Evolutionary Tuning Method for PID Controller Parameters of a Cruise Control System Using Metamodeling

Málek, Michal; Ali, Mohamed Sultan Mohamed

doi:10.1155/2009/234529

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…There are several classical approaches to tune the PID controller namely Ziegler-Nichols [7], Cohen-Coon [8], Chien-Hrones-Reswick [9], Astrom and Hagglund [10]. However, these conventional methods typically consume a great deal of time as tuning of parameters must be done iteratively until optimal solution is obtained [11] and results in undesirable overshoot [12]. To overcome these disadvantages, number of PID tuning methods have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

PID speed control of DC motor using meta-heuristic algorithms

Acharya

Dhakal

Bhattarai

et al. 2021

IJPEDS

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This paper presents archimedes optimization algorithm(AOA) and dispersive flies optimization(DFO) to optimally tune gain parameters of PID control scheme in order to regulate DC motor’s speed. These suggested techniques tune the controller by the minimization of the fitness function represented by the integral of time multiplied by absolute error (ITAE). The modelling and simulation are carried out in MATLAB/Simulink. The transient response of unit step input obtained from AOA-PID-ITAE andDFO-PID-ITAE controllers were compared to those obtained from Ziegler-Nichols (ZN) method and particle swarm optimization(PSO). The results indicate that AOA-PID-ITAE and DFO-PID-ITAE are more efficient than ZN method and PSO in reducing rise time and settling time. Likewise, DFOconverge faster to the optimal solution with lower overshoot than AOA and PSO.

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Section: Introductionmentioning

confidence: 99%

PID speed control of DC motor using meta-heuristic algorithms

Acharya

Dhakal

Bhattarai

et al. 2021

IJPEDS

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“…It is worth noting that SUMOs have been widely used to optimize UAVs [ 15 , 16 , 17 ]. Researchers have used SUMOs to tune different controllers in different systems successfully, such as the mixing process [ 18 ], the cruise control system [ 19 ], and the unmanned underwater vehicle [ 20 ]. Among these systems, through offline optimization, various controllers were tuned, including the fuzzy logic controller [ 20 ], the proportional-integral controller [ 18 ], and the PID controller [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Singularity points are fatal to the accuracy of SUMOs. In previous studies, singularity points did not raise close attention because researchers have prior experience, and successful samples were easy to be found [ 18 , 19 , 20 , 23 , 25 ]. The reasons for singularity points occurrence in this work are summarized as follows: First, the error of closed-loop systems may be reinforced compared to open-loop systems.…”

Section: Introductionmentioning

confidence: 99%

Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

Wang

Duan

Yan

et al. 2020

Entropy

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The leader–follower structure is widely used in unmanned aerial vehicle formation. This paper adopts the proportional-integral-derivative (PID) and the linear quadratic regulator controllers to construct the leader–follower formation. Tuning the PID controllers is generally empirical; hence, various surrogate models have been introduced to identify more refined parameters with relatively lower cost. However, the construction of surrogate models faces the problem that the singular points may affect the accuracy, such that the global surrogate models may be invalid. Thus, to tune controllers quickly and accurately, the regional surrogate model technique (RSMT), based on analyzing the regional information entropy, is proposed. The proposed RSMT cooperates only with the successful samples to mitigate the effect of singular points along with a classifier screening failed samples. Implementing the RSMT with various kinds of surrogate models, this study evaluates the Pareto fronts of the original simulation model and the RSMT to compare their effectiveness. The results show that the RSMT can accurately reconstruct the simulation model. Compared with the global surrogate models, the RSMT reduces the run time of tuning PID controllers by one order of magnitude, and it improves the accuracy of surrogate models by dozens of orders of magnitude.

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“…There are many theoretical and practical papers on PID tuning algorithms, in time and frequency domain. They are using analytical [1][2][3][4][5], graphical [6][7][8], or empirical methods, including artificial intelligence [9,10].…”

Section: Introductionmentioning

confidence: 99%

On PID Controller Design by Combining Pole Placement Technique with Symmetrical Optimum Criterion

Nicolau

2013

Mathematical Problems in Engineering

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In this paper, aspects of analytical design of PID controllers are studied, by combining pole placement technique with symmetrical optimum criterion. The proposed method is based on low-order plant model with pure integrator, and it can be used for both fast and slow processes. Starting from the desired closed-loop transfer function, which contains a second-order oscillating system and a lead-lag compensator, it is shown that the zero value depends on the real-pole value of closed-loop transfer function. In addition, there is only one pole value, which satisfies the assumptions of symmetrical optimum criterion imposed to open-loop transfer function. In these conditions, by combining the pole placement technique with symmetrical optimum criterion, the analytical expressions of the controller parameters can be simplified. For simulations, PID autopilot design for heading control problem of a conventional ship is considered.

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Evolutionary Tuning Method for PID Controller Parameters of a Cruise Control System Using Metamodeling

Cited by 9 publications

References 17 publications

PID speed control of DC motor using meta-heuristic algorithms

PID speed control of DC motor using meta-heuristic algorithms

Rapidly Tuning the PID Controller Based on the Regional Surrogate Model Technique in the UAV Formation

On PID Controller Design by Combining Pole Placement Technique with Symmetrical Optimum Criterion

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