scholarly journals Cascaded PID Control System for UAV with Gain Factor Prediction Using ML

2021 ◽  
Vol 9 (11) ◽  
pp. 665-674
Author(s):  
Shubhankar Goje
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Polynomial Regression ◽  
Poor Performance ◽  
Gain Factor ◽  
Prediction Algorithm ◽  
Pid Controllers ◽  
New Approach ◽  
Robot Operating System

Abstract: Drones are not inherently stable, necessitating the use of a flight controller. If the UAV is properly tuned, the drone will fly steadily; otherwise, it won’t. Hence, we have used a PID (proportional, integral, differential) controller for a stable flight. A well-functioning PID controller should enable amazing climbs and long-range flights. But, when used singly, PID controllers can provide poor performance, resulting in a long settling time, overshoot, and oscillation. Here, we propose a new approach to maneuver UAVs using a PID control system and overcome the shortcomings of using PID controllers in UAVs. This disadvantage is resolved using the Machine Learning polynomial regression model. The gain factors in a PID control system, which is otherwise ideally constant, should be changed in order to reduce the minor instabilities for a smooth flight. Our method has been elaborated and illustrated with suitable diagrams in the following work. When simulated in Gazebo on a Robot Operating System (ROS), our technique is proven to be successful. Keywords: Control Systems, PID, UAV, Drones, Polynomial Regression, Gain Factor, Prediction Algorithm.

Fuzzy PID Control for Fast-Steering Mirror System

2014 ◽  
Vol 568-570 ◽  
pp. 1026-1030
Author(s):  
Xue Jin Bai ◽  
Yong Ming Qiao
Keyword(s):  
Control System ◽  
Transient Process ◽  
Pid Control ◽  
Pid Controller ◽  
High Speed ◽  
Mirror System ◽  
Pid Controllers ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Fuzzy Pid Controller

Fast-steering mirror system demands higher accuracy and fast responding speed to track targets, but the conventional PID controllers cannot meet the demands. Instead, the fuzzy PID control can greatly improve the capturing and tracking capacities to high-speed dynamic targets. So we apply the fuzzy-PID controller in the positioning loop of the stabilized system, not only improving the transient process of the control system and decreasing the overshoot, but also enhancing the accuracy of tracking stabilization and response. At the end, simulations were performed to test the effectiveness of this method through the MATLAB platform.

scholarly journals Kalman Filter to Improve Performance of PID Control Systems on DC Motors

2021 ◽  
Vol 5 (3) ◽  
pp. 96
Author(s):  
Seta Yuliawan ◽  
Oyas Wahyunggoro ◽  
Nurman Setiawan
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Absolute Error ◽  
Pid Controllers ◽  
Electronic Components ◽  
Improve Performance ◽  
Noise Sources ◽  
Dc Motors

A proportional–integral–derivative (PID) controller is a type of control system that is most widely applied in industrial world. Various tuning models have been developed to obtain optimal performance in PID control. However, the methods are designed under ideal circumstances. This means that the control system which has been built will not work optimally when noise exists. Noise can come from electrical vibrations, inference of electronic components, or other noise sources. Thus, it is necessary to design PID control system that can work optimally without being disturbed by noise. In this research, Kalman filter was used to improve the performance of PID controllers. The application of Kalman filter was used to reduce the noise of the input signal so that it could generate output signal which is in accordance with the expected output. Simulation result showed that the PID performance with Kalman filter was more optimal than the ordinary one to minimize the existing noise. The resulting speed of DC motor with Kalman filter had a lower overshoot than PID control without Kalman filter. This method resulted lower integral of absolute error (IAE) than ordinary PID controls. The IAE value for the PID controller with the Kalman filter was 25.4, the PID controller with the observer was 31.0, while the IAE value in the ordinary controller was only 60.9.

