PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes

Castellanos-Cárdenas, Duby; Castrillón, Fabio; Vásquez, Rafael E.; Smith, Carlos A.

doi:10.3390/pr8091183

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…ISE is likely to be as an indicator to measure error in the unexpected changes of the process loads or SP, if the response has produce a larger error. The expression of IAE, ITAE and ISE are respectively formulated as ( 7)- (9).…”

Section: Performance Indexes Measurementmentioning

confidence: 99%

“…Nevertheless, the ZN is appropriately used only in the processes with a slight time delay and lower-order models [8]. On the other hand, Internal Model Control (IMC) is another common option, whereby its aggressiveness degree is adjustable to suit the users' expectation for the speed control in their applications [9] but yet compromises the optimum control for both SP and load changes [10].…”

Section: Introductionmentioning

confidence: 99%

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Metaheuristic-based PID Control: Evaluating the Effect of Parameter Settings

Chew

Wong

Juwono

et al. 2023

Preprint

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Proportional-Integral-Derivative (PID) control method has been utilized in many industrial control applications, which has its limitations for the optimized control. Manually calculated controller tunings by using fixed formulas are only adequately to give basic control but not well-competent to non-linear process with various operating levels. Optimization analysis via metaheuristic approach has substantially obtained the best solutions and has proven its credibility to provide the better controller values. Nevertheless, the optimization analysis might provides bad result if the determinant parameter settings are not properly adjusted during the optimization analysis. This paper objectively presents a direct way to determine the best determinant parameter settings for the optimization analysis, whereby the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are chosen for the research analysis. In detail, parameter settings that include upper and lower bounds (UB, LB), maximum iteration (MaxIt), population size (nPop), mutation rate (mu) and damping ratio (wdamp) are analyzed. Ultimately, analysis results are compared with manually calculated controller tunings by using Level Process Control Training Module, SE-207. Both performance indexes and response curves showed that PSO and GA were better performed than the conventional tunings. While comparing optimization analyses, PSO reacted more aggressively than GA corresponding to slight overshoots but both optimizations produced very close error values. In conclusion, metaheuristic approach with proper parameter settings produces better control tunings as for the controlled loop demands aggressive control action would prefer PSO, and in contrast, a stabilized control prefers to apply GA for the nonlinear process.

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Section: Performance Indexes Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metaheuristic-based PID Control: Evaluating the Effect of Parameter Settings

Chew

Wong

Juwono

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Irshad and Ali [12] investigated the control of inverse response systems by determining PI/PID controller parameters using the PSO algorithm based on integral performance criteria. A research based on IMC and PID controller control of second-order time delay and inverse response processes is presented in [13]. Kaya's method gives optimum analytical tuning rules for Integral-Proportional Derivative (I-PD) controllers in integrating processes [14].…”

Section: Introductionmentioning

confidence: 99%

Design of I-PD Controller Based Modified Smith Predictor for Processes With Inverse Response and Time Delay Using Equilibrium Optimizer

Doğruer

2023

IEEE Access

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In the process control industry, it is arduous to control some integrating or unstable processes since they involve time delays and have an inverse response. Conventional controllers such as PID cannot provide sufficient control performance alone in the control of these systems. This article proposes a control algorithm based on an I-PD-based Smith predictor for the control of time-delayed integrating or unstable inverse processes. The controller parameters are tuned by using the Equilibrium optimizer (EO) algorithm, which is presented in the literature in 2020, in the proposed control approach. The EO algorithm aims to determine the optimal controller parameters by minimizing the error and control signal using a multiobjective function based on ITAE performance criterion. Thus, the controller parameters that will provide the set-point tracking and disturbance rejection control most properly can be determined. Simulation studies are conducted based on different process structures to evaluate the performance of the proposed method. The proposed method is compared with studies from the literature in terms of set-point tracking, parameter uncertainties, control signals, and disturbance rejection. It is seen that the transient responses and disturbance rejection of the time-delayed and inverse response integrating or unstable processes are improved with the proposed method.INDEX TERMS Equilibrium optimizer algorithm, I-PD controller design, inverse response, non-minimum phase system, Smith predictor, and time delay systems.

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“…Although the parameters were presented for an under-dumped example, they are applicable to critically damped and over damped, as well. Therefore, the same procedure may be applied to any second-order plant [4,5].…”

Section: Introductionmentioning

confidence: 99%

Auto-Tuning PID Controller Based on Genetic Algorithm

Figueiredo¹,

Toso²,

Schmith³

2023

Disturbance Rejection Control

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The PID controller is widely used in industry and its tuning is always a concern for the plant stabilization. Several methods for auto-tuning the PID have been proposed over the years, however, the relay method is the most used even though this method may determine nonideal PID gains and cause some physical stress on the plant. Here is presented a proposal for an auto-tuning PID controller based on a genetic algorithm. Genetic algorithm is a well-known method that imitates the natural selection process in order to obtain approximate solutions to optimization problems. Here, the method is presented in underdamped plants with the hypothesis that any plant can be approximated to a second-order function. From the unit step response of the system, the maximum overshoot and peak time were used in the GA evolution to obtain optimal PID parameters. The system was tested with a set of parameters and compared to MATLAB PID tuner function. Using the rising time and the settling time of unit step response from the closed loop system as validation parameters, the GA presented better results than the MATLAB tuner for most cases.

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PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes

Cited by 12 publications

References 53 publications

Metaheuristic-based PID Control: Evaluating the Effect of Parameter Settings

Metaheuristic-based PID Control: Evaluating the Effect of Parameter Settings

Design of I-PD Controller Based Modified Smith Predictor for Processes With Inverse Response and Time Delay Using Equilibrium Optimizer

Auto-Tuning PID Controller Based on Genetic Algorithm

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