scholarly journals Histogram of oriented gradients as feedback on proportional navigation (PN) control and proportional-integral-derivative (PID) control on quadcopter platforms

2019 ◽  
Author(s):  
A. Faza ◽  
S. Darma
Keyword(s):  
Pid Control ◽  
Histogram Of Oriented Gradients ◽  
Proportional Integral Derivative ◽  
Proportional Navigation ◽  
Proportional Integral

Modelling and PID Control of a Quadrotor Aerial Robot

2014 ◽  
Vol 903 ◽  
pp. 327-331 ◽  
Author(s):  
Ismail Mohd Khairuddin ◽  
Anwar P.P.A. Majeed ◽  
Ann Lim ◽  
Jessnor Arif M. Jizat ◽  
Abdul Aziz Jaafar
Keyword(s):  
Pid Control ◽  
Pitch Angle ◽  
Pid Controller ◽  
Pd Controller ◽  
Proportional Integral Derivative ◽  
Hovering Flight ◽  
Proportional Integral ◽  
Aircraft Model ◽  
Aerial Robot ◽  
Aerial Vehicle

This paper elucidates the modeling of a + quadrotor configuration aerial vehicle and the design of its attitude and altitude controllers. The aircraft model consists of four fixed pitch angle propeller, each driven by an electric DC motor. The hovering flight of the quadrotor is governed by the Newton-Euler formulation. The attitude and altitude controls of the aircraft were regulated using heuristically tuned (Proportional-Integral-Derivative) PID controller. It was numerically simulated via Simulink that a PID controller was sufficient to bring the aircraft to the required altitude whereas the attitude of the vehicle is adequately controlled by a PD controller.

Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Takao Sato ◽  
Toru Yamamoto ◽  
Nozomu Araki ◽  
Yasuo Konishi
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Design Method ◽  
Design Parameters ◽  
Generalized Predictive Control ◽  
Control Performance ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

scholarly journals Analysis and Simulation of Proportional Derivative and Proportional Integral Derivative Control Systems Using Xcos Scilab

2021 ◽  
Vol 3 (1) ◽  
pp. 36-44
Author(s):  
Edi Kurniawan
Keyword(s):  
Pid Control ◽  
System Response ◽  
Response Analysis ◽  
System Control ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Pid Tuning ◽  
Proportional Integral Derivative Control ◽  
Tuning Process ◽  
Derivative Control

PID (Proportional Integral Derivative) control is a popular control in the industry and aims to improve the performance of a system. This control has controlling parameters, namely Kp, Ki, and Kd which will have a control effect on the overall system response. In this research, P, PD, and PID control simulations with the transfer function of the mass-damper spring as a plant using Xcos Scilab. The method used is the trial and error method by setting and varying the values of the control constants Kp, Ki, and Kd to produce the desired system response. The value adjustment of system control parameters is carried out with several variations, namely Kp control variation, Kp variation to constant Kd, Kd variation to constant Kp, Kp variation to Ki, constant Kd, variation of Ki to Kp, constant Kd and variation of Kd to Kp, Ki constant. The second method is automatic tuning which is done through mathematical calculations to obtain PID control constants, namely Zieglar Nichols PID tuning with the oscillation method. From the system simulation results, the best parameter is obtained through the Zieglar Nichols PID tuning process based on the results of the transient response analysis, namely when the proportional gain value (Kp) is 50. The system performance characteristics produced in the tuning process are 3.994 seconds of settling time at 2.36 seconds research time. resulting in a maximum overshoot value of 3.6% and a peaktime value of 3.994 seconds

scholarly journals IMPLEMENTASI DAN ANALISIS KENDALI KECEPATAN MOTOR BLDC 1 kW MENGGUNAKAN ALGORITMA PID

JURNAL ELTEK ◽  
2019 ◽  
Vol 17 (2) ◽  
pp. 81
Author(s):  
Muchlis Dwi Ardiansyah ◽  
Fatkhur Rohman
Keyword(s):  
Steady State ◽  
Direct Current ◽  
Pid Control ◽  
Block Diagram ◽  
Bldc Motor ◽  
Trial And Error ◽  
Proportional Integral Derivative ◽  
Matlab Simulink ◽  
Proportional Integral ◽  
Parameter Values

