Real Time Trajectory Tracking Controller based on Lyapunov Function for Mobile Robot

Elsayed, Mohammed; Hammad, Abdallah; Hafez, Ashraf; Mansour, Hala M.

doi:10.5120/ijca2017914540

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…In [78], Flatness‐based Model Predictive Control is developed for AV trajectory tracking, where the high‐level predictive controller generates the desired global forces and moment, which are then allocated to four wheels’ independent steering and torque signals by a simple control allocation module. In [79], the authors integrate Lyapunov theory for the desired path‐tracking system, thereby ensuring the stability of the overall system.…”

Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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This abstract discusses the significant progress made in autonomous vehicles, focusing on decision‐making systems and control algorithms. It explores recent advances, challenges, and contributions in the field, emphasizing the need for precise navigation and control. The paper covers various methodologies, including rule‐based methods, machine learning, deep learning, probabilistic approaches, and hybrid approaches, examining their applications and effectiveness in ensuring safe navigation. Additionally, it reviews ongoing research efforts, emerging trends, and persistent challenges related to decision‐making and manoeuvre execution in autonomous vehicles, addressing complex topics such as sensor measurement uncertainty, dynamic environment modelling, real‐time responsiveness, and safe interactions with other road users. The objective is to provide a comprehensive overview of the state of the art in autonomous vehicle navigation and control for readers.

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Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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“…This nonlinear control is designed for automatic path tracking of straight or curved trajectories, and also it can be used to perform lane keeping and lane changing while avoiding obstacles. In [38], the authors integrate Lyapunov theory for the desired path-tracking system, thus guaranteeing global system stability. Model Predictive Control 'MPC' is designed for low-level control or cooperative driving applications.…”

Section: 21mentioning

confidence: 99%

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Abdallaoui

Ikaouassen

Kribèche

et al. 2023

Preprint

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As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models

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“…A model-based robot is an excellent approach to control a robot because it is easier to analyze its stability [16], [17]. However, it is hard to formulate the mathematical model representing all physical forces acting on the robot [18], [19]. The more manageable and less computation option is to model a robot using the kinematic models and ignore forces applied to the robot [20].…”

Section: Introductionmentioning

confidence: 99%

Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot

Auzan

Hujja

Fuadin

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Differential drive wheeled mobile robot (DDWMR) is one example of a robot with a constrained movement, Multiple Input Multiple Output (MIMO), and nonlinear system. Designing a low resource position and heading controller using linear MIMO methods such as LQR became a problem because of the linearization of robot dynamics at zero value. One of the solutions is to design a MIMO controller using a Single Input Single Output (SISO) controller. This work design a controller using PID for DDWMR Jetbot and selects the best feedback gain using different scenarios. The designed controller manipulates both motors by using calculated control signal to achieve a complex task such as path tracking with robot position in x-Axis, y-Axis, and heading angle as the feedback. The priority between position and heading angle can be adjusted by changing three feedback gains. The controller was tested, and the best gain was selected using Integral Absolute Error (IAE) metrics in a path tracking task with four different path shapes. The proposed methods can track square, circle, and two types of infinity shape paths, with the less well-formed shape being the four edges square path.

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Real Time Trajectory Tracking Controller based on Lyapunov Function for Mobile Robot

Cited by 5 publications

References 13 publications

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot

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