Adaptive Neural Control and Modeling for Continuous Stirred Tank Reactor with Delays and Full State Constraints

Dongjuan Li,Ying Gao,Dongxing Wang,Dan Selişteanu

doi:10.1155/2021/9948044

Dongjuan Li, Ying Gao + Show 2 more

Open Access

https://doi.org/10.1155/2021/9948044

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Abstract

In this paper, an adaptive neural network control method is described to stabilize a continuous stirred tank reactor (CSTR) subject to unknown time-varying delays and full state constraints. The unknown time delay and state constraints problem of the concentration in the reactor seriously affect the input-output ratio and stability of the entire system. Therefore, the design difficulty of this control scheme is how to debar the effect of time delay in CSTR systems. To deal with time-varying delays, Lyapunov–Krasovskii functionals (LKFs) are utilized in the adaptive controller design. The convergence of the tracking error to a small compact set without violating the constraints can be identified by the time-varying logarithm barrier Lyapunov function (LBLF). Finally, the simulation results on CSTR are shown to reveal the validity of the developed control strategy.

Highlights

To eliminate the effect of nonlinearities and uncertainties appearing in nonlinear systems, the fuzzy logic systems (FLSs) and neural networks (NNs) [1, 2] are useful tools, which avoid the requirement that nonlinearities must be known or can be linearly parameterized. erefore, adaptive neural or fuzzy control methods are usually applied for the nonlinear single-input-single-output (SISO) systems in [3,4,5,6,7,8] and multi-input-multi-output (MIMO) systems in [9,10,11,12]
A robust adaptive control method for the nonlinear continuous stirred tank reactor (CSTR) system was proposed to ensure that the entire system remains stable in [20]
According to the above discussion, this study develops an adaptive NN control strategy for CSTRs with both timevarying delays and full time-varying state constraints

Summary

Introduction

To eliminate the effect of nonlinearities and uncertainties appearing in nonlinear systems, the fuzzy logic systems (FLSs) and neural networks (NNs) [1, 2] are useful tools, which avoid the requirement that nonlinearities must be known or can be linearly parameterized. erefore, adaptive neural or fuzzy control methods are usually applied for the nonlinear single-input-single-output (SISO) systems in [3,4,5,6,7,8] and multi-input-multi-output (MIMO) systems in [9,10,11,12]. Employing appropriate LKFs, the robust adaptive backstepping control strategies in [41,42,43] were reported for nonlinear strict-feedback systems to debar time delays. Afterwards, based on [41,42,43], some adaptive NN and fuzzy controllers in [44,45,46,47] were further constructed to debar the effect of unknown time-varying delays appearing in several classes of nonlinear systems. According to the above discussion, this study develops an adaptive NN control strategy for CSTRs with both timevarying delays and full time-varying state constraints. (1) Under the adaptive control framework, the problem of time-varying delays and time-varying full state constraints is considered for CSTRs simultaneously, which is more in line with the needs of engineering systems.

Preliminaries and Problem Formulation

The Controller Design and Stability Analysis

Simulation Example