scholarly journals A Novel Self-Tuning Fuzzy Logic-Based PID Controllers for Two-Axis Gimbal Stabilization in a Missile Seeker

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
S. Senthil Kumar ◽  
G. Anitha
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Control Method ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Self Tuning ◽  
Essential Function ◽  
Tracking Device ◽  
Rule Bases ◽  
Logic Rule

Tracking a target is an essential function of a seeker for missiles. The target tracking mechanism of a seeker consists of gimbals, mounted with gyroscopes, and an antenna or some other energy receiving devices such as radar, infrared (IR), or laser. Stabilization of such a gimbal is necessary for any guided missile to maintain the tracking device always pointing towards the target. For the stabilization of the gimbal system, several control methods have been employed for making the gimbal to follow an input rate command by eliminating all the gimbal disturbances. Here, a new self-tuning fuzzy logic-based proportional, integral, derivative (PID) controller is introduced for the stabilization of a two-axis gimbal for a manoeuvring guided missile. The proposed control method involves tuning the gains of the PID controller based on the fuzzy logic rule bases considering the missile body rotation. The performance of the stabilization loops has been verified through MATLAB simulations for fuzzy logic-based PID controller compared with the conventional PID controller. The simulation results show the response of the gimbal system with stabilization loops met the control requirements with fuzzy PID controllers but not with conventional PID controllers.

Self-Tuning Neuro-PID Controller for Indoor Entertainment Balloon Robot

2018 ◽  
Vol 30 (3) ◽  
pp. 390-396
Author(s):  
Hiroya Nagata ◽  
Soichiro Yokoyama ◽  
Tomohisa Yamashita ◽  
Hiroyuki Iizuka ◽  
Masahito Yamamoto ◽  
...  
Keyword(s):  
Pid Controller ◽  
Control Algorithm ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Target Trajectory ◽  
Self Tuning ◽  
Classical Control ◽  
Changes Over Time ◽  
Exact Ideal ◽  
Over Time

Proportional-integral-derivative (PID) controllers are a classical control algorithm that are still widely used owing to their simplicity and accuracy. However, tuning the three parameters is difficult. No methods have been known to determine the exact ideal combination of the P, I, and D gains. Moreover, controlling a system that contains dynamics changes over time using fixed parameters is difficult. A self-tuning neuro-PID controller is applied to a balloon robot for indoor entertainment to enhance its accuracy in following a target trajectory. Our experiment shows the effectiveness of the neuro-PID controller over conventional hand-tuned PID controller.

scholarly journals Position Tracking of ML System using CSA Based PID Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 1956-1959
Keyword(s):  
Pid Controller ◽  
Magnetic Levitation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Pid Controllers ◽  
Position Tracking ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Simulation Results

The classical proportional integral derivative (PID) controllers are still use in various applications in industry. Magnetic levitation (ML) systems are rigidly nonlinear and sometimes unstable systems. Due to inbuilt nonlinearities of ML systems, tracking of position of ML Systems is still difficult. For the tracking purpose of position, PID controller parameters are found by choosing Cuckoo Search Algorithm (CSA) of optimization. The ranges of parameters are customized by z-n method of parameters. Simulation results show the tracking of position of ML systems using conventional and optimized parameters obtained with the CSA based controller.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Fuzzy logic algorithm of hovering control for the quadrotor unmanned aerial system

2017 ◽  
Vol 10 (4) ◽  
pp. 451-463 ◽  
Author(s):  
Lie Yu ◽  
Jia Chen ◽  
Yukang Tian ◽  
Yunzhou Sun ◽  
Lei Ding
Keyword(s):  
Fuzzy Logic ◽  
Tracking Control ◽  
Pid Controller ◽  
Position Control ◽  
Horizontal Component ◽  
Pid Controllers ◽  
Content Type ◽  
Fuzzy Logic Algorithm ◽  
Velocity Tracking ◽  
Hovering Control

Purpose The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems (UASs). In addition, the aim of using two PID controller is to achieve the position control and velocity control simultaneously. Design/methodology/approach The dynamic of the UASs is mathematically modeled. One PID controller is used for position tracking control, while the other is selected for the vertical component of velocity tracking control. Meanwhile, fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position. Findings Based on this fuzzy logic algorithm, the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero. The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground. Social implications The acquired results are based on simulation not experiment. Originality/value This is the first study to use two independent PID controllers to realize stable hovering control for UAS. It is also the first to use the velocity of the UAS to calculate the desired position.

Application of Fuzzy PID Control in Sluice Control

2012 ◽  
Vol 241-244 ◽  
pp. 1248-1254
Author(s):  
Feng Chen Huang ◽  
Hui Feng ◽  
Zhen Li Ma ◽  
Xin Hui Yin ◽  
Xue Wen Wu
Keyword(s):  
Fuzzy Control ◽  
Pid Control ◽  
Information Integration ◽  
Pid Controller ◽  
Fuzzy Pid ◽  
Proportional Integral Derivative ◽  
Fuzzy Pid Control ◽  
Tuning Method ◽  
Fuzzy Pid Controller ◽  
Self Tuning

Fuzzy control, based on traditional Proportional-Integral-Derivative (PID) control, is used to improve the management of a hydro-junction’s sluice scheduling. In this study, we combined the PID and Fuzzy control theories and determined the PID parameters of the fuzzy self-tuning method of a hydro-junction’s sluice. A fuzzy self-tuning PID controller and its algorithm were designed. In hydro-junction sluice control, the Fuzzy PID controller can modify PID parameters in real-time, resulting in a more dynamic response. The application of the fuzzy self-tuning PID controller in the CiHuai River project information integration system yielded very good results.

Smith Prediction Based on Genetic Algorithm Self-Tuning PID Control

2014 ◽  
Vol 602-605 ◽  
pp. 1186-1189
Author(s):  
Dong Sheng Wu ◽  
Qing Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Delay System ◽  
Time Delay System ◽  
Self Tuning ◽  
On Line ◽  
Turning Control

Aiming at the phenomena of big time delay are normally existing in industry control, this paper proposes an intelligent GA-Smith-PID control method based on genetic algorithm and Smith predictive compensation algorithm and traditional PID controller. This method uses the ability of on line-study, a self-turning control strategy of GA, and better control of Smith predictive compensation to deal with the big time delay. This method overcomes the limitation of traditional PID control effectively, and improves the system’s robustness and self-adaptability, and gets satisfactory control to deal with the big time delay system.

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