Sliding Mode Control for Coupled-Tank Liquid Level Control System

Abbas, Hur; Asghar, Sajjad; Qamar, Shahid

doi:10.1109/fit.2012.65

Cited by 34 publications

(15 citation statements)

References 9 publications

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“…All these parameters of the coupled-tank plant are given in Table I, where the nominal ones are taken from [38]. The exact discrete-time model of a nonlinear system is not always computable since we need to solve nonlinear initial value problems, as is the case in this example.…”

Section: B Examplementioning

confidence: 99%

Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping

Liu

Wang

Hou

2014

IEEE Trans. Neural Netw. Learning Syst.

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In this paper, we propose a sliding-mode-based stochastic distribution control algorithm for nonlinear systems, where the sliding-mode controller is designed to stabilize the stochastic system and stochastic distribution control tries to shape the sliding surface as close as possible to the desired probability density function. Kullback-Leibler divergence is introduced to the stochastic distribution control, and the parameter of the stochastic distribution controller is updated at each sample interval rather than using a batch mode. It is shown that the estimated weight vector will converge to its ideal value and the system will be asymptotically stable under the rank-condition, which is much weaker than the persistent excitation condition. The effectiveness of the proposed algorithm is illustrated by simulation.

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Section: B Examplementioning

confidence: 99%

Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping

Liu

Wang

Hou

2014

IEEE Trans. Neural Netw. Learning Syst.

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Section: Introductionmentioning

confidence: 99%

“…Controlling of a coupled tank system is considered to be an important issue in process industries such as pharmaceuticals, automatic liquid dispensing, food processing, and other chemical-related industries. [1][2][3][4] In these areas, the most widely used technology for level control is PID (proportional, integral, and derivative) controllers since PID controllers provide easy implementation and simplicity in level control, and they are successful in linear systems. Particularly for level control within coupled tank systems, the PID family of controllers are used in different configurations: Ziegler-Nichols proportional-integral (PI)-based tuning method for level control in coupled tank systems 5 ; control of multiple-input/multiple-output (MIMO)-structured coupled twin tanks with a fractional order PI controller 6 ; control of a two-input-two-output-structured coupled tank system with a hybrid PI neural network controller 7 ; PID controllers 8 ; control of single-input-single-output (SISO)-structured coupled tank systems with the fractional PID controller 9 ; fractional-order PID controllers for level control in a multiple-tank system 10 ; PID method using intelligent approaches.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode PI control with backstepping approach for MIMO nonlinear cross‐coupled tank systems

Aksu

Çoban

2019

Intl J Robust & Nonlinear

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Summary The control of tank systems in industrial applications is an important issue for monitoring the chemical processes involved in the manufacture and delivery of product. The most important reason to control the tank systems is to keep the liquid level in the tanks constant and at the desired level for a specified period of time. In this study, the sliding mode control (SMC) with a repetitive approach called backstepping that is insensitive to uncertainties in system parameters and input disturbances is proposed and experimentally applied to a quadruple, cross‐coupled, uncertain, nonlinear, and multiple‐input/multiple‐output tank system. A proportional‐integral (PI) control is used to reduce the steady‐state error caused by the parameter variations and external noises. The traditional way of introducing PI usually leads to sliding surfaces. In this paper, the PI action is introduced to the control signal. The proposed backstepping sliding mode PI control (BSMPIC) is applied to such a complex tank system for the first time. The experimental results are compared with those of the SMC, sliding mode PI control, and backstepping sliding mode control to see the effect of the proposed BSMPIC on the system. As a result of the comparison, it is observed that less overshoot and tracking error, better tracking performance, and faster rise time in the transient regime is obtained by the BSMPIC.

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“…Literature and indus-19 try distinguish these control approaches as: fixed-gain controllers, 20 predictive controllers, robust controllers, and intelligent controllers 21 (Abbas et (Abbas et al, 2012;Liang, 2011). In any case, the PID controller is 27 the most used strategy in coupled-tanks systems (Holic & Vesely, 28 2011).…”

mentioning

confidence: 98%

“…Table 4 summarizes relevant results 483 of the performance using these controllers. 484 In addition, the integral time-weighted absolute error (ITAE) 485 (Abbas et al, 2012;Veerachary, 2014) was computed for each con-486 troller that measures the integral of error between the reference and 487 the actual values weighted by time. Roughly speaking, it measures 488 the speed and the error in steady-state of a controller, in which the 489 smaller the ITAE the better of performance.…”

mentioning

confidence: 99%

A novel robust liquid level controller for coupled-tanks systems using artificial hydrocarbon networks

Ponce

Bastida

et al. 2015

Expert Systems with Applications

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Sliding Mode Control for Coupled-Tank Liquid Level Control System

Cited by 34 publications

References 9 publications

Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping

Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping

Sliding mode PI control with backstepping approach for MIMO nonlinear cross‐coupled tank systems

A novel robust liquid level controller for coupled-tanks systems using artificial hydrocarbon networks

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