Control of a nonlinear continuous stirred tank reactor via event triggered sliding modes

Sinha, Anupama; Mishra, Rajiv Kumar

doi:10.1016/j.ces.2018.04.057

Cited by 60 publications

(19 citation statements)

References 30 publications

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“…The continuous stirred tank reactor is the most important piece of equipment in many chemical and biochemical industries and has second-order nonlinear dynamic characteristics [38]. Therefore, it can be used to test the ability of the SSMI method to solve nonlinear and time-varying problems.…”

Section: Cstr Simulation Experiments and Results Analysismentioning

confidence: 99%

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Dai

2019

Symmetry

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To solve the soft sensor modeling (SSMI) problem in a nonlinear chemical process with dynamic time variation and multi-rate data, this paper proposes a dynamic SSMI method based on an autoregressive moving average (ARMA) model of weighted process data with discount (DSSMI-AMWPDD) and optimization methods. For the sustained influence of auxiliary variable data on the dominant variables, the ARMA model structure is adopted. To reduce the complexity of the model, the dynamic weighting model is combined with the ARMA model. To address the weights of auxiliary variable data with different sampling frequencies, a calculation method for AMWPDD is proposed using assumptions that are suitable for most sequential chemical processes. The proposed method can obtain a discount factor value (DFV) of auxiliary variable data, realizing the dynamic fusion of chemical process data. Particle swarm optimization (PSO) is employed to optimize the soft sensor model parameters. To address the poor convergence problem of PSO, ω-dynamic PSO (ωDPSO) is used to improve the PSO convergence via the dynamic fluctuation of the inertia weight. A continuous stirred tank reactor (CSTR) simulation experiment was performed. The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process. The AMWPDD proposed in this paper can reflect the dynamic change of chemical process and improve the accuracy of SSM data prediction. The ω dynamic PSO method proposed in this paper has faster convergence speed and higher convergence accuracy, thus, these models correlate with the concept of symmetry.

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Section: Cstr Simulation Experiments and Results Analysismentioning

confidence: 99%

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Dai

2019

Symmetry

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“…CSTRs have nonlinear dynamic characteristics [66] and are widely employed to test the capabilities of soft sensor modeling methods for solving nonlinear and time-varying problems.…”

Section: Cstr Simulation and Discussionmentioning

confidence: 99%

An adaptive soft sensor deterioration evaluation and model updating method for time-varying chemical processes

Dai

2020

CI&CEQ

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Article Highlights • An adaptive soft sensor model deterioration evaluation method is proposed • An adaptive modeling sample data updating method is proposed • The RMSE and MAE of the fitting performance of the proposed method in a CSTR are improved • The RMSE and MAE of the predicted performances of the proposed method in a CSTR have lower values • The RMSE and MAE of the predicted performances of the proposed method in a DCP are the lowest average values

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“…SMC is widely avowed for its robustness and disturbance rejection potencies . The discontinuous switching of control involved nullifies matched uncertainties and bounded disturbances.…”

Section: Synthesis Of the Proposed Controllermentioning

confidence: 99%

Robust congestion control in cognitive radio network using dynamic event‐triggered sliding mode

Majumder

Mishra

Sinha

et al. 2019

Int J Communication

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The ever-increasing service demand from wireless users under restricted bandwidth necessitates the development of suitable congestion control scheme for throughput maximization. A robust congestion controller design has been addressed in this work to sustain necessary Quality of Service in Cognitive Radio Network. The goal is to reduce packet loss while ensuring optimum utilization of available resources. Sliding mode controller proclaimed for its disturbance rejection capabilities and robustness has been used to design the proposed controller. Using the benefits of sliding mode control, dynamic event-triggering strategies have been combined for better utilization in resource-constrained environments. Control execution requires minimal updates while guaranteeing the desired closed-loop system behavior. The controller efficacy has been validated in simulations.

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Control of a nonlinear continuous stirred tank reactor via event triggered sliding modes

Cited by 60 publications

References 30 publications

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

An adaptive soft sensor deterioration evaluation and model updating method for time-varying chemical processes

Robust congestion control in cognitive radio network using dynamic event‐triggered sliding mode

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