PID and Fuzzy-PID Control Model for Quadcopter Attitude with Disturbance Parameter

Kuantama, Endrowednes; Vesselényi, Tiberiu; Dziţac, Simona; Ţarcă, Radu

doi:10.15837/ijccc.2017.4.2962

Cited by 70 publications

(29 citation statements)

References 5 publications

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“…The controller is designed using the incremental PID/PD control algorithm and multilayer neural network. The PID and PD control equations can be expressed in (11) and (12) respectively.…”

Section: Neural Network Pid/pd Control Structurementioning

confidence: 99%

“…So, combining the characteristics of PID/PD controllers with intelligent algorithms represents a very promising technique for a nonlinear system. The designed neural network and Fuzzy PID control algorithms are tested using the full nonlinear mathematical model of the considered Quadcopter with disturbed and variable inputs (Bojja et al, 2019, Kuantama et al, 2017, Chen et al, 2015. As mentioned in previous researches, the neural networks and the fuzzy logic PID controllers can achieve good results in controlling an intelligent vehicle, and provide safe driving.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking

Hamidi¹,

Mjahed²,

Kari³

et al. 2019

SIC

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The purpose of this research is to design adaptive control methods for addressing the stabilization and trajectory tracking problems in a quadcopter unmanned aerial vehicle (UAV). To accomplish these tasks, a comparative study of the Proportional Integral Derivative (PID) and PD controllers is performed. Intelligent algorithms (IAs) have been used to tune the conventional structure of PID/PD controllers. The proposed hybrid intelligent controllers consist of the neural network PID/PD (NNPID/PD) and the Optimized Fuzzy PID/PD based on the Particle Swarm Optimization (FPID/PD-PSO). Adaptive neural networks are deployed to schedule PID/PD gains, the improved back-propagation algorithm is used to update the weights of the neural network. Then, an effective control approach based on adaptive PID Fuzzy logic and Particle Swarm Optimization (PSO) algorithm has been applied. PSO algorithm is introduced to adjust the scaling factors for improving the convergence speed and production rate. Finally, in order to demonstrate the robustness of the proposed control methods, disturbances in the quadcopter system are added. The results so obtained demonstrate the effectiveness of the proposed control strategy.

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“…The controller is designed using the incremental PID/PD control algorithm and multilayer neural network. The PID and PD control equations can be expressed in (11) and (12) respectively.…”

Section: Neural Network Pid/pd Control Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking

Hamidi¹,

Mjahed²,

Kari³

et al. 2019

SIC

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show abstract

“…Because the steering gear drive system has a very large overshoot, this article aims to reduce the overshoot of the system. According to the PID parameter setting principle [14]: the function of the proportional coefficient K p is to speed up the response speed of the system and improve the adjustment accuracy of the system, the integral time constant T i is to eliminate the steady-state error of the system. The function of the differential time constant T d is to improve the dynamic performance of the system.…”

Section: Pid Control Systemmentioning

confidence: 99%

“…The discrete form of the PID formula can be further derived from Equation 13 (14) In the above formula, Kp is the proportion coefficient, Ki is the integral coefficient, and Kd is the differential coefficient.…”

Section: Pid Control Systemmentioning

confidence: 99%

Design and Analysis of a Drive System for a Series Manipulator Based on Orthogonal-Fuzzy PID Control

et al. 2019

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Because the proportional–integral–derivative (PID) parameters selected by experience are random, the control effect of fuzzy PID cannot be optimized. In order to improve the accuracy and stability of robot motion control, an orthogonal-fuzzy PID intelligent control method is proposed. In this paper, the electric steering gear is used as the actuator, and the mathematical model of the servo motor joint drive system is established. The simulation analysis of the original control, PID control, fuzzy PID control, and orthogonal-fuzzy PID control of the manipulator joints in the Simulink software simulation environment and the motion control experiment of the manipulator show that using the orthogonal test method to adjust the PID parameters can quickly determine the appropriate PID parameters and greatly reduce the number of trials. The rise time, adjustment time, and overshoot of the system are significantly reduced by using fuzzy PID control, which can improve the adaptability of the system. By comparing and analyzing fuzzy PID and orthogonal-fuzzy PID control methods, it can be found that the system of orthogonal-fuzzy PID for optimal factor level combination (Kp = 0.1, Ki = 30 and Kd = 0.02) is the optimal system. The experiment results show that the orthogonal-fuzzy PID can further improve the accuracy of the system and reduce the oscillation process of the system near the steady state and make the motion more stable.

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“…This control system allows for minimizing errors during manoeuvers, by performing a manual adjustment to the coefficient of the PID in correlation with the quadcopter's angle and position on each axis. Details of the PID algorithm and simulation analysis are presented in previous research [30]. A translation or rotational error can occur with different magnitudes on the pitch, roll, and yaw motion.…”

Section: Pid Control Systemmentioning

confidence: 99%

Flight Stability Analysis of a Symmetrically-Structured Quadcopter Based on Thrust Data Logger Information

et al. 2018

Self Cite

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Quadcopter flight stability is achieved when all of the rotors-propellers generate equal thrust in hover and throttle mode. It requires a control system algorithm for rotor speed adjustment, which is related with the translational vector and rotational angle. Even with an identical propeller and speed, the thrusts generated are not necessarily equal on all rotors-propellers. Therefore, this study focuses on developing a data logger to measure thrust and to assist in flight control on a symmetrically-structured quadcopter. It is developed with a four load cells sensor with two-axis characterizations and is able to perform real-time signal processing. The process includes speed adjustment for each rotor, trim calibration, and a proportional integral derivative (PID) control tuning system. In the data retrieval process, a quadcopter was attached with data logger system in a parallel axis position. Various speeds between 1200 rpm to 4080 rpm in throttle mode were analyzed to determine the stability of the resulting thrust. Adjustment result showed that the thrust differences between the rotors were less than 0.5 N. The data logger showed the consistency of the thrust value and was proved by repeated experiments with 118 s of sampling time for the same quadcopter control condition. Finally, the quadcopter flight stability as the result of tuning process by the thrust data logger was validated by the flight controller data.

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PID and Fuzzy-PID Control Model for Quadcopter Attitude with Disturbance Parameter

Cited by 70 publications

References 5 publications

Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking

Neural Network and Fuzzy-logic-based Self-tuning PID Control for Quadcopter Path Tracking

Design and Analysis of a Drive System for a Series Manipulator Based on Orthogonal-Fuzzy PID Control

Flight Stability Analysis of a Symmetrically-Structured Quadcopter Based on Thrust Data Logger Information

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