Real-Time Speed Control of a Mobile Robot Using PID Controller

MohandSaidi, Sabrina; Mellah, Rabah

doi:10.1007/978-3-030-96311-8_51

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The PID control is considered as Method 4 (Aqeel‐Ur‐Rehman & Cai, 2020; MohandSaidi & Mellah, 2022). The fundamental idea behind PID is to modify the feedback signal, particularly the error between the desired and measured states.…”

Section: Resultsmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

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Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross‐entropy‐based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT‐UMbot) for road cracks. Specifically, the CEAFC method uses cross‐entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT‐UMbot based on a four‐wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state‐of‐the‐art performance with high efficiency.

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Section: Resultsmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

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“…MRs are made to carry out certain jobs in a challenging and hazardous environment. Because of this, it's crucial to proceed at a precise, accurate velocity that will be appropriate for the task itself and the surrounding circumstances [27]. Fig.…”

Section: A Pid Controllersmentioning

confidence: 99%

Discussion on different controllers used for the navigation of mobile robot

Raj,

Kos

2024

International Journal of Electronics and Telecommunications

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Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.

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Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

Saidi¹,

Mellah²,

Fekik

et al. 2022

International Journal of Service Science, Management, Engineering, and Technology

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In this work, the differential mobile robot is controlled utilizing fuzzy PID speed control, which combines fuzzy control with conventional PID control in real time. The path may be convoluted, and the surrounding environment may contain a range of arbitrary shape and size obstacles. A monocular camera is used to detect obstacles during the navigation process. To enable a robot to travel within an indoor space while avoiding obstacles, a basic image processing approach based on area of interest was used. The goal of this research is to develop a fuzzy PID speed controller on a real robot, as well as a simple and efficient visual obstacle avoidance system. MATLAB is used to implement the control system. GUIDE (graphical user interface development environment) has enabled the creation of graphical user interfaces. These interfaces make it easy to manipulate the system in real time and capture live video. The proposed methodologies are tested on a non-holonomic dr robot i90 mobile robot, and the results are satisfactory.

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Real-Time Speed Control of a Mobile Robot Using PID Controller

Cited by 3 publications

References 13 publications

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Discussion on different controllers used for the navigation of mobile robot

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

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