scholarly journals Optimum PID controller for airplane wing tires based on gravitational search algorithm

2021 ◽  
Vol 10 (4) ◽  
pp. 1905-1913
Author(s):  
Ammar Hussein Mutlag ◽  
Omar Nameer Mohammed Salim ◽  
Siraj Qays Mahdi
Keyword(s):  
Pid Controller ◽  
Transfer Functions ◽  
Search Algorithm ◽  
Fitness Function ◽  
Gravitational Search Algorithm ◽  
Harmony Search ◽  
Absolute Error ◽  
Airplane Wing ◽  
Gravitational Search ◽  
Opening And Closing

In this paper, the gravitational search algorithm (GSA) is proposed as a method for controlling the opening and closing of airplane wing tires. The GSA is used to find the optimum proportional-integral-derivative (PID) controller, which controls the wing tires during take-off and landing. In addition, the GSA is suggested as an approach for overcoming the absence of the transfer function, which is usually required to design the optimum PID. The use of the GSA is expected to improve the system. Two of the most popular optimisation algorithms-the harmony search algorithm (HSA) and the particle swarm optimisation (PSO)-were used for the sake of comparison. Moreover, the GSA-, HSA- and PSO-based optimum PID controllers were compared with one of the most important PID tuning methods, the Ziegler-Nichols (ZN) method. In this study, the integral time absolute error (ITAE) was used as a fitness function. First, four transfer functions for different applications were used to compare the performance of the GSA-based PID (PID-GSA), HSA-based PID (PID-HSA), PSO-based PID (PID-PSO) and Ziegler-Nichols-based PID (PID-ZN). Next, the GSA was used to design the optimum PID controller for the opening and closing systems of the airplane wing tires. The results reveal that the GSA provides better outcomes in terms of ITAE when compared with the other adopted algorithms. Furthermore, the GSA demonstrates a fast and robust response to reference variation.

scholarly journals Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 1819-1826
Author(s):  
M. A. Basri ◽  
A. Noordin
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Fitness Function ◽  
Gravitational Search Algorithm ◽  
Absolute Error ◽  
Difficult Problem ◽  
Quadrotor Uav ◽  
Search Optimization ◽  
Backstepping Controller ◽  
Gravitational Search

Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Gravitational Search Algorithm Optimization for PID Controller Tuning in Waste-water Treatment Process

2015 ◽  
Vol 73 (3) ◽  
Author(s):  
Mohamad Saiful Islam Aziz ◽  
Sophan Wahyudi Nawawi ◽  
Shahdan Sudin ◽  
Norhaliza Abdul Wahab ◽  
Mahdi Faramarzi ◽  
...  
Keyword(s):  
Pid Controller ◽  
Treatment Process ◽  
Search Algorithm ◽  
Waste Water Treatment ◽  
Gravitational Search Algorithm ◽  
Water Treatment Process ◽  
Pid Controller Tuning ◽  
Gravitational Search ◽  
Law Of Gravity

This paper presents a new approach of optimization technique in the controller parameter tuning for waste-water treatment process (WWTP) application. In the case study of WWTP, PID controller is used to control substrate (S) and dissolved oxygen (DO) concentration level. Too many parameters that need to be controlled make the system becomes complicated. Gravitational Search Algorithm (GSA) is used as the main method for PID controller tuning process. GSA is based on Newton's Law of Gravity and mass interaction. In this algorithm, the searcher agents survey the masses that interact with each other using law of gravity and law of motion. For WWTP system, the activated sludge reactor is used and this system is multi-input multi-output (MIMO) process. MATLAB is used as the platform to perform the simulation, where this optimization is compared to other established optimization method such as the Particle Swarm Optimization (PSO) to determine whether GSA has better features compared to PSO or vice-versa. Based on this case-study, the results show that transient response of GSA-PID was 20%-30% better compared to transient response of the PSO-PID controller.

OPTIMAL TIMAH TASOH RESERVOIR IN, PERLIS: AN OPERATION USING THE GRAVITATIONAL SEARCH ALGORITHM (GSA)

2015 ◽  
Vol 77 (30) ◽  
Author(s):  
Asmadi Ahmad ◽  
Siti Fatin Mohd Razali ◽  
Ahmed El-Shafie ◽  
Zawawi Samba Mohamad
Keyword(s):  
Search Algorithm ◽  
Fitness Function ◽  
Gravitational Search Algorithm ◽  
Inertial Mass ◽  
Storage Condition ◽  
Sustainable Engineering ◽  
Release Policy ◽  
Gravitational Search ◽  
The Times ◽  
Law Of Gravity

The construction of a dam or a reservoir can have a serious impact the environment. When dealing with the increasing water demand from irrigation and water supply, alternative solution has to be sought way rather than building a new dam. Therefore, reservoir optimization can be employed as a new approach in sustainable engineering to solve this kind of problem. In this paper an optimization algorithm based on the Newton law of gravity, which is called Gravitational Search Algorithm (GSA), is introduced for optimal reservoir operation study. In GSA, every mass has four specifications, which are position, inertial mass, active gravitational mass, and passive gravitational.  The location of the mass is the solution of the problem, with the gravitational and inertial masses being determined by using a fitness function. Furthermore, The algorithm was applied to the Timah Tasoh reservoir and the release policy was tested by using simulation of demand and release. The result revealed that 72% of the times the reservoir managed to fulfill the demand to the users. Moreover, with the new optimized release policy, the dam operator can manage the reservoir release for the users by determining the inflow pattern, as well as by and observing the current storage condition as a guideline

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