Interval Fuzzy Type-II Controller for the Level Control of a Three Tank System

Sahu, Hituraj; Ayyagari, Ramakalyan

doi:10.1016/j.ifacol.2016.03.114

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…MATLAB can be used to validate the efficiency of the intelligent FL controller of the second type, as was shown in [14], on the basis of level control in a hybrid 3-tank system.…”

Section: Matlab/simulink Environmentmentioning

confidence: 99%

“…A coupled or cascaded dynamical system consisting of many tanks, which is investigated in [8,15,68,[77][78][79][80][81][82] (state-space approach, PI, PID), [10,30,80] (transfer function approach), [14,16] (FL, NN), [9,10,15,78,83] (model-based or model-predictive control, state predictor), [8,16,79] (geometry-varying tank), [77,84] (SMC), and [80] (dead-time).…”

Section: Mathematical Modelingmentioning

confidence: 99%

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Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Olejnik

Awrejcewicz

2022

Machines

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In this paper, a unique overview of intelligent machines and mathematical methods designed and developed to measure and to control the water level in industrial or laboratory setups of coupled and cascaded configurations of tanks is made. A systematized and concise overview is made of the mechatronic systems used in the measurement, identification, and control of the water level enumerates, the software used in the associated scientific research, modern techniques and sensors, and mathematical models, as well as analysis and control strategies. The broad overview of applications of the last decade is finalized by a proposition of a control system that is based on a parameter estimation of a new experimental setup, an integral dynamic model of the system, a modern mechatronic machine such as the Watson-Marlow peristaltic pump, the Anderson Negele sensor of level, the NI cRIO-9074 controller, and LabVIEW virtual instrumentation. The results of real experimental tests, exploiting a hybrid proportional control, being improved by a numerically predicted water level, are obtained using a few tools, i.e., the static characteristics, the classical step response, and a new pyramid-shaped step function of a discontinuous path-following reference input, being introduced to evaluate the effectiveness and robustness of the regulation of the level height.

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“…MATLAB can be used to validate the efficiency of the intelligent FL controller of the second type, as was shown in [14], on the basis of level control in a hybrid 3-tank system.…”

Section: Matlab/simulink Environmentmentioning

confidence: 99%

Section: Mathematical Modelingmentioning

confidence: 99%

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Olejnik

Awrejcewicz

2022

Machines

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“…1 Many control methods have been proposed for the liquid level tracking control problem of the three-tank system. An interval type-II fuzzy logic systems (IT2FLS) is presented by Sahu and Ayyagari, 2 and it is compared with a linear quadratic regulator (LQR). The test results show that the response is oscillatory when the liquid level is controlled by the LQR controller.…”

Section: Introductionmentioning

confidence: 99%

Model-Predictive Control for the Three-Tank System Utilizing an Industrial Automation System

Kortela

2022

ACS Omega

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A three-tank process has difficulty in controller design because of nonlinear flow and interactions between tanks. This paper addresses the design methodology of the model-predictive controller (MPC) for the three-tank system. The control performance of the proposed MPC controller is compared with the proportional plus integral (PI) controller by both simulations and experiments on the real three-tank pilot with the industrial ABB 800xA automation system. The MPC controller shows a faster response for the two tanks: In the simulation, the settling times are about 120 s for both tanks of the MPC controller. On the other hand, the settling times for the PI controller are about 200 s for the first tank and 150 s for the second tank. The experiments confirm these results.

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“…21,22 These issues can be solved with the PIDA controller to improve the parameters of the controller for enhanced outcome. 23 The classical tuning algorithms are utilized in the PIDA controllers design, such as the F-MIGO optimization, [24][25][26] Ziegler-Nichols rules, linear programming formulation, 27,28 Hermite-Biehler and Pontryagin theorems. 29 Some of the recent literatures related to the higher-order non-linear time delay system utilizing various optimization approaches, which are reviewed as follows, Sumathi et al 30 have presented the state feedback gain based on the time delay system of the optimal partial-order proportionate integration-derived (PID) controller model.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the optimization issues are becoming more difficult and the existing optimization algorithms cannot achieve a reasonable solution 21,22 . These issues can be solved with the PIDA controller to improve the parameters of the controller for enhanced outcome 23 . The classical tuning algorithms are utilized in the PIDA controllers design, such as the F‐MIGO optimization, 24–26 Ziegler‐Nichols rules, linear programming formulation, 27,28 Hermite‐Biehler and Pontryagin theorems 29 .…”

Section: Introductionmentioning

confidence: 99%

Optimal design of proportional integral derivative acceleration controller for higher‐order nonlinear time delay system using m‐MBOA technique

Arulvadivu

Manoharan²,

Singh³

et al. 2022

Int J Numerical Modelling

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In general, the parameter adjustment is used to design the proportional‐integral‐derivative (PID) controllers. Despite that, to enhance the capacity of control performance by adjusting parameters is limited. Therefore, in this paper, an optimal design of proportional integral derivative acceleration (PIDA) controller is proposed for higher‐order nonlinear time delay system (TDS) using modified butterfly optimization algorithm (MBOA) with the aim of achieving optimal closed‐loop response, which is enhanced with mutualism system (m‐MBOA). In the proposed approach, the mutualism stage of symbiotic organisms search (SOS) and butterfly optimization algorithm (BOA) global exploration phase is considered. The major aim of the proposed approach is to “tune the gain of the PIDA controller to get the desired response.” It reduces the drawback of the PIDA controller in the higher‐order TDS using advanced control limits is the individuality of the proposed approach. Here, the proposed approach is employed to reduce the higher‐order delays together with reliability limits, like minor overshoots, time resolution, and fixed stage deficiency. To evaluate the proposed PIDA controller, the execution is done by the MATLAB/Simulink platform, also its efficiency is compared with existing approaches. Moreover, the proposed and existing approaches are analyzed with mean, median, and standard deviations. From the analysis, the proposed approach achieves the stability with minimal delay time and reduces the computational complexity. Finally, the simulation results demonstrate that the proposed approach can efficiently with accurately find the optimal global solutions.

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Interval Fuzzy Type-II Controller for the Level Control of a Three Tank System

Cited by 8 publications

References 16 publications

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Intelligent Mechatronics in the Measurement, Identification, and Control of Water Level Systems: A Review and Experiment

Model-Predictive Control for the Three-Tank System Utilizing an Industrial Automation System

Optimal design of proportional integral derivative acceleration controller for higher‐order nonlinear time delay system using m‐MBOA technique

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