Genetic Algorithm Based Adaptive PID Tuning of Time Delay Process

Barman, Alpana; Dutta, Souvik; Tiwari, Kamalika; Roy, Subhasish; Pain, Santigopal

doi:10.1007/978-3-031-22485-0_7

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Although this controller type is conventional one, but till now it is widely used [6], [7]. Generally, as shown in figure (6), the desired position (displacement) is the control system input. The error between desired and measured signals is the input for controller.…”

Section: Pid Controller Design and Simulationmentioning

confidence: 99%

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Position control of Stewart platform with electric linear actuator

Mohamed,

Roshdy,

Ali

et al. 2023

J. Phys.: Conf. Ser.

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In recent decades, several technological applications have depended on manipulators like Stewart platform due to its accuracy and precision. Based on actuation type, Stewart platforms could be rotary or linearly actuated. Electric linear actuators are devices commonly consist of DC or AC motors coupled with lead screw or mechanism of gears and spindle which are used to convert rotary motion into linear (push or pull) motion. To increase accuracy and precision and achieve the desired linear actuator response, controllers should be used. The main aspiration of this work is to investigate the feasibility of using PID controllers for position control of Stewart platform with linear actuators. The mathematical model has been derived and the model transfer function has been obtained. To meet the required response of performance characteristics, the PID controller has been designed based on analysis of root-locus. Model simulation analysis has been carried out on both MATLAB and Simulink. For the electric linear actuator, comparison between the obtained response and the results of Ziegler-Nichols tuning method has been discussed on basis of the specifications of the time response. The PID controller parameters for the electric linear actuator has been tested experimentally and compared with simulation results.

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Section: Pid Controller Design and Simulationmentioning

confidence: 99%

“…The error could be calculated as follows 𝑒(𝑡) = 𝑅(𝑡) − 𝑦(𝑡) (20) 𝑅(𝑡) is desired reference displacement and 𝑦(𝑡) is actual measured displacement. The PID controller block diagram could be on the following form as shown in figure(6).…”

mentioning

confidence: 99%

Position control of Stewart platform with electric linear actuator

Mohamed,

Roshdy,

Ali

et al. 2023

J. Phys.: Conf. Ser.

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“…Zhu et al [24] compared GA-optimized fuzzy-PID controller with simple fuzzy-PID and showed that the former one has 22.32% less regulation time, 23.29% shorter rise time and 7.18% reduced steady-state error. Barman et al [25] demonstrated that Ziegler-Nichols (ZN) tuning technique for PID provides better outcomes when compared with conventional PID. But still, it had large settling time and rapid oscillations.…”

Section: Introductionmentioning

confidence: 99%

GA Optimized PI-PDN Robust Control of a 1-DOF Maglev Precision Position System

Malik,

Ahmad,

Wadood

2023

IEEE Access

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This paper investigates 2 difficult problems encountered in magnetic levitation systems: robust control design and disturbance rejection. A PI-PDN robust control strategy is developed to control the position of a 1-degree-of-freedom (DOF) magnetic levitated rigid beam system or MLS for precision positioning applications under parametric variations, uncertainties, and external disturbances. The proposed PI-PDN gains are optimized by employing a Genetic Algorithm (GA), Marine Predator Algorithm (MPA), and Particle Swarm Optimization (PSO). GA-tuned gains lead to manifold improvement in key time and frequency domain performance specifications of rise time, settling time, overshoot, tracking error, bandwidth, classical gain and phase margins. Additionally, the metrics of disk margins (DM) are employed for assessing the robustness of a closed-loop system subjected to simultaneous variation in gain and phase. To demonstrate the performance and effectiveness of the proposed GA-PI-PDN topology, numerical simulation results are compared with corresponding results of the benchmark PIDN with that of PI-PDN and GA-PI-PDN. The developed control algorithms were deployed on a dedicated embedded system utilizing Simulink automatic C++ code generation capabilities. The development of different control strategies was aided by building high fidelity mathematical model of electromagnets and experimental determination of important model parameters.

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