Optimal design of 2-D FIR digital differentiator using $$L_1$$ L 1 -norm based cuckoo-search algorithm

2016 ◽  
Vol 28 (4) ◽  
pp. 1569-1587 ◽  
Author(s):  
Apoorva Aggarwal ◽  
Manjeet Kumar ◽  
Tarun K. Rawat ◽  
D. K. Upadhyay
Author(s):  
Wenjie Wang ◽  
Congcong Chen ◽  
Yuting Cao ◽  
Jian Xu ◽  
Xiaohua Wang

Background: Dexterity is an important index for evaluating the motion performance of a robot. The size of the robot connecting rods directly affects the performance of flexibility. Objective: The purpose of this study is to provide an overview of optimal design methods from many pieces of literature and patents, and propose a new optimal design method for ensuring the robot completes its tasks flexibly and efficiently under workspace constraints. Methods: The kinematics and working space of the robot are analyzed to determine the range of motion of each joint. Then, a dexterity index is established based on the mean value of the global spatial condition number. Finally, an improved cuckoo algorithm is proposed, which changes the step size control factor with the number of iterations. Taking the dexterity index as the objective optimization function and the working radius as the constraint condition, the improved cuckoo search algorithm is used to optimize the size of the robot rod. Results: The improved cuckoo algorithm and proposed rod size optimized method are fully evaluated by experiments and comparative studies. The optimization design process shows that the proposed method has better solution accuracy and faster convergence speed. The optimized design results show that the robot's dexterity index has increased by 26.1%. Conclusion: The proposed method has better solution accuracy and faster convergence speed. The method was suitable for optimizing the rod parameters of the robot, and it was very meaningful to improve the motion performance of the robot.


2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.


Sign in / Sign up

Export Citation Format

Share Document