Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

DEVELOPMENT AND CONTROL OF AN UNDERACTUATED TWO-WHEELED MOBILE ROBOT

Ph.DDOCTOR OF PHILOSOPH

Control of a class of multibody underactuated mechanical systems with discontinuous friction using sliding-mode

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This paper studies sliding-mode control of a class of multibody underactuated systems with discontinuous friction presenting on the unactuated configuration variable with consideration of parametric uncertainties. Global motion for this class system including sticking, stick-slip, and slip regimes are analyzed, and their corresponding equilibria are identified. Our control objective is to avoid the sticking and stick-slip regimes while track a desired velocity in the slip regime. The proposed sliding-mode controllers are robust to parametric uncertainties, and their stabilities are proved by using the Lyapunov direct method. Two examples, a mass-spring-damping system and a drill-string system, are used to demonstrate the validity of the proposed controllers.The author(s) received no financial support for the research, authorship, and/or publication of this articl

Adaptive sliding mode control for uncertain wheel mobile robot

In this paper a simple adaptive sliding mode controller is proposed for tracking control of the wheel mobile robot (WMR) systems. The WMR are complicated systems with kinematic and dynamic model so the error dynamic model is built to simplify the mathematical model. The sliding mode control then is designed for this error model with the adaptive law to compensate for the mismatched. The proposed control scheme in this work contains only one control loop so it is simple in both implementation and mathematical calculation. Moreover, the requirement of upper bounds of disturbance that is popular in the sliding mode control is cancelled, so it is convenient for real world applications. Finally, the effectiveness of the presented algorithm is verified through mathematical proof and simulations. The comparison with the existing work is also executed to evaluate the correction of the introduced adaptive sliding mode controller. Thoroughly, the settling time, the peak value, the integral square error of the proposed control scheme reduced about 50% in comparison with the compared disturbance observer based sliding mode control

Modeling and Control Strategies for a Two-Wheel Balancing Mobile Robot

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic model for the mobile robot. The robot dynamics is derived using the classical Euler-Lagrange method, where motion can be described using potential and kinetic energies of the bodies. Non-holonomic constraints are included in the model to achieve desired motion, such as non-drifting of the mobile robot. These non-holonomic constraints are included using the method of Lagrange multipliers. Navigation for the robot is developed using artificial potential field path planning to generate a map of velocity vectors that are used for the set points for linear velocity and yaw rate. The second part of the thesis focuses on developing and evaluating three different control strategies for the mobile robot: PID controllers, Hierarchical Sliding Mode Control, and Deep-Q-Learning. The performances of the different control strategies are evaluated and compared based on various metrics, such as stability, robustness to mass variations and disturbances, and tracking accuracy. The implementation and evaluation of these strategies are modeled tested in a MATLAB/SIMULINK virtual environment

Modeling and Control Strategies for a Two-Wheel Balancing Mobile Robot

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic model for the mobile robot. The robot dynamics is derived using the classical Euler-Lagrange method, where motion can be described using potential and kinetic energies of the bodies. Non-holonomic constraints are included in the model to achieve desired motion, such as non-drifting of the mobile robot. These non-holonomic constraints are included using the method of Lagrange multipliers. Navigation for the robot is developed using artificial potential field path planning to generate a map of velocity vectors that are used for the set points for linear velocity and yaw rate. The second part of the thesis focuses on developing and evaluating three different control strategies for the mobile robot: PID controllers, Hierarchical Sliding Mode Control, and Deep-Q-Learning. The performances of the different control strategies are evaluated and compared based on various metrics, such as stability, robustness to mass variations and disturbances, and tracking accuracy. The implementation and evaluation of these strategies are modeled tested in a MATLAB/SIMULINK virtual environment

Unmanned Robotic Systems and Applications

This book presents recent studies of unmanned robotic systems and their applications. With its five chapters, the book brings together important contributions from renowned international researchers. Unmanned autonomous robots are ideal candidates for applications such as rescue missions, especially in areas that are difficult to access. Swarm robotics (multiple robots working together) is another exciting application of the unmanned robotics systems, for example, coordinated search by an interconnected group of moving robots for the purpose of finding a source of hazardous emissions. These robots can behave like individuals working in a group without a centralized control

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PD

Robust control of underactuated wheeled mobile manipulators using GPI disturbance observers

This article describes the design of a linear observer–linear controller-based robust output feedback scheme for output reference trajectory tracking tasks in the case of nonlinear, multivariable, nonholonomic underactuated mobile manipulators. The proposed linear feedback scheme is based on the use of a classical linear feedback controller and suitably extended, high-gain, linear Generalized Proportional Integral (GPI) observers, thus aiding the linear feedback controllers to provide an accurate simultaneous estimation of each flat output associated phase variables and of the exogenous and perturbation inputs. This information is used in the proposed feedback controller in (a) approximate, yet close, cancelations, as lumped unstructured time-varying terms, of the influence of the highly coupled nonlinearities, and (b) the devising of proper linear output feedback control laws based on the approximate estimates of the string of phase variables associated with the flat outputs simultaneously provided by the disturbance observers. Simulations reveal the effectiveness of the proposed approach