# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 5 Conclusions  
In this paper, the FLC has been introduced and successfully applied for the DDWMR control for robot movement on the given path. However, it is too complex and time-consuming to manually search for the proper parameters of the MFs, and its performances of motion control are still poor. To tackle such problems, an optimal FLC (GA_FLC) has been found with the use of the GA and the minimum energy consumption criterion. In many studies, scientists have only optimized the output MFs. However, in this study, the GA automatically tunes all the parameters of input/output MFs of the FLC. Such an approach leads to fast and effective tuning. For the optimal parameters' identification of the MFs for the FLC, the zigzag path has been used in the optimization process at first. The fitness function has been derived from the energy consumption and after that minimized. Also, the length of the traveled path has been considered. Finally, the performance of suboptimal FLC has been tested on the benchmark paths and its results have been compared to the results of several controllers based on another approaches. The comparisons have shown that the GA_FLC is much better than all used well-known controllers in terms of energy consumption while the other performances of the motion parameters are still good.  
To keep all experiments reproducible, the GA_FLC and several well-known controllers including the Circle-based controller, the Mathew-based controller, the Chaudhari-based controller, the Thoa-based controller, the Expert-based controller, and the Mohammad-based FLC controllers have been successfully implemented in the Google Colab(r) by using the Python language and made freely available for all the readers who are interested in them.  
Conceptualization, A.S. and V.T.P.; methodology, A.S., V.T.P. and V.K.; software, A.S. and V.T.P.; validation, A.S., V.T.P., V.K. and K.L.P.; formal analysis, A.S., V.T.P. and V.K.; investigation, A.S. and V.T.P.; resources, A.S. and V.T.P.; data curation, A.S. and V.T.P.; writing--original draft preparation, A.S. and V.T.P.; writing--review and editing, A.S., V.T.P., V.K. and K.L.P.; visualization, A.S., V.T.P., V.K. and K.L.P.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. and V.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by internal fund \"DZRO Cyber forces and resources\" of University of Defence, the Czech Republic.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The simulation as open-source code is has been linked to [[https://colab.research.google.com/drive/17](https://colab.research.google.com/drive/17) FUnDR7 XNjMsDOFxZnBNWfqXYntEjF?usp=sharing]([https://colab.research.google.com/drive/17](https://colab.research.google.com/drive/17) FUnDR7 XNjMsDOFxZnBNWfqXYntEjF?usp=sharing), (accessed on 2 March 2021) and it has been stored in the GitHub(r): [[https://github.com/vanthuanhvhq/GA_FLC](https://github.com/vanthuanhvhq/GA_FLC)]([https://github.com/vanthuanhvhq/GA_FLC](https://github.com/vanthuanhvhq/GA_FLC)). (accessed on 3 May 2021), also it has been published on Code Ocean [43] with the link [[https://doi.org/10.24433/CO.1867832v1](https://doi.org/10.24433/CO.1867832v1)]([https://doi.org/10.24433/CO.1867832v1](https://doi.org/10.24433/CO.1867832v1)). (accessed on 4 May 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.