Comparison of some well-known PID tuning formulas

Tan, Wen; Liu, Jizhen; Chen, Tongwen; Marquez, Horacio J.

doi:10.1016/j.compchemeng.2006.04.001

Cited by 169 publications

(80 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1-4 as the Nyquist based SOPTD models closely follows the original higher order process in each case than the other three cases. To further justify the point, a comparison of the achievable accuracies of the H 2 norm based and proposed Nyquist based model reduction technique has been shown on the basis of objective function (3) and shown in semi-log/log-log scale in Fig 5. It is clear from Fig. 5 that for each case the proposed Nyquist based SOPTD models yields more accurate models in frequency domain over that with the H 2 norm based methods.…”

Section: Model Reduction Of a Test-bench Of Higher Order Processesmentioning

confidence: 99%

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

et al. 2012

View full text Add to dashboard Cite

Abstract:Genetic Algorithm (GA) has been used in this paper for a new approach of sub-optimal model reduction in the Nyquist plane and optimal time domain tuning of PID and fractional order (FO) PI λ D μ controllers. Simulation studies show that the Nyquist based new model reduction technique outperforms the conventional H 2 norm based reduced parameter modeling technique. With the tuned controller parameters and reduced order model parameter data-set, optimum tuning rules have been developed with a test-bench of higher order processes via Genetic Programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by weighted sum of error index and controller effort. From the reported Pareto optimal front of GP based optimal rule extraction technique a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP based tuning rules has been compared with original GA based control performance for the PID and PI λ D μ controllers, handling four different class of representative higher order processes. These rules are very useful for process control engineers as they inherit the power of the GA based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three dimensional plots of the required variation in PID/FOPID controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP based tuning rules has also been shown with credible simulation examples.

show abstract

Section: Model Reduction Of a Test-bench Of Higher Order Processesmentioning

confidence: 99%

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Bikas Mondal et al [9] in many industrial applications conventional controller are used due to their simplicity and robustness. All the systems are non-linear in nature: Conventional controller is not always providing good and correct result.…”

Section: K J åStröm Et Al [3]mentioning

confidence: 99%

Comparative Analysis of Pso-Pid and Hu-Pid

Thakur¹,

Batish²

2017

IJERA

View full text Add to dashboard Cite

PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the "bread and butter" of power system engineering. It is an important component in every control engineer"s tool box. PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence on the PID controller

show abstract

“…The sensitivity function appears also in the transfer function of the input disturbance to the output (16) The quantity represents good evaluation of the performance rejection of the load disturbance. Finally, in order to achieve good transient response, good stability margin, good robustness in the presence of model uncertainties and good rejection of the load disturbance, it is necessary to determine the parameters and such that (17) There is not a known analytical solution of this optimization problem. A way to solve this problem is the numerical optimization.…”

Section: ____________________________________________________________mentioning

confidence: 99%

Performance Enhancement of SOPDT System with Numerically Optimized PID Controller

B¹

2015

IJRITCC

View full text Add to dashboard Cite

Abstract-This paper presents a simple but effective method for designing robust PID controller. The robust PID controller design problem is solved by the maximization, on a finite interval of the shortest distance from the Nyquist curve of the open loop transfer function to the critical point i.e. from the knowledge of maximum sensitivity . Simple formulae are derived to tune/design PID controllers to achieve the improved performance for the given process or system. From control theory we know that all the real time processes have inherent time delays and time constants associated with it. The PID tuning method elaborated in this paper is found to be superior as compared with basic PID tuning methods based on second-order plus delay-time (SOPDT) model of process. Two simulation examples are demonstrated to show the applicability and effectiveness of the given method.

show abstract

Comparison of some well-known PID tuning formulas

Cited by 169 publications

References 13 publications

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

Comparative Analysis of Pso-Pid and Hu-Pid

Performance Enhancement of SOPDT System with Numerically Optimized PID Controller

Contact Info

Product

Resources

About