach are tested using two nonlinear cases including a continuous stirred-tank reactor (CSTR) example.

Highlights

Constraints on input and state commonly exist in chemical processes due to nite capability of the actuators [1], safety limits [2], requirements on product quality, and environmental regulations [3]
The value of manipulated variables is constrained in a certain range de ned by the operating instructions
Erefore, the ability to handle constraints is an essential concern in the control design and synthesis of real chemical processes [4, 5]

Summary

Introduction

Constraints on input and state commonly exist in chemical processes due to nite capability of the actuators [1], safety limits [2], requirements on product quality, and environmental regulations [3]. Since the 1960s, numerous approaches have been proposed to handle the input and/or state constraints in the optimal control of linear/nonlinear systems. With the increased unavailability of quality raw materials, it is imperative that raw materials of low grade with large variations should be employed in the production in order to maximize the use of resources In this context, the operation of some chemical processes exhibit complexity in terms of variable dynamic characteristics, strong nonlinearities, heavy coupling, unclear mechanism, and mathematically unmodelable parts. Chi et al [48] studied the constrained data-driven iterative learning control (ILC) for point-to-point optimal control problem of discrete-time nonlinear systems with input and output constraints. E aim of this study is to design an approximated optimal control algorithm for continuous-time nonlinear chemical processes with both state and input constraints.

Problem Formulation and Preliminaries

Handling Input Constraints by Using Nonquadratic

Case Study

Conclusions