Handbook of PI and PID Controller Tuning Rules

“…The chosen controllers are conventional PID type which is widely used in process control industries and its analogous fractional order PI D λ μ , proposed by Podlubny [21] which is gaining increased interest amongst the research community [22]- [26]. The PI D λ μ controller has been considered to have a parallel structure (11) similar to the conventional PID controller [1].…”

Section: Controller Structure and Objective Function For Tuningmentioning

confidence: 99%

“…In the present paper, GP is used for symbolic regression to find out an analytic expression that maps the input variables of the process parameters to the output values of the controller parameters while minimizing the mean absolute error (MAE) of the predicted controller parameters (from the rule) and the specified well-tuned values. Thus instead of finding the coefficients of a particular structure as in the conventional regression in [1], [28]- [33]; GP searches in the infinite dimensional functional space to find an optimum structure along with the numerical coefficients, minimizing MAE of the controller parameters.…”

Section: Genetic Programming Based Analytical Tuning Rule Extraction mentioning

confidence: 99%

“…O' Dwyer [1] has given many complicated analytical PID tuning rules especially for SOPTD systems with minimum integral error index. The present study reports the analytical tuning rules for PID/FOPID controllers with minimum error index as well as controller effort for wide variety of higher order process including repeated pole and non-minimum phase processes.…”

Section: Genetic Programming Based Analytical Tuning Rule Extraction mentioning

confidence: 99%

“…These rules are mainly devised from certain design specification in time or frequency domain. O' Dwyer [1] has tabulated several optimal PI/PID controller tuning rules for various types of reduced order processes based on diverse control objectives like set-point tracking, load disturbance rejection etc. The conventional step-response process reaction curve based graphical method to obtain First Order Plus Time Delay (FOPTD) models for unknown processes has been extended by Skogestad in [2] for PID controller tuning.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2012

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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.

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The Proportional‐Integral‐Derivative (PID) Controller

Vilanova

Visioli

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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Commercial controllers with a proportional–integral–derivative (PID) control algorithm were introduced back in the 1940s. As it has been widely reported elsewhere, 80 years later PID is still the most common control algorithm used in the processes industry. In this article, fundamentals of PID control are outlined. Starting from the elemental constituent control actions that are at the core of the basic control law, additional considerations, functionalities, and implementation facts are also introduced. Afterward, special attention is placed on the considerations regarding PID‐based feedback control loops. Although the central concept of PID control can be gleaned from an examination of how these three terms are blended to form a control signal, the intelligent application of PID control in any given case requires an understanding of the process dynamics at hand as well of the achievable feedback properties.

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Handbook of PI and PID Controller Tuning Rules

Cited by 569 publications

References 0 publications

Modified active disturbance rejection control for time-delay systems

Modified active disturbance rejection control for time-delay systems

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

The Proportional‐Integral‐Derivative (PID) Controller

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