A review of PID control, tuning methods and applications

Borase, Rakesh P.; Maghade, D. K.; Sondkar, Shilpa Y.; Pawar, Sushant N.

doi:10.1007/s40435-020-00665-4

Cited by 498 publications

(201 citation statements)

References 124 publications

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“…This work uses a fixed-state GA, in which the algorithm starts from a random population, where each individual is a vector with the three PID gains (Kp, Ki, and Kd). Taking into account the law of control of a PID described by (1).…”

Section: Database With Genetic Algorithmsmentioning

confidence: 99%

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Self-Tuning Neural Network PID With Dynamic Response Control

et al. 2021

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PID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a selfadjusting PID controller based on a backpropagation artificial neural network. The network calculates the appropriate gains according to the desired output, that is, the dynamic response desired which is composed of the transient part and the stationary part of the step response of a system. The contribution of the work is that in addition to using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were used as input data for the network. An offline training database was created using genetic algorithms to obtain the dynamic response data associated with PID gains. The genetic algorithm allows getting data in different operating ranges and allows using only stable gains combinations. The database was used for training. Subsequently, the neural network estimates an appropriate gain combination, adapting to the error and the desired response. The method performance is evaluated by controlling the speed of a direct current motor. The results indicate an average error of 4% for the database between the requested and system response. On the other hand, the gains estimated by the network in the test dataset (1544 combinations) did not cause instability and complying with the expected dynamic response in 86% of the dataset.

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Section: Database With Genetic Algorithmsmentioning

confidence: 99%

“…PIDs are the most widely used controllers commercially due to their efficiency/simplicity ratio [1]. Its flexibility allows to find them in multiple areas.…”

Section: Introductionmentioning

confidence: 99%

Self-Tuning Neural Network PID With Dynamic Response Control

et al. 2021

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“…An essential part of the control system is the control algorithm. The proportional-integral-derivative (PID) controller is among the most frequently used techniques used for industrial control systems according to the literature [40]. PID controllers have already been used and investigated in various applications in the field material processing to control parameters such as cladding height, bead geometry or temperature [41][42][43].…”

Section: Existing Work On Process Optimization and Limitationsmentioning

confidence: 99%

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

et al. 2021

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In laser-based additive manufacturing (AM) of metal parts from powder bed, information about actual part quality obtained during build is essential for cost-efficient production and high product quality. Reliable and effective monitoring strategies for laser powder bed fusion (LPBF) therefore remain in high demand and are the subject of current research. To address this demand, a novel analysis approach using high dynamic range (HDR) optical imaging in combination with convolutional neural networks (CNN) is proposed for spatially resolved and layer-wise prediction of the surface roughness of LPBF parts. In a further step, the predicted surface roughness maps are used as a feedback signal for a reinforcement learning technique that employs a dynamics model to subsequently identify optimal process parameters under varying and uncertain conditions. The proposed approach ultimately combines the estimation of the local surface roughness based on image texture and model-based reinforcement learning to an in-situ optimization framework for LPBF processes. In addition, the relationship between the layer surface roughness of the part and the overall part density is discussed on the basis of experimental data, which also indicate the applicability of the proposed method in industrial environments. This preliminary study is a first step towards highly adaptive and intelligent machines in the field of automated laser powder bed fusion with the primary goals of reducing production costs and improving the environmental fingerprint as well as print quality.

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“…PID controllers are one of the most preferred controllers in the areas of process control, motion control, hydraulics, pneumatics thanks to their simplicity, low maintenance costs and ease of implementation [41]. PID controllers deliver a good efficiency in terms of cost per benefit ratio where other controllers may fail [42]. Usage area of the PID controllers are not limited with just lowlevel control applications but PID controller are also employed in modern applications such as self-driving cars, autonomous robots, and UAVs [43].…”

Section: Pid Controllermentioning

confidence: 99%

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

Kaba

2020

IJAST

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QUAVs have some shortcomings in terms of nonlinearities, coupled dynamics, unstable open–loop characteristics, and they are prone to internal and external disturbances. Therefore, control problem of the QUAVs is still an open issue. Designed controllers based on the linear dynamics have limited operating ranges. Therefore, nonlinear dynamics of the QUAVs must be derived and used in the control problem. Although some advanced controllers are presented for QUAV control, PID controllers are the most employed, well–known controllers with the simple structure, ease of implementation, solid functionality and robustness amongst the variations up to a degree. In this paper, PID based controllers are proposed for the nonlinear attitude dynamics to overcome the control problem of the QUAVs. However, since optimality and tuning of the PID controllers are fuzzy because of trial and error approaches, swarm intelligence based meta-heuristic algorithms (ABC, ACO and PSO) are employed to optimize the PID coefficients. Results are compared in terms of transient analysis and MC analysis to cover the rise time, settling time, percentage overshoot, steady–state error for the former and stochastic fitness evaluation for the latter, respectively.

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A review of PID control, tuning methods and applications

Cited by 498 publications

References 124 publications

Self-Tuning Neural Network PID With Dynamic Response Control

Self-Tuning Neural Network PID With Dynamic Response Control

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

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