Design and Real Time implementation of fmincon, MOGA tuned IO-PID and FO-PID Controllers for Stabilization of TRMS

Valluru, Sudarshan K.; Kumar, Rajul; Kumar, Rahul

doi:10.1016/j.procs.2020.04.305

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…Using the PID control can be used separately or together and not use any of the P, I or D components [25], [26] shown in Figure 5. The main parameters of the PID controller are proportional strengthening of Kp, integral time Ti, and derivative time Td.…”

Section: Proportional Integral Derivative Controlmentioning

confidence: 99%

Applying fuzzy proportional integral derivative on Internet of things for figs greenhouse

Riansyah

Mulyono

Roichani

2021

IJ-AI

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Indonesia is an agrarian country where most of population work as farmers. Various planting media have been developed in Indonesia such as using greenhouses. Greenhouse is one of very promising planting media for plant cultivators, because it can be a solution to challenges of extreme climate change. In a greenhouse, the state of the room can be easily controlled using technologies such as automatic watering systems, air temperature control, air humidity and soil moisture. This research focuses on figs by applying fuzzy proportional integral derivative (FPID) as artificial intelligence on the Internet of things (IoT) for greenhouses. It uses Tsukamoto method serves to monitor air conditions and soil conditions and then it is coupled with proportional integral derivative (PID) control to control air temperature, air humidity, and soil moisture so that it is always in the ideal condition of figs in greenhouse. By implementing FPID on IoT for greenhouse, the development of figs in greenhouse can be optimized because air and soil conditions can be maintained in ideal conditions.

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Section: Proportional Integral Derivative Controlmentioning

confidence: 99%

Applying fuzzy proportional integral derivative on Internet of things for figs greenhouse

Riansyah

Mulyono

Roichani

2021

IJ-AI

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“…In a few decades ago, many researchers proposed the different types of control techniques to control the performance of TRMS on horizontal and vertical planes. For instance, the proposed control techniques that applied in TRMS system such as Proportional-Integral-Derivative (PID) controller [4][5][6][7][8][9], Linear Quadratic Regulator (LQR) [10], Sigmoid-PID (SPID) controller [11], Fractional-order PID (FOPID) controller [12][13][14][15][16][17], Fuzzy Logic controller [18], Sliding Mode Controller (SMC) [19][20][21], Fuzzy-PID (FPID) controller [22][23][24][25][26] and Model Predictive Controller (MPC) [27]. Nevertheless, researchers and scientific community put the greatest attention in the combinations of different type control technique based PID controller.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Sigmoid Proportional-Integral-Derivative (SPID) Controller for Twin Rotor MIMO System

Qian¹,

Ghazali²,

Ahmad³

et al. 2023

JESA

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This paper presents a data driven Sigmoid Proportional-Integral-Derivation (SPID) controller for a Twin Rotor Multiple-Input-Multiple-Output (MIMO) System (TRMS). A time-varying PID parameters based on sigmoid function is adopted to solve the low control accuracy of the conventional PID controller. In particular, the parameters of new version controller were vigorously changed based on its error signal of sigmoid function where its variability is limited in a predefined upper and lower bound. These SPID parameters are then optimized by using Adaptive Safe Experimentation Dynamics (ASED) method such that the control performance accuracy in terms of trajectory tracking error and control input energy are minimized. The simulations of step response analysis and stability analysis are conducted to evaluate the effectiveness of the proposed SPID controller compared to PID controller on TRMS system. Consequently, the results obtained from the simulations revealed that the SPID controller has successfully produced improvement in terms of objective function, 𝐽, total norm of error, 𝑒̅ 1 + 𝑒̅ 2 and total norm of output, 𝑢 ̅ ℎ + 𝑢 ̅ 𝑣 by reduced 6.84%, 6.38% and 4.25%, respectively compared to the PID controller. In addition, the results of Integral-Absolute-Error (IAE), Integral-Square-Error (ISE), Integral-Time-Absolute-Error (ITAE) and Integral-Square-Error (ITSE) are proven that the SPID controller is outperform on horizontal and vertical planes for TRMS system in comparison with PID controller.

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Experimental validation of adaptive RBFNN global fast dynamic terminal sliding mode control for twin rotor MIMO system against wind effects

et al. 2021

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Design and Real Time implementation of fmincon, MOGA tuned IO-PID and FO-PID Controllers for Stabilization of TRMS

Cited by 8 publications

References 3 publications

Applying fuzzy proportional integral derivative on Internet of things for figs greenhouse

Applying fuzzy proportional integral derivative on Internet of things for figs greenhouse

Data Driven Sigmoid Proportional-Integral-Derivative (SPID) Controller for Twin Rotor MIMO System

Experimental validation of adaptive RBFNN global fast dynamic terminal sliding mode control for twin rotor MIMO system against wind effects

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