scholarly journals Flight Motion Controller Design using Genetic Algorithm for a Quadcopter

2018 ◽  
Vol 51 (3-4) ◽  
pp. 59-64 ◽  
Author(s):  
Huu Khoa Tran ◽  
Thanh Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Controller Design ◽  
Fitness Function ◽  
Absolute Error ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Time Operation ◽  
Real Time Operation ◽  
Effective Stability

In this study, the Genetic Algorithm operability is assigned to optimize the proportional–integral–derivative controller parameters for both simulation and real-time operation of quadcopter flight motion. The optimized proportional–integral–derivative gains, using Genetic Algorithm to minimum the fitness function via the integral of time multiplied by absolute error criterion, are then integrated to control the quadcopter flight motion. In addition, the proposed controller design is successfully implemented to the experimental real-time flight motion. The performance results are proven that the highly effective stability operation and the reliable of waypoint tracking.

scholarly journals Comparison between genetic algorithm and linear programming approach for real time operation

2008 ◽  
Vol 2 (3) ◽  
pp. 172-181 ◽  
Author(s):  
H. Md. Azamathulla ◽  
Fu-Chun Wu ◽  
Aminuddin Ab Ghani ◽  
Sandeep M. Narulkar ◽  
Nor Azazi Zakaria ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Real Time ◽  
Programming Approach ◽  
Linear Programming Approach ◽  
Time Operation ◽  
Real Time Operation

Investigation and performance comparison of ride comfort on the created human vehicle road integrated model adopting genetic algorithm optimized proportional integral derivative control technique

2020 ◽  
Vol 234 (2) ◽  
pp. 288-305 ◽  
Author(s):  
Arivazhagan Anandan ◽  
Arunachalam Kandavel
Keyword(s):  
Genetic Algorithm ◽  
Ride Comfort ◽  
Active Suspension ◽  
Absolute Error ◽  
Integrated Model ◽  
Suspension System ◽  
Vehicle Stability ◽  
Head Acceleration ◽  
Proportional Integral Derivative ◽  
Proportional Integral

This context exhaustively investigates the ride comfort performance index on the proposed active suspension vehicle system. Ride comfort in terms of occupants (includes driver and passenger) head acceleration, sprung mass vertical and pitching accelerations is considered. For this examination, a 14-degree-of-freedom human vehicle road integrated system model was extensively developed. Then, an active suspension system composed of a hydraulic actuator and proportional-integral-derivative controller is incorporated into the developed vehicle model to enhance the ride comfort. Besides, the designed controller needs to satisfy other vehicle performance indices like vehicle stability and ride safety. Accordingly, the controller parameters were optimally tunned with the help of genetic algorithm technique, on the basis of integral time absolute error criterion. The objective function was created on the basis of minimizing the integral time absolute error of sprung mass displacement, suspension working space and tire deflection responses. The entire response of human vehicle road integrated model, with the proposed active suspension system and passive suspension system on various random road surfaces (A, B, C, D and E with respect to ISO 8608) with five constant speeds (20, 40, 60, 80 and 100 kmph), was compared via surficial presentation. Furthermore, the comfort measures such as root mean square and vibration dose value from ISO 2631-1 were adopted to evaluate the severity between the occupants via head acceleration response. The simulation results showed that the suggested active suspension system significantly improved the ride comfort with guaranteed vehicle stability and ride safety.

Comparative Study of Fuzzy PID and PID controller optimized with Spider Monkey Optimization for a Robotic Manipulator System

2019 ◽  
Vol 12 ◽  
Author(s):  
Alka Agrawal ◽  
Vishal Goyal ◽  
Puneet Mishra
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Absolute Error ◽  
Robotic Manipulator ◽  
Spider Monkey ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Spider Monkey Optimization

Background: Robotic manipulator system has been useful in many areas like chemical industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling the end position of a robotic armeffectively. However, with the increasing non-linearity and the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network and optimization algorithms has emerged as an efficient tool for controlling the highly complex non-linear functions with uncertain dynamics. Objective: To implement an efficient and robustcontroller using Fuzzy Logic to effectively control the end position of Single link Robotic Manipulator to follow the desired trajectory. Methods: In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. Results: Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-Derivative controller have been shown in this paper and the superiority of the implemented controller has also been described by comparing itwith the conventional Proportional-Integral-Derivative controller and Genetic Algorithm optimization technique. Conclusion: From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm techniques.Also, by comparing the integral absolute error values of all the controllers, it has been found that the controller optimized with the Spider Monkey Optimization technique shows 99% better efficacy than the genetic algorithm technique.

scholarly journals Optimization of Proportional Integral Derivative Parameters of Brushless Direct Current Motor Using Genetic Algorithm

