Implementation and test of a new autotuning method for PID controllers of TITO processes

Pereira, René D. O.; Veronesi, Massimiliano; Visioli, Antonio; Normey‐Rico, Julio E.; Torrico, Bismark C.

doi:10.1016/j.conengprac.2016.10.010

Cited by 23 publications

(11 citation statements)

References 27 publications

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“…In order to compare the control efficiency of this plant, six classical controllers have been taken from literature [1], [2], [4], [35]- [37]. In [1], [35]- [37], a distributed control scheme, as shown in Fig. 1, has been used while in works [2] and [4] a dynamic decoupling controller have been adopted.…”

Section: Examplementioning

confidence: 99%

“…To check the decoupling abilities of the MPC controller, all simulations have been carried out by both adjusting the weighting factors and output constraints. Value of 1  is equal to 1 for the first 80 seconds and 10 afterwards. Controllers with changed weighting factors are marked by "mpcw', output constraints by "mpcc', and those without any change of weighting factors and output constraints by "mpc".…”

Section: Examplementioning

confidence: 99%

“…Most often, it boils down to minimization of the coupling effects instead of full decoupling. As control of MIMO dynamic systems enjoys a continuous attention [1], [2], a challenging task becomes to include the dynamic decoupling problem [3]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…In [1], an automatic two-step procedure for tuning of Proportional-Integral-Derivative (PID) controller for a Two Input Two Output (TITO) process is presented. References [6], [7] gives necessary and sufficient conditions for the existence of diagonal, block-diagonal, and triangular decoupling controllers for non-square plants and systems with non-unity feedback, and with one or two degree-offreedom controller configuration.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effective Use of MPC for Dynamic Decoupling of MIMO Systems

Dworak¹,

Goyal²,

Aggarwal³

et al. 2019

ElAEE

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The article analyses possible ways of using predictive controllers to perform control tasks and dynamic decoupling for dynamic systems with Multiple Inputs and Multiple Outputs (MIMO). The results of experiments on the selected reference plant are presented, showing the effectiveness of individual decoupling methods. These are also compared to those obtained in typical control systems with Proportional-Integral-Derivative (PID) controllers. Recommendations are made on how to tune model predictive controllers (MPC) for their effective use for MIMO plants.

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective Use of MPC for Dynamic Decoupling of MIMO Systems

Dworak¹,

Goyal²,

Aggarwal³

et al. 2019

ElAEE

View full text Add to dashboard Cite

show abstract

“…For instance, the applied source separation method (Broman et al, 1999), curve fitting and genetic algorithm-based method (Viswanathan et al, 2001), a closed-loop reaction curve method (Ram and Chidambaram, 2014) and nonlinear least square minimization method (Ram and Chidambaram, 2016) have been well explained in the literature. Various techniques using step response data were developed for parameter estimations and include a sequence of step signals (Li et al, 2005a, 2005b; Pereira et al, 2017). A step response test with particle swarm optimization and search algorithm for the multivariable process was presented by Jin et al (2012).…”

Section: Introductionmentioning

confidence: 99%

A single-step identification strategy for the coupled TITO process using fractional calculus

Kothari

Mehta

Prasad

2021

Transactions of the Institute of Measurement and Control

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The reliable performance of a complete control system depends on accurate model information being used to represent each subsystem. The identification and modelling of multivariable systems are complex and challenging due to cross-coupling. Such a system may require multiple steps and decentralized testing to obtain full system models effectively. In this paper, a direct identification strategy is proposed for the coupled two-input two-output (TITO) system with measurable input–output signals. A well-known closed-loop relay test is utilized to generate a set of inputs–outputs data from a single run. Based on the collected data, four individual fractional-order transfer functions, two for main paths and two for cross-paths, are estimated from single-run test signals. The orthogonal series-based algebraic approach is adopted, namely the Haar wavelet operational matrix, to handle the fractional derivatives of the signal in a simple manner. A single-step strategy yields faster identification with accurate estimation. The simulation and experimental studies depict the efficiency and applicability of the proposed identification technique. The demonstrated results on the twin rotor multiple-input multiple-output (MIMO) system (TRMS) clearly reveal that the presented idea works well with the highly coupled system even in the presence of measurement noise.

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Real‐time data‐driven PID controller for multivariable process employing deep neural network

Jeyaraj¹,

Nadar²

2022

Asian Journal of Control

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The complex industrial processes exhibiting nonstationary and multivariable with time‐varying dynamics result in low accuracy. Also, stability compensation is difficult to be obtained by a conventional PID controller. Hence, a deep learning‐based data‐driven PID controller is designed for unmodeled dynamics compensation for complex industrial processes. In this research work, a nonlinear PID controller is designed with a deep neural network (DNN) model from unmodeled dynamics of the complex industrial processes. To validate the performance, results from stability compensation and convergence of the model parameters for closed‐loop systems were obtained. When tested on a real‐time twin tank system, it achieved an accurate output flowrate with 97.65% accuracy and 1.89% peak overshoot compared with conventional PID controller. Both simulated and experimental results validate that proposed controller has improved stability and uniform convergence of system variables. The proposed deep learning‐based PID controller was employed on a twin tank control system. This confirms the feasibility and practical application of a real‐time complex process.

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Implementation and test of a new autotuning method for PID controllers of TITO processes

Cited by 23 publications

References 27 publications

Effective Use of MPC for Dynamic Decoupling of MIMO Systems

Effective Use of MPC for Dynamic Decoupling of MIMO Systems

A single-step identification strategy for the coupled TITO process using fractional calculus

Real‐time data‐driven PID controller for multivariable process employing deep neural network

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