Optimal tuning of PID controller in time delay system: a review on various optimization techniques

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Thomas George ◽  
V. Ganesan
Keyword(s):  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Delay Systems ◽  
Pid Controllers ◽  
Time Delay Systems ◽  
Process Industries ◽  
First Order

AbstractThe processes which contain at least one pole at the origin are known as integrating systems. The process output varies continuously with time at certain speed when they are disturbed from the equilibrium operating point by any environment disturbance/change in input conditions and thus they are considered as non-self-regulating. In most occasions this phenomenon is very disadvantageous and dangerous. Therefore it is always a challenging task to efficient control such kind of processes. Depending upon the number of poles present at the origin and also on the location of other poles in transfer function different types of integrating systems exist. Stable first order plus time delay systems with an integrator (FOPTDI), unstable first order plus time delay systems with an integrator (UFOPTDI), pure integrating plus time delay (PIPTD) systems and double integrating plus time delay (DIPTD) systems are the classifications of integrating systems. By using a well-controlled positioning stage the advances in micro and nano metrology are inevitable in order satisfy the need to maintain the product quality of miniaturized components. As proportional-integral-derivative (PID) controllers are very simple to tune, easy to understand and robust in control they are widely implemented in many of the chemical process industries. In industries this PID control is the most common control algorithm used and also this has been universally accepted in industrial control. In a wide range of operating conditions the popularity of PID controllers can be attributed partly to their robust performance and partly to their functional simplicity which allows engineers to operate them in a simple, straight forward manner. One of the accepted control algorithms by the process industries is the PID control. However, in order to accomplish high precision positioning performance and to build a robust controller tuning of the key parameters in a PID controller is most inevitable. Therefore, for PID controllers many tuning methods are proposed. the main factors that lead to lifetime reduction in gain loss of PID parameters are described in This paper and also the main methods used for gain tuning based on optimization approach analysis is reviewed. The advantages and disadvantages of each one are outlined and some future directions for research are analyzed.

Simulation Analysis of Electro-Pneumatic Proportional Fuzzy-PID Control System Based on AMESim/Simulink

2014 ◽  
Vol 945-949 ◽  
pp. 2568-2572
Author(s):  
Si Yuan Wang ◽  
Guang Sheng Ren ◽  
Pan Nie
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Proportional Control ◽  
Fuzzy Pid Controller ◽  
Design Requirements ◽  
Set Up

The test rig for hydro-pneumatic converter used in straddle type monorail vehicles was researched, and its electro-pneumatic proportional control system was set up and simulated based on AMESim/Simulink. Compared fuzzy-PID (Proportion Integral Derivative) controller with PID controller through fuzzy logic tool box in Simulink, the results indicate that, this electro-pneumatic proportional control system can meet design requirements better, and fuzzy-PID controller has higher accuracy and stability than PID controller.

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Design of PID Controller Based on Compensation with Repetitive Control

2011 ◽  
Vol 105-107 ◽  
pp. 2125-2128
Author(s):  
Hong Liang Guo ◽  
Dong Jie Zhao ◽  
Ling Zhao ◽  
Qing Wang
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Combustion Engine ◽  
Permanent Magnet Synchronous Motor ◽  
Tracking Error ◽  
Repetitive Control ◽  
Valve Train ◽  
Accuracy Control ◽  
Loop Control

Valve train is one of important mechanisms in internal combustion engine. The experiment is an important method to study the valve train. In the design of valve train experiment, the Permanent-Magnet Synchronous Motor (PMSM) is used as the driving force to drive the camshaft. PID controller based on compensation with repetitive control is designed to control the PMSM. It can eliminate all periodic errors in closed-loop control. And it has a virtue of nonparametric dependence on its control performance by combining with two control methods. An example has been given and simulation has been made. The simulation result shows that the controller apparently improves the position tracking precision and reduces the tracking error of servo system. So the PID control system based on compensation with repetitive control has a much higher accuracy than the PID control system has. It is fit for high-accuracy control of valve train.

