# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
###### Abstract  
The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot's independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab(r) and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research. differential drive robots; fuzzy logic control; genetic algorithms; optimal control; wheeled mobile robot control; pymoo 2021  
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On the one hand, the conventional control techniques such as the back-stepping, the linearization, the PID, etc. depend largely on precise mathematical modeling. So, the absence of an accurate analytical model makes it quite difficult to design an appropriate controller. In practice, robots are nonlinear systems and usually have to operate in unstructured environments with uncertainties, which leads to even more complex tasks for the robots. On the other hand, intelligent control algorithms such as the Fuzzy Logic (FL) control, the neural network control, etc. allow us to drive the DDWMR without a detailed mathematical model and thus they are preferred over others. The FL control algorithms can deliver fast and precise dynamic responses, which allows information analysis through a true-and-false scale, and are generally robust, tolerant to uncertainties and noise in the input data [13]. It should be mentioned that many different FL control algorithms such as (1) Conventional FL control methods and hybrid fuzzy-PID control algorithms [2,4,6]; (2) Adaptive FL algorithms, for example, an adaptive fuzzy output feedback control of a Wheeled Mobile Robot (WMR) [7], an adaptive FL control based on harmony search [14], an adaptive fuzzy output feedback simultaneous posture stabilization and tracking control [15], a robust adaptive fuzzy variable structure tracking control for a WMR [16]; (3) A hybrid fuzzy-neural network, such as navigation of a WMR using adaptive network-based fuzzy inference system [9], an adaptive network fuzzy inference system based navigation controller for mobile robot [10], a fuzzy-neuro based navigational strategy for mobile robot [11]; (4) Bio-inspired optimization techniques applied for FL control methods, such as using the particle swarm optimization [1], the Genetic Algorithm [8,17,18,19,20,21], etc. have been used to control a WMR so that the robot can perform its different tasks. However, most of the above-mentioned control algorithms only focus on solving the robot motion control problems, i.e., making the robot move smoothly, with no or minimum vibration, tire slipping, etc.