A Neural Network-Based Adaptive Backstepping Control Law With Covariance Resetting for Asymptotic Output Tracking of a CSTR Plant

Abstract

A robust nonlinear adaptive controller merging a backstepping approach with neural networks is proposed for a nonlinear non-affine model. The work presented here is evaluated on a complex uncertain model of a continuous stirred tank reactor plant including an unknown varying parameter that enters the complexity model. By exploiting NN and adaptive backstepping approximation methods, an equivalent adaptive NN controller is constructed to achieve robust asymptotic output tracking control. The robustness to uncertainties as well as the lack of informative process data is the main enhancement of this work. This is attained through the implementation of the covariance resetting algorithm in the least square estimation of the NN weight tuning algorithm. The proposed novel control algorithm has been analyzed using Lyapunov analysis. In addition to excellent output trajectory tracking performance, the proposed approach has a profound benefit in terms of substantially lower control effort in comparison to the established work in the literature. In terms of applications in the petrochemical industry, lower control effort can translate to a more energy-efficient actuator, leading to lower costs over a long-run operation. The proposed method's feasibility for chemical process control was shown via numerical simulation.

Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method

“…In Table 4, this evaluation is specified. The aim is to track the temperature trajectory of the CSTR in the occurrence of hard constrained setpoint variations as presented in [29,30]. The works were performed by designing those regulators and comparing them with results presented in [29].…”

“…The aim is to track the temperature trajectory of the CSTR in the occurrence of hard constrained setpoint variations as presented in [29,30]. The works were performed by designing those regulators and comparing them with results presented in [29]. The outcomes of the synthesized controllers have been analyzed based on (ISE), (IAE) and control effort criteria.…”

“…Control techniques using feedback linearization established for linear control have been adapted to control nonlinear systems [24][25][26][27][28][29][30].…”

Optimization and Realization of the Continuous Reactor with Improved Automatic Disturbance Rejection Control

“…In another study [12], PID adaptive control was used to track temperature quickly, which has superior control performance; moreover, the application effect of adaptive control method on piezoelectric actuators is useful [13]. In [14], an equivalent adaptive neural network controller was constructed for the CSTR nonlinear model to achieve robust progressive output tracking control and it significantly reduces the control workload. e neural network effectively detects the electrical fault of an asynchronous motor [15].…”

Concentration estimation and fault tolerant control in a CSTR modelled as a quasi linear parameter varying system

“…Usually the mathematical model of a CSTR is represented by a set of ordinary differential equations (Zheng et al, 2020;Alshammari et al, 2020;Boudjella and Illoul, 2019) or linear approximations (Hernández-Osorio et al, 2020;Yazdi and Khayatian, 2020;Simkoff and Baldea, 2019). However an alternative to represent its nonlinear dynamic is through a collection of linear subsystems.…”