Control System Design for a Neutralization Process using Block Oriented Models

Nagammai, S.; Sivakumaran, N.; Radhakrishnan, T. K.

doi:10.1080/10739140600963905

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The trajectory tracking and regulatory response obtained thus are appended (Figs. [6][7][8][9][10][11]. The RNN based adaptive controller shows faster settling time and less over shoot compared to IMC based PI controller for both trajectory tracking and disturbance rejection.…”

Section: Resultsmentioning

confidence: 99%

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Adaptive Control of Neutralization Process Using Recurrent Neural Networks

Balasubramanian

Hariprasad

Sivakumaran

et al. 2009

Instrumentation Science & Technology

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This paper presents Recurrent neural Network (RNN) based adaptive control scheme for a pH neutralization process which is difficult to control due to its nonlinear dynamics with uncertainties. The proposed design comprises of both RNN estimator which adapts online and a RNN controller. Desired performance of the system is ensured by the parallel operation of both. The estimator weights are updated recursively by back propagation algorithm and controller weights are modified by steepest descent approach. Stability and convergence of proposed controller is guaranteed by Lyapunov stability analysis. Servo and regulatory performance of the system thus obtained by simulation is compared with a model based IMC controller. The RNN based controller is exhibits better performance as shown by the control simulation of a nonlinear pH neutralization process.

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Section: Resultsmentioning

confidence: 99%

“…[11] To account for varying process gains gain scheduling approach can be adopted. Steady state titration curve can be divided into various zones where the system is assumed to follow linear behavior, and corresponding transfer functions can be found out.…”

Section: Imc Based Pi Controllermentioning

confidence: 99%

Adaptive Control of Neutralization Process Using Recurrent Neural Networks

Balasubramanian

Hariprasad

Sivakumaran

et al. 2009

Instrumentation Science & Technology

Self Cite

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“…Figure schematically illustrates the structures of these block-oriented models, in which u and y are process input and output, respectively, whereas v and w are inaccessible intermediate variables. These model structures have been successfully used to describe nonlinear systems in a number of practical applications in the areas of chemical processes − and biological processes. , …”

Section: Introductionmentioning

confidence: 99%

Data-Driven Nonlinear Control Design Using Virtual-Reference Feedback Tuning Based on the Block-Oriented Modeling of Nonlinear Systems

Jeng

Lin

2018

Ind. Eng. Chem. Res.

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Process nonlinearities impose difficulties for model identification and control-system design. This paper presents a novel data-driven method for nonlinear control design based on the virtual-reference feedback tuning (VRFT) framework and block-oriented modeling of nonlinear systems. Control-design algorithms for Hammerstein, Wiener, and Hammerstein–Wiener systems were systematically developed. The proposed method can be applied to design a nonlinear controller for an unknown plant directly using one-shot input–output data generated by the plant. In the method, identifying a complete dynamic model of the nonlinear system is not necessary; and only the static non-linearity (or its inverse), represented by the B-spline series, requires estimation. Moreover, in the method, the non-linearity estimation and control design processes are performed simultaneously without the need for nonlinear optimization or iterative procedures. The effectiveness of the proposed control design method is demonstrated herein through several simulation examples, including two benchmark processes (namely, a distillation column and a pH-neutralization process).

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“…Due to these reasons, pH control is viewed as an important benchmark for control of highly non linear processes. At an earlier stage, pH Control was done using linear conventional controllers (Nagammai et al, 2006) by employing cascade and feed forward controllers as proposed by Mcmillan. They suffer from the problems of robustness and load disturbances.…”

Section: Introductionmentioning

confidence: 99%

Tuning of a Proportional-Integral-Derivative Controller using Multi-Objective Non Dominated Sorting Particle Swarm Optimization Applied to pH Control in Continuous Stirred Tank Reactor

Kumar¹

2011

American Journal of Applied Sciences

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Problem statement: Most of the control engineering problems are characterized by several, contradicting, conflicting objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion and using Pareto-optimal solutions. Approach: Non-Dominated Sorting Particle Swarm Optimization algorithm (NSPSO) based approach is used in the design of multiobjective PID controller to find the constant proportional-integral-derivative gains for a chemical neutralization plant. The plant considered in this study is highly non-linear and with varying time delay, provides a challenging test bed for nonlinear control problems. Results: Experimental results confirm that a multi-objective, Paretobased GA search gives a better performance than a single objective GA. Conclusion: Finally, the results for single objective and multiobjective optimization using NSPSO for the neutralization plant are compared. Gain scheduled PID controllers are designed from Pareto front obtained with NSPSO which exhibit good disturbance rejection capability.

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Control System Design for a Neutralization Process using Block Oriented Models

Cited by 5 publications

References 15 publications

Adaptive Control of Neutralization Process Using Recurrent Neural Networks

Adaptive Control of Neutralization Process Using Recurrent Neural Networks

Data-Driven Nonlinear Control Design Using Virtual-Reference Feedback Tuning Based on the Block-Oriented Modeling of Nonlinear Systems

Tuning of a Proportional-Integral-Derivative Controller using Multi-Objective Non Dominated Sorting Particle Swarm Optimization Applied to pH Control in Continuous Stirred Tank Reactor

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