Disturbance Observer and L2-Gain-Based State Error Feedback Linearization Control for the Quadruple-Tank Liquid-Level System

Abstract

This paper proposes a fresh state error feedback linearization control method with disturbance observer (DOB) and L2 gain for a quadruple-tank liquid-level system. Firstly, in terms of the highly nonlinear and strong coupling characteristics of the quadruple-tank system, a state error feedback linearization technique is employed to design the controller to achieve liquid-level position control and tracking control. Secondly, DOB is purposed to estimate uncertain exogenous disturbances and applied to compensation control. Moreover, an L2-gain disturbance attenuation technology is designed to resolve one class of disturbance problem by uncertain parameter perturbation existing in the quadruple-tank liquid-level system. Finally, compared with the classical proportion integration differentiation (PID) and sliding mode control (SMC) methods, the extensive experimental results validate that the proposed strategy has good position control, tracking control, and disturbance rejection performances.

Advanced Control for Quadruple Tank Process

“…The QTP can be considered as a prototype of many industrial applications in the process industry involving liquid level control such as chemical and petrochemical plants [12]. Previous research has explored various control strategies for the QTP, including decentralized PI/PID controllers [9], [10], [13]- [21], fractional order PI control [22]- [25], Model Reference Adaptive Controller (MRAC) [13], [26], state error feedback linearization control method with disturbance observer (DOB) and gain [27]- [30], low-gain integral controllers [31], generalized predictive control (GPC) [32], and sliding mode control [12], [23], [33]- [37], optimal control [38]- [40], and intelligent control techniques [21], [39], [41]- [49].…”

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

“…This issue is relevant in various industries, including water treatment, chemical, and biochemical plants, food processing, metallurgy, filtration devices, etc. [2,20]. The QTS is a nonlinear system that illustrates several phenomena of multivariable systems, such as the interaction and effects of multivariable zeros [21].…”

“…Other authors have applied internal model control [22], multivariable H ∝ control [3], quantitative feedback control [23], LQG optimal control [24], predictive control [25,26], and distributed model predictive control [27]. More recent works have applied nonlinear techniques to the QTS such as sliding mode control [28,29], feedback linearization [20], fuzzy control [30,31], and neural networks [32], among others.…”

State Estimation and Optimal Control of Four-Tank System with Stochastic Approximation Approach

“…Then, compare their performances in terms of disturbance rejection to study the effects of these control systems on the four-tank system. Moreover, a distributed control and estimation method [13] was designed for a multivariate four-tank process, whereas active disturbance rejection techniques [14] and disturbance observers [15] were proposed for studying four-tank systems in the presence of random disturbances. Furthermore, some studies have been conducted on tank systems using the simultaneous perturbation stochastic approximation (SPSA) method.…”