Simple method of designing centralized PI controllers for multivariable systems based on SSGM

Ram, V. Dhanya; Chidambaram, M.

doi:10.1016/j.isatra.2014.11.019

Cited by 26 publications

(8 citation statements)

References 13 publications

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“…The performance metric ISE and ISV is given in Table 4 for the case of rejecting a sinusoidal change in output disturbance of frequency 0.1 rad/min in both of the output channels. The sinusoidal disturbance rejection property of SOF controller is comparable to that of the controller designed in [3] and [4] and superior to the one designed in [2]. From Table 4, clearly the control effort (ISV of U 1 ) is on the lower side for SOF controller when compared to other controllers.…”

Section: Numerical Simulation Results and Discussionmentioning

confidence: 76%

“…The performance of the controllers designed in [3] and [4] are similar, but consumes very large control input when compared to SOF controller. Clearly, the SOF controller outperforms the one designed in [2]. Unmeasured output sinusoidal disturbances of very small frequencies are also prominent in process control applications.…”

Section: Numerical Simulation Results and Discussionmentioning

confidence: 99%

“…The solid blue curve shows the response of the SOF controller given in equation 30. The response of the controllers presented in [2], [3] and [4] is given by black curve (dashed), brown curve (dotted) and green curve (dash-dot) respectively. After the introduction of output disturbance at a time instant of 50 minutes, the response of SOF controller is superior in terms of lowest undershoot and smallest settling time when compared to other controllers.…”

Section: Numerical Simulation Results and Discussionmentioning

confidence: 99%

“…Wood and Berry proposed a popular two-input two-output (TITO) transfer function model of a pilot scale binary distillation column [1]. A plethora of multivariable control techniques are available in the literature, applied to TITO model of binary distillation column proposed by Wood and Berry, [2]- [9]. A method based on the static decoupler design using the steady state gain matrix is proposed in [2].…”

Section: Introductionmentioning

confidence: 99%

“…A plethora of multivariable control techniques are available in the literature, applied to TITO model of binary distillation column proposed by Wood and Berry, [2]- [9]. A method based on the static decoupler design using the steady state gain matrix is proposed in [2]. The decentralized proportional-integral-derivative (PID) controller designed for the decoupled process is then combined with the static decoupler to yield a multivariable controller.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multivariable Static Output Feedback Control of a Binary Distillation Column using Linear Matrix Inequalities and Genetic Algorithm

Kalpana¹,

Kandath²,

Senthilkumar³

et al. 2017

Preprint

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The current work addresses the control of two-input two-output (TITO) Wood and Berry model of a binary distillation column. The controller design problem is formulated in terms of multivariable H∞ control synthesis. The controller structure takes the form of simplest static output feedback (SOF) control. The controller synthesis is performed using a hybrid approach of blending linear matrix inequalities (LMI) and genetic algorithm (GA). The performance of the static output feedback controller is compared with three other controllers designed for Wood and Berry model available in the literature. The first simulation study is performed for the case of tracking a unit step command in the presence of a step change in output disturbance. A second simulation study is performed for rejecting a change in sinusoidal output disturbance.

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Section: Numerical Simulation Results and Discussionmentioning

confidence: 76%

Section: Numerical Simulation Results and Discussionmentioning

confidence: 99%

Section: Numerical Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multivariable Static Output Feedback Control of a Binary Distillation Column using Linear Matrix Inequalities and Genetic Algorithm

Kalpana¹,

Kandath²,

Senthilkumar³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

Centralized Proportional Integral Controller Design for the Activated Sludge Process

Anchan

Rao

2022

Chem Eng & Technol

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The design of a centralized proportional integral controller for a single-tank activated sludge process is presented. The relative normalized gain array (RNGA) and dynamic relative gain array (dRGA) were adopted for enhancing the closed-loop performance of a multivariable system. The dRGA method gave superior and acceptable responses compared to the RNGA method. The designated error indices decreased significantly for the dRGA method compared with the RNGA method. The dRGA method is simple and effective, since it considers the process dynamics to provide an accurate interaction assessment. The designed centralized controller overcomes the limitations of the decoupler for stability challenges when compared with decentralized controllers.

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Model based multivariable control scheme in a reset configuration for stable multivariable systems

Pathiran¹,

Prakash

2017

Can J Chem Eng

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The multivariable control scheme is a widely used advanced process control methodology to control key process variables in chemical engineering processes. However, successful implementation of multivariable control requires a simplified control structure, a lower number of tuning parameters, and an appropriate tuning method. In this work, a model based multivariable control scheme is realized in reset configuration for the control of stable multi‐input and multi‐output systems. This control scheme utilizes the process transfer function matrix and the inverse of the steady state gain matrix for its implementation. The control scheme has a single tuning parameter and also inherently takes into account the interaction that exists between process inputs and outputs. This scheme is easy to implement and apply in multivariable systems with multiple delays, high‐dimensional systems, non‐square systems, and systems with RHP zeros. Simulation examples from process system engineering such as industrial scale polymerization reactor system, quadruple‐tank system, temperature control for the four room process, and shell control problem are presented to show the efficacy of the proposed multivariable control scheme.

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Simple method of designing centralized PI controllers for multivariable systems based on SSGM

Cited by 26 publications

References 13 publications

Multivariable Static Output Feedback Control of a Binary Distillation Column using Linear Matrix Inequalities and Genetic Algorithm

Multivariable Static Output Feedback Control of a Binary Distillation Column using Linear Matrix Inequalities and Genetic Algorithm

Centralized Proportional Integral Controller Design for the Activated Sludge Process

Model based multivariable control scheme in a reset configuration for stable multivariable systems

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