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TopDespite the fact that the first tests with mobile robots date back to the late 1960s, the topic did not attract major attention until the 1990s. A significant amount of research has been published. Mobile Robots have proven their worth on land, in the air, at sea, and in submarines, and are now used in industries, security, first aid, personal support, and exploration (Khamis et al., 2022, 2021; Ajeil et al., 2020a,b; Ibraheem et al., 2020a,b; Ammar et al., 2020a,b; Barakat et al., 2020). Mobile robotics is clearly at the forefront of technological innovation, with companion robots, personal assistance robots, and even robotic transportation systems. The majority of the research is devoted to creating and developing robot motion (Krivić et al., 2011; Mahfouz et al., 2013), path planning (Kayacan and Chowdhary, 2019; Matoui et al., 2019), map building (Jia et al., 2010), obstacle avoidance (Trinh et al., 2022), object tracking (Yilmaz et al., 2006, and speed control (Ng et al., 2012; Sharma and Jain, 2016). Mobile robots, for example, can map their surroundings, develop collision-free dynamic settings, and plan safe approaches to humans. The majority of research has been focused on applying dynamic models to build and implement mobile robot control (Sharma and Jain, 2016; Park et al., 2017; Sun et al., 2016). Others may not see the need to use the dynamic model or may lack access to specific information essential for its use, hence the kinematic model is widely used (Krivić et al., 2011; MohandSaidi and Mellah, 2019; Lang et al., 2010).
The differential mobile robot is becoming increasingly vital and broadly used in human daily lives. Because of its ease of assembly and intriguing kinematic features, this sort of robot is quite popular. As a result, they are widely used. In recent years, there has been an upsurge in and interest in research on differential mobile robots (Krivić et al., 2011; Lopez-Franco et al., 2015). Because lateral translation is impossible, non-holonomic mobile robots have just two degrees of freedom on a plane (Krivić et al., 2011; Matoui et al., 2019). Control with PID is a technique used in engineering to reduce process variability by altering some quantifiable system variables with feedback and compensation. Since its conception in 1910 and the presentation of the tuning rules by Ziegler–Nichols in 1942, PID control has risen in popularity significantly. PID Control is still used in almost every field. A great amount of time and effort has gone into determining the best PID settings for various process models. Ahmad et al. (2019) and Li et al. (2006) conducted a comparative study of PID controller tuning approaches. PID parameter fine tuning remains a difficult task. The majority of research is focused on the use of intelligent methods to modify the settings of this controller, such as neural networks (Rossomando and Soria, 2015; Fan and Chen, 2013) and fuzzy logic (Kumbasar and Hagras, 2014) or both techniques (Zerfa and Nouiba, 2013).