2020 ◽  
pp. 24-32
Author(s):  
Isaiah Adebayo ◽  
David Aborisade ◽  
Olugbemi Adetayo
Keyword(s):  
Genetic Algorithm ◽  
Direct Current ◽  
Pid Controller ◽  
Absolute Error ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Bldc Motor ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Brushless Dc

Optimal performance of the Brushless Direct Current (BLDC) motor is to be realized using an efficient Proportional Integral Derivative (PID) controller. However, conventional tuning technique fails to perform satisfactorily under parameter variations, nonlinear conditions and time delay. Also using conventional technique to tune the parameters gain of the PID controller is a difficult task. To overcome these difficulties, modern heuristic optimization technique are required to optimally tune the Proportional, Integral, Derivative of the controller for optimal speed control of three phase BLDC motor. Thus, genetic algorithm (GA) based PID controller was used to achieve a high dynamic control performance. The Brushless DC Motor mathematical equation which describes the voltage and corresponding rotational angular speed and torque of the brushless DC motor was employed using electrical DC Machines theorem. The Genetic algorithm was further analyzed by adopting the three common performance indices i.e. Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Absolute Error (IAE) in order to capture and compare the most suitable BLDC Motor speed and torque control characteristics. All simulations were done using MATLAB (R2018a). The simulation result showed that the system with GA-PID controller had the better system response when compared with the existing technique of ZN-PID controller.

scholarly journals Genetic algorithms for real-time operation of multipurpose water resource systems

2004 ◽  
Vol 6 (1) ◽  
pp. 19-38 ◽  
Author(s):  
Alcigeimes B. Celeste ◽  
Koichi Suzuki ◽  
Akihiro Kadota
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real Time ◽  
Water Resource ◽  
Water Resource System ◽  
Shuffled Complex Evolution ◽  
Time Operation ◽  
Real Time Operation ◽  
Evolution Algorithm ◽  
Resource System

This paper deals with the application of genetic algorithms to the operation of a water resource system in real time. A genetic algorithm was developed and applied to solve an optimization model for the operation of the system responsible for the water supply of Matsuyama City, in Japan. For comparison purposes, the same model was solved by a technique based on calculus and the Shuffled Complex Evolution Algorithm. The general characteristics of the algorithms and the results from simulations carried out for various conditions are presented. Genetic algorithms appear to be effective tools for real-time reservoir operation.

scholarly journals Fuzzy gain scheduling control apply to an RC Hovercraft

2020 ◽  
Vol 10 (3) ◽  
pp. 2434
Author(s):  
Huu Khoa Tran ◽  
Pham Duc Lam ◽  
Tran Thanh Trang ◽  
Xuan Tien Nguyen ◽  
Hoang-Nam Nguyen
Keyword(s):  
Controller Design ◽  
Tracking Error ◽  
Operation Time ◽  
Absolute Error ◽  
Gain Scheduling ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Motion Models ◽  
Complex Models ◽  
On Line

The Fuzzy Gain Scheduling (FGS) methodology for tuning the Proportional – Integral – Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional – Integral – Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller.

scholarly journals Design, implementation, control and optimization of single stage pilot scale reverse osmosis process

2021 ◽  
Author(s):  
G. Guna ◽  
D. Prabhakaran ◽  
M. Thirumarimurugan
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Reverse Osmosis ◽  
Absolute Error ◽  
Pilot Scale ◽  
Single Stage ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Squared Error ◽  
Integral Time

Abstract In this paper, a single-stage pilot-scale RO (Reverse Osmosis) process is considered. The process is mainly used in various chemical industries such as dye, pharmaceutical, Beverage, and so on. Initially, mathematical modeling of the process is to be done followed by linearization of the system. Here a dual loop construction with a master and a slave is used. The slave uses the conventional PID (Proportional Integral Derivative) with a reference model of the RO process and the master uses the FOPID (Fractional Order Proportional Integral Derivative) with a real time RO process. The slave's output is compared with output of the real time RO process to obtain the error which is in turn used to tune the master. The slave controller is tuned using Ziegler Nicholas method and the error criterion such as IAE (Integral Absolute Error), ISE (Integral Squared Error), ITSE (Integral Time Squared Error), ITAE (Integral Time Absolute Error) are calculated and the minimum among them was chosen as the objective function for the master loop tuning. Hence the tuning of the controller becomes a whole. Therefore two optimization techniques such as PSO (Particle Swarm Optimization) and Bacterial Foraging Optimization Algorithm (BFO) are used for the tuning of the master loop. From the calculations the ITSE was having the minimum value among the performance indices hence it was used as the objective function for the BFO and PSO. The best-tuned values will be obtained with the use of these techniques and the best among all can be considered for various industrial applications. Finally, the performance of the process is compared with both techniques and BFO outperforms the PSO from the simulations.

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