Time Optimal and Ziegler-Nichols Control. Experimental and Theoretical Results

McAvoy, Thomas J.

doi:10.1021/i260041a014

Cited by 47 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Latour, et al (1965Latour, et al ( , 1967Latour, et al ( , 1968, applied time optimal control theory to processes whose dynamic behavior can be represented by a second-order plus dead time transfer function. Recent experimental studies have been made by Mellichamp (1970), Nyquist andRamirez (1971), andMcAvoy (1972).…”

Section: Control Systemsmentioning

confidence: 99%

On-Line Control of a Nonlinear Multivariable Process

Hutchinson¹,

McAvoy²

1973

Ind. Eng. Chem. Proc. Des. Dev.

View full text Add to dashboard Cite

Section: Control Systemsmentioning

confidence: 99%

On-Line Control of a Nonlinear Multivariable Process

Hutchinson¹,

McAvoy²

1973

Ind. Eng. Chem. Proc. Des. Dev.

View full text Add to dashboard Cite

“…In light of these, studies [8] argued that adaptive control based techniques may be used for greater control results. Early works on pH control featuring adaptive control techniques used dynamic process models, which were developed based on first-principle approaches such as laws of conservation, physical and chemical laws, reaction invariants, and strong acid equivalent [9][10][11][12][13][14]. Many other adaptive controllers with nonlinear features were later developed following the works done by the previous authors [2].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Novel Self-adaptive Fuzzy Logic Controller for a pH Neutralization Process

Nsengiyumva¹,

Chen²

2018

Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)

View full text Add to dashboard Cite

This paper presents a self-adaptive fuzzy logic controller for a pH neutralization process which has been designed and implemented in real time process. The controller deals with the minimization of both the steady state error and time taken to reach steady state under varying operating conditions. The interesting fact about this control technique is that it operates a full-scale pH neutralization plant using the same equipment like that in real time full-scale industrial pH neutralization plant. The proposed self-adaptive technique updates the control parameters and the normalized universe of discourse of output fuzzy membership functions using varying scaling factors based on error and change of error values. A combination of both the coarse control and fine control was used for the overall control action. The speed of the response of the self-adaptive controller is handled by the coarse control technique while the amount of deviation of the measured values with respect to the setpoint under steady state is handled by the fine control technique. The performance of the envisaged selfadaptive scheme is tested for pH control in real time, and results are encouraging. MATLAB TM SIMULINK TM software is used for control, display as well as online communication with the DCS system through an OPC server configured on a PC. The controller is easily implementable and user-friendly and can, therefore, be adopted by process engineers. Lastly, the control technique envisaged herein can be implemented on preexisting control loops, hence alleviating financial constraints and great savings benefits of the controller.

show abstract

“…The control of pH processes is beyond the conventional proportional-integral-derivative (PID) feedback mechanism. In the literature, numerous methods have been proposed to improve the control performance, including optimal control [12], modified PID control [13], robust loopshaping [14], gain scheduling [15], multimodel control [16], model predictive control [17], bilinear adaptive control [18], nonlinear adaptive control [19]- [22], artificial neural network approach [23]- [24] and fuzzy rule-based approach [25]- [26].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Control of a pH Process

Wan

Shang

Wang

2006

2006 IEEE International Conference on Fuzzy Systems

View full text Add to dashboard Cite

pH process control is a challenging problem due to the strong nonlinearity and extreme sensitivity to disturbances of the process. This paper presents an application of one-stepahead adaptive fuzzy control scheme for a strong acid-strong base neutralization process. The controller is designed based on a Mamdani type fuzzy system constructed to model the dynamics of the process. The fuzzy system model can take advantage of both a priori linguistic human knowledge through parameter initialization, and process measurements through online parameter adjustment using the least square algorithm with deadzone. In both setpoint tracking and disturbance rejection tasks, simulation experiments show satisfactory performances of the resulted pH control scheme as well as performance improvements owing to the linguistic information.

show abstract

Time Optimal and Ziegler-Nichols Control. Experimental and Theoretical Results

Cited by 47 publications

References 4 publications

On-Line Control of a Nonlinear Multivariable Process

On-Line Control of a Nonlinear Multivariable Process

Design and Implementation of a Novel Self-adaptive Fuzzy Logic Controller for a pH Neutralization Process

Adaptive Fuzzy Control of a pH Process

Contact Info

Product

Resources

About