The Modified Quadruple-Tanks Process: A Flexible Mathematical Model with an Adjustable Simulink Block

Numsomran, A.; Chaoraingern, Jutarut; Trisuwannawat, Thanit

doi:10.4028/www.scientific.net/amm.541-542.813

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…Assume 0, and denote the positive eigenvalue of (20) with >0 and the negative with <0. The free response of (20) can be written as …”

Section: Problem A: Ideal Control Law and Zero Dynamicsmentioning

confidence: 99%

“…The subject is not detailed here for brevity's sake. The feedback gains in (19) and (34) are designed by fixing the eigenvalues of the closed-loop state matrices in (20) and (35). The corresponding frequency BW will be wider that the state predictor one , not to disturb state predictor design.…”

Section: Control Algorithms and Designmentioning

confidence: 99%

“…To deal with the multivariable dead times, decentralized integral controllability and time-domain bounds on closed loop performance were derived and discussed, and a laboratory process was described and tested in [19]. Other studies refer to simulation [20] and fault-diagnosis [21]. Most of the approaches focused on continuous-time control design.…”

Section: Introductionmentioning

confidence: 99%

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The four-tank control problem: Comparison of two disturbance rejection control solutions

2017

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The paper aims to compare and prove a pair of disturbance/uncertainty rejection control laws for the well-known four tank control problems. Control requirements are expressed in terms of a set point sequence as it usual in the literature. Uncertainty class is defined as the union of four sub-classes: unknown disturbance, parametric uncertainty, measurement errors and neglected dynamics. Modelling and design allow insight of the dynamic properties of the problem. They are formulated by a pair of theorems which fix the range of application. Theorem are confirmed by the results simulated runs, and indicate the correct way to further broaden control design applicability. Disturbance rejection (better uncertainty) design is deployed using the Embedded Model Control methodology: only unknown disturbance and parametric uncertainty can be rejected, whereas neglected dynamics effects must be filtered. As a result, simple performance and stability inequality can be formulated in the frequency domain and lead to closed-loop pole placement. Inequalities are such to reveal whether pole placement is feasible and how feasibility can be recovered, an issue which at authors knowledge is rarely encountered in the literature. Simulated runs prove the design procedure.

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“…Assume 0, and denote the positive eigenvalue of (20) with >0 and the negative with <0. The free response of (20) can be written as …”

Section: Problem A: Ideal Control Law and Zero Dynamicsmentioning

confidence: 99%

Section: Control Algorithms and Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The four-tank control problem: Comparison of two disturbance rejection control solutions

2017

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“…A fuzzy PID controller based on ARM7TDMI was implemented in the couple-tank process for illustrating the control performance of a non-linear fuzzy inference system comparing with conventional PID control [15]. Specifically, the several mathematical models of modified quadrupletank process were explained clearly as well as providing the evaluation of two degrees of freedom PID control algorithm on multi-input-multi-output system [16]. Therefore, it is recognizable that numerous advantage of knowledge can be illustrated and investigated by multi-tank system.…”

Section: Introductionmentioning

confidence: 99%

“…Besides providing theoretical control system analysis and design, we develop the multi-configuration tank process software toolbox for providing the non-linear functions of dynamic models of multi-configuration tank process which is the advantage tool for investigating the performances of the controllers. The essential features advanced the previous studies [16] are that the software toolbox is developed from discrete state P-file S-function which offers more flexible implementation of simulation and improvement simulation times. Likewise, it is able to provide a complete non-linear function set of the dynamic models of multiconfiguration tank process.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fractional Order PIλDμ Control for Multi-Configuration Tank Process (MCTP) Toolbox

Numsomran¹,

Trisuwannawat²,

Tipsuwanporn³

2018

IJIES

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This paper presents an adaptive fractional order PI λ D μ control for multi-configuration tank process (MCTP) toolbox which aims at demonstrating the problem of reference tracking and cross coupling rejection in multi-input-multi-output system. Moreover, we investigate the cases where the system is in the mode of minimum phase and non-minimum phase configuration. Besides providing theoretical control system analysis and design, we develop the multi-configuration tank process software toolbox for providing the non-linear functions of dynamic models of multi-configuration tank process which is the advantage tool for investigating the performances of the controllers. The software toolbox is developed from discrete state P-file S-function which is operated within the MATLAB environment for providing many non-linear functions of dynamic models of multi-configuration tank process such as multi-input multi-output quadruple tank full-interacting process, multi-input multi-output quadruple tank process, multi-input single-output triple tank interacting process, multi-input multi-output coupled tank interacting process. All actual process attributes are encapsulated in S-function as the input parameters, thus the multi-tank function block can be simply adjusted by specifying the physical properties of the tank system. The study explains about the mathematical model of multi-configuration tank process, nonlinear dynamic characteristic, minimum phase and non-minimum phase configuration, software toolbox features and also describes the design of the adaptive fractional order PI λ D μ controller including the performance validation. The results have been illustrated that the proposed controller design scheme can provide the sufficient effectiveness in the performance, stability and robustness. Furthermore, these tests reinforce the usefulness of MCTP toolbox as a complete simulation tool for users to perform an engineering research of multi-configuration tank control system analysis and design, moreover, it contains very useful for validating the control algorithm of multi-input-multi-output system.

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The four-tank benchmark: A simple solution by Embedded Model Control

Huang

Canuto

Novara

2016

2016 American Control Conference (ACC)

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The four-tank benchmark is a multivariate and nonlinear control problem which has been widely studied in the literature. Two pairs of tanks in series are supplied by two pumps. Under certain configurations, the Embedded Model Control approach provides a simple decoupled solution by separately controlling the two output tank levels and treating the input flow as a partly unknown disturbance. Neglected dynamics in a form of unknown delays both in sensors and actuator dynamics is considered. The core of the control unit is a discrete-time embedded model consisting of unknown disturbance dynamics and partly known nonlinear interactions. The embedded model is driven by the plant command and by a feedback vector which is retrieved from the model error. The feedback is capable of keeping updated the unknown disturbance prediction, ready to be cancelled by the control law. The control gains are tuned using two sets of closed-loop eigenvalues in order to trade-off between disturbance rejection and robust stability. Simulated runs under different tank interactions prove design effectiveness.

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The Modified Quadruple-Tanks Process: A Flexible Mathematical Model with an Adjustable Simulink Block

Cited by 4 publications

References 8 publications

The four-tank control problem: Comparison of two disturbance rejection control solutions

The four-tank control problem: Comparison of two disturbance rejection control solutions

Adaptive Fractional Order PIλDμ Control for Multi-Configuration Tank Process (MCTP) Toolbox

The four-tank benchmark: A simple solution by Embedded Model Control

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