Research on Fuzzy Pid Control System of Temperatuer for Tertiary Air

2013 ◽  
Vol 341-342 ◽  
pp. 892-895
Author(s):  
Jun Chao Zhang ◽  
Shao Hong Jing
Keyword(s):  
Control System ◽  
Control Theory ◽  
Pid Control ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Effective Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Fuzzy Pid Controller ◽  
Self Tuning

The introduction of the AQC boiler has complex effects on the temperature of Tertiary air, traditional PID is difficult to achieve the effective control. Combined the method of the conventional PID with the fuzzy control theory, a fuzzy self-tuning PID controller is designed. Compared with traditional PID, results of simulation show that the fuzzy PID controller improves not only the adaptability and robustness of the system, but also the system's static and dynamic performance.

Application of Gain Scheduled PID Control in the Lithography Positioning System

2012 ◽  
Vol 220-223 ◽  
pp. 1752-1756
Author(s):  
Gui Rong Dong
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Structural Vibration ◽  
Positioning System ◽  
Mean Square ◽  
Positioning Control ◽  
System A ◽  
Gain Scheduled ◽  
Small Disturbances

According to the perturbation in lithography positioning control system, a novel gain scheduled PID controller using a root mean square (RMS) signal is proposed. Perturbation is also referred as the stage hunting, and the positioning control system will be very weak against small disturbances such as electrical noise or even structural vibration of the building in which the stage is installed. The gain scheduled PID controller is used to minimize the stage hunting and simultaneously maximize the immunity to disturbances. Simulations results verify the effectiveness of the gain scheduled PID controller for the positioning control in the lithography stage, as compared with the traditional PID controller.

PID Control Based Missile Sub-Channel Simulation

2012 ◽  
Vol 433-440 ◽  
pp. 7011-7016 ◽  
Author(s):  
Chao Bo Chen ◽  
Bing Liu ◽  
Ning He ◽  
Song Gao ◽  
Quan Pan
Keyword(s):  
Control System ◽  
Real Time ◽  
Flight Control ◽  
Pid Control ◽  
Pid Controller ◽  
Channel Model ◽  
Control Method ◽  
Flight Control System ◽  
Aerodynamic Control ◽  
The Stability

The accuracy and real-time of modern missile flight control system of traditional aerodynamic can not be satisfied. In this paper a new method is presented to improve the accuracy and real-time of missiles under this condition. First of all, a missile sub-channel model of the dynamic equations and steering gear is established, then based on the established model, using PID controller to control steering gear and three channels of missile pitch, yaw, roll respectively which is called missile sub-channel PID control method, and finally making use of MATLAB/Simulink to complete the simulation. Simulation results show that compared with traditional aerodynamic control system, this method can reduce the response time of aerodynamic missile and enhance the stability of the control system obviously.

scholarly journals STUDI NUMERIK SIMULASI ROBOT PEMBERSIH KACA PADA GEDUNG BERTINGKAT

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Balisranislam Balisranislam ◽  
I Nyoman Sutantra ◽  
Bambang Sampurno ◽  
Herry Sufyan Hadi
Keyword(s):  
Control System ◽  
Public Relations ◽  
Pid Control ◽  
Pid Controller ◽  
Circulation System ◽  
Natural Lighting ◽  
Pid Tuning ◽  
Human Labor ◽  
Glass Cleaning ◽  
Tuning Methods

<p><span lang="IN">Buildings have priority to support the comfort and public relations of air circulation system and natural lighting, where the most widely used system is glass. In general, the process of cleaning glass in multi-storey building using conventional labor is by human labor. This process is relatively simple but has a loss in work accidents. Therefore, this study discusses glass cleaning robots. the working system of moving the wheel of the robot directly, and the control system using PID control. Tuning PID using Zigler-Nichols and Find Tuning methods with Simulink. Based on the results of PID Controller Calculation using Zigler Nichols method, the value obtained Kp = 0,01446, Ki = 0,0000026, and Kd = 9524,35. While calculation of PID controller using PID tuning with simulink, obtained value Kp = 19,365, Ki = 13,115, and Kd = 5,699. The speed control system using the Zigler-Nichols method does not produce a good response, because the resulting response is still unstable. While PID control using Tuning can produce a good response with up time can be achieved within 1.39 seconds, over shoot by 8% and the exact completion time is 5 seconds</span></p><p> </p>

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