Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

doi:10.17559/tv-20170612090511

Cited by 3 publications

(2 citation statements)

References 24 publications

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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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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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“…An alternative approach to identification of characteristic points on the process model frequency characteristic (Nyquist or Bode plots) in the vicinity of the cross-over frequency is by using time-delay filtering of process input and output in real-time during a modified relay experiment [22,23], or process input and output integration [24]. Some other refinements of the relayfeedback auto-tuning experiment may include: (i) identification of parameters of the second-order proportional model with dead time (SOPDT) [25], (ii) using an asymmetric relay function in order to estimate in real time the process model gain and normalized dead-time [26], and (iii) using evolutionary algorithms [27,28] or particle swarm optimization type search [29] to find the optimal PID controller parameters based on suitable cost function. Unfortunately, most of these auto-tuning algorithms require considerable amount of time and computing power to obtain the final result, which may not be suitable for practical applications.…”

Section: Introduction (Introductory Remarks)mentioning

confidence: 99%

PI/PID Controller Relay Experiment Auto-Tuning with Extended Kalman Filter and Second-Order Generalized Integrator as Parameter Estimators

2023

Teh. vjesn.

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This paper presents a method for the estimation of key parameters of limit cycle oscillations (amplitude and frequency) during a relay experiment used for automatic tuning of proportional-integral (PI) and proportional-integral-derivative (PID) feedback controllers. The limit cycle parameter estimator is based on the first-order extended Kalman filter (EKF) for resonance frequency estimation, to which a second-order generalized integrator (SOGI) is cascaded for the purpose of limit cycle amplitude estimation. Based on thus-obtained parameters of the limit cycle oscillations, the ultimate gain and the ultimate period of the limit cycle oscillations are estimated. These are subsequently used for the tuning of PI and PID controller according to Takahashi modifications of Ziegler-Nichols tuning rules. The proposed PI and PID controller auto-tuning method is verified by means of simulations and experimentally on the heat and air-flow experimental setup for the case of air temperature feedback control. The results have shown that the proposed auto-tuning system based on relay control experiment for the heat and air-flow process PI/PID temperature control can capture the ultimate gain and period parameters fairly quickly in simulations and in experiments. Subsequent controller tuning according to Takahashi modifications of Ziegler-Nichols rules using thusobtained ultimate point parameters can provide favourable closed-loop load disturbance rejection, particularly in the case of PID controller.

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Design and Evaluation of the PID, SMC and MPC Controllers by State Estimation by Kalman Filter in the TRMS System

Zapata

Heredia

Proaño

2020

Innovation and Research

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Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

Cited by 3 publications

References 24 publications

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

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

PI/PID Controller Relay Experiment Auto-Tuning with Extended Kalman Filter and Second-Order Generalized Integrator as Parameter Estimators

Design and Evaluation of the PID, SMC and MPC Controllers by State Estimation by Kalman Filter in the TRMS System

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