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Design and Implementation of Fuzzy Logic Controller for A Class of Hexapod Mobile Robot
Najmurrokhman A.a, Kusnandara, Sofyan G.I.a, Djamal E.C.a, Munir A.b, Wibowo B.H.a
a Dept. of Electrical Engineering, Universitas Jenderal Achmad, Yani Cimahi, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Recently, the development of automation technology relies on the application of artificial intelligence methods such as artificial neural networks, fuzzy logic, genetic algorithms, and so on. Application of such methods improves the system performance and to some extent increases the efficiency of the resources. This paper describes the application of fuzzy logic methods in controlling the speed of a hexapod mobile robot by utilizing Takagi-Sugeno-Kang type of its inference system. A fuzzy logic controller is used to drive a hexapod mobile robot in such a way to avoid an obstacle in the front of it. Such controller was designed to adjust the speed of gait based on two fuzzy inputs, i.e distance between robot and obstacle and its error. The distance variables and its errors comprise of three fuzzy sets with triangular membership function. While, the output of system is the speed variables of robot with three fuzzy sets by their triangle membership function. The experimental results show that the system work well according the objective of the system design. By comparing to the fuzzy logic controller run by simulation using fuzzy logic toolbox under Matlab environment and the experimental results, the speed of robot movement can be adjusted. Robot could move forward with the speed gradually decreases according to the distance between the robot and obstacle in the front of it.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Artificial intelligence methods,Automation technology,Design and implementations,Fuzzy logic controllers,Neural networks , fuzzy logic,Takagi-sugeno,Takagi-Sugeno-Kang types,Triangular membership functions[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]fuzzy logic,hexapod mobile robot,obstacle avoidance,Takagi-Sugeno-Kang[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/EECCIS.2018.8692971[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]