Pemanfaatan teknologi alternatif dalam bidang otomotif maupun otomasi industri menggunakan motor Brushless Direct Current (BLDC) sudah banyak digunakan karena memiliki kelebihan dibanding dengan jenis mesin penggerak bertenaga elektrik lainnya. Namun motor BLDC masih memiliki beberapa kekurangan ketika menerima beban sehingga menyebabkan penurunan kecepatan putaran pada motor BLDC. Tujuan penelitian ini adalah untuk merancang dan mengaplikasikan sistem kendali kecepatan motor BLDC dengan kontrol Proportional Integral Derivative (PID) dan menentukan nilai parameter untuk mendapatkan persentase error steady state terkecil pada variasi  kecepatan dan beban motor BLDC. Metode pengambilan data diambil dengan cara memasukkan nilai parameter secara trial and error. Sebagai simulasi beban, motor BLDC dihubungkan dengan generator yang diberi beban berupa lampu yang divariasikan. Hasil pengujian mendapatkan pemodelan blok diagram PID dengan Matlab Simulink. Hasil parameter kontrol PID diperoleh nilai Kp = 1,5; Ki = 10,5 dan Kd = 0,04. Dengan nilai parameter tersebut motor BLDC dapat mempertahankan nilai set point dengan kestabilan yang tinggi (error steady state rendah).   The usage of alternative technology in the field of automotive and industrial automation using Brushless Direct Current (BLDC) has been widely used because it has advantages compared to other types of electric-powered drive engines. But the BLDC motor still has some disadvantages when receiving a load that causes a decrease in rotation speed on the BLDC motor. The purpose of the study is to design and apply a BLDC motor speed control system with a Proportional Integral Derivative (PID) control and determine the parameter value to obtain the smallest error steady state percentage at a speed variation and motor load the BLDC. The method of retrieving data was taken by entering parameter values by trial and error. As a load simulation, the BLDC motor was connected to a generator that was given the load in the form of a varied lamp. The test results gets the PID block diagram modeling with Matlab Simulink. The results of the PID control parameter are Kp = 1.5; Ki = 10.5 and Kd = 0.04. With these parameter values, the BLDC motor can maintain the setpoint value with high stability (low steady-state error).

Two-loop controller design and implementations for an inverted pendulum system with optimal self-adaptive fuzzy-proportional–integral–derivative control

2021 ◽  
pp. 014233122110403
Author(s):  
Tayfun Abut ◽  
Servet Soyguder
Keyword(s):  
Pid Control ◽  
Inverted Pendulum ◽  
Absolute Error ◽  
Performance Criteria ◽  
Control Methods ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Adaptive Fuzzy ◽  
Squared Error ◽  
Self Adaptive

Since inverted pendulum systems (IPSs) are under-actuated systems, controlling these systems has become an important problem. Because of their irregular movements and being structurally unstable, the balancing of these systems poses a major problem. In this study, the dynamic model of a linear IPS has been obtained by using the Newton–Euler method and also conventional proportional–integral–derivative (PID), fuzzy logic (FLC), and self-adaptive fuzzy-proportional–integral–derivative (SAF-PID) control algorithms have been designed for the control of the system. The purpose of these designed controllers is to keep the arms of the IPS on the moving cart in a vertical position and bring the cart to the specified equilibrium position. In conventional control methods, unwanted oscillations have occurred during the movement of the cart. By using two-loop control methods, it is aimed to reduce these oscillations and to design a robust system. The results have been obtained using the conventional PID, FLC, and SAF-PID controllers. To increase the performance of the SAF-PID control type, the optimum value of the coefficients of the points where the legs of the membership functions touch were calculated using the firefly optimization algorithm. To conclude, the designed controllers were performed on computer simulation. The results are given graphically and numerically. Mean absolute error (MAE), mean squared error, integral absolute error IAE, and integral squared error have been compared and examined with each other and with the studies in the literature using performance criteria. As a result, the proposed control method was at least 26.31% more successful in the angular control of the pendulum and at least 9.26% better in the position control of the cart than the studies in the literature.

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