Self-tuning parameter fuzzy PID controller for autonomous differential drive mobile robot

Heikkinen, Jani; Minav, Tatiana; Stotckaia, Anastasiia D.

doi:10.1109/scm.2017.7970592

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…A differential drive wheeled mobile robot is usually composed of two independently controlled wheels and an additional wheel (or caster) for balance purposes. The direction of movement, speed, and orientation of the robot is defined by the speed ratios between its wheels [14]. Figure 1 is a schematic of a differential drive wheeled mobile robot in an inertial frame, where 𝑋 𝐼 and 𝑌 𝐼 represent the inertial (or global) frame axes and 𝑋 𝑅 and 𝑌 𝑅 represent the robot frame (or local frame) axes.…”

Section: A Kinematic Model Of Differential Mobile Robotmentioning

confidence: 99%

Control of mobile robot formations using A-star algorithm and artificial potential fields

Manuel

Inanc

Erten

2021

J. Mechatron. Electr. Power Veh. Technol.

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Formations or groups of robots become essential in cases where a single robot is insufficient to satisfy a given task. With an increasingly automated world, studies on various topics related to robotics have been carried out in both the industrial and academic arenas. In this paper, the control of the formation of differential mobile robots based on the leader-follower approach is presented. The leader's movement is based on the least cost path obtained by the A-star algorithm, thus ensuring a safe and shortest possible route for the leader. Follower robots track the leader's position in real time. Based on this information and the desired distance and angle values, the leader robot is followed. To ensure that the followers do not collide with each other and with the obstacles in the environment, a controller based on Artificial Potential Fields is designed. Stability analysis using Lyapunov theory is performed on the linearized model of the system. To verify the implemented technique, a simulator was designed using the MATLAB programming language. Seven experiments are conducted under different conditions to show the performance of the approach. The distance and orientation errors are less than 0.1 meters and 0.1 radians, respectively. Overall, mobile robots are able to reach the goal position, maintaining the desired formation, in finite time.

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Section: A Kinematic Model Of Differential Mobile Robotmentioning

confidence: 99%

Control of mobile robot formations using A-star algorithm and artificial potential fields

Manuel

Inanc

Erten

2021

J. Mechatron. Electr. Power Veh. Technol.

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“…The combination of maneuverability, cost-effectiveness, and ease of integration positions differential drive mobile robots as a versatile and efficient solution in the ever-evolving landscape of robotics and automation. In order to control the performance variables of such robotic vehicles, several control approaches have been proposed, including, among many, PID type of controllers (see [10][11][12]), linear static state feedback controllers (see [5]), optimal type of controllers (see [13][14][15][16]), predictive controllers (see [17,18]), and fuzzy (see [19,20]) and adaptive controllers (see [21,22]).…”

Section: Introductionmentioning

confidence: 99%

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

Kouvakas,

Koumboulis,

Sigalas

2024

Robotics

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Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior.

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“…In particular, trajectory tracking of WMRs have been widely addressed (Mac Thi et al, 2016;Heikkinen et al, 2017;Hou et al, 2009). In Mac Thi et al (2016), a MIMO fuzzy controller was proposed for path tracking of autonomous WMRs, Heikkinen et al (2017) have been concerned with fuzzy-PID controllers for DDMRs and adaptive fuzzy-based control was addressed by Hou et al (2009). One of the most common class of fuzzy controller is the Mamdani-type Fuzzy Logic Controller (FLC).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Maneuvering Controller Optimization Using an Optimum Differential-Drive Mobile Robot Model

Ushikoshi

Carneiro

Coutinho

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This paper proposes a method for improving the performance of a fuzzy controller applied to a Differential-Drive Mobile Robot. From previously recorded data, the DDMR model is improved through Particle Swarm Optimization aiming to obtain a better representation of the real system. The PSO algorithm is also applied to adjust the fuzzy controller parameters so that trajectory tracking error is minimized. The manually adjusted controller settings are compared to the optimized's in terms of Root Mean Square Error of trajectory tracking, control effort and acceleration. The latter is useful for protecting the robot from damage caused by abrupt variations. Numerical simulations show that the optimized model can describe better the real system behavior and the optimized controller can lead the robot dynamic model to track the given trajectory more accurately, with reduced control effort, and smoother velocity variation than the non-optimized controller.

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Self-tuning parameter fuzzy PID controller for autonomous differential drive mobile robot

Cited by 11 publications

References 3 publications

Control of mobile robot formations using A-star algorithm and artificial potential fields

Control of mobile robot formations using A-star algorithm and artificial potential fields

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

Fuzzy Maneuvering Controller Optimization Using an Optimum Differential-Drive Mobile Robot Model

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