Dynamic modeling and sliding mode control of a wheeled mobile robot assuming lateral and longitudinal slip of wheels

Ghobadi, Narges; Dehkordi, S. F.

doi:10.1109/icrom48714.2019.9071913

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…A time invariant discontinuous feedback law is applied for regulation control and a sliding mode control is proposed for turning control. A dynamic model of a WMR under slip conditions has been developed and controlled with the sliding mode controller in Ghobadi and Dehkordi (2019). In this model, longitudinal and lateral traction forces are estimated linearly as a function of longitudinal and lateral slip.…”

Section: Introductionmentioning

confidence: 99%

“…But, under real conditions, the wheel slip is inevitable because the WMR always slips on a slippery surface (wet or icy roads and rough terrain) and at high speed (Tian and Sarkar, 2014). Thus, modeling mobile robots without slip leads to results with insufficient accuracy (Ghobadi and Dehkordi, 2019). So, it is necessary to use controllers to drive the WMR to a desired trajectory and to ensure efficient performance considering the wheels’ slipping.…”

Section: Introductionmentioning

confidence: 99%

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Multibody dynamics modeling and simulation of a three-wheeled mobile robot using a robotic approach

Khadr

2024

IJIUS

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PurposeThis paper focuses on the application of a robotic technique for modeling a three-wheeled mobile robot (WMR), considering it as a multibody polyarticulated system. Then the dynamic behavior of the developed model is verified using a physical model obtained by Simscape Multibody.Design/methodology/approachFirstly, a geometric model is developed using the modified Denavit–Hartenberg method. Then the dynamic model is derived using the algorithm of Newton–Euler. The developed model is performed for a three-wheeled differentially driven robot, which incorporates the slippage of wheels by including the Kiencke tire model to take into account the interaction of wheels with the ground. For the physical model, the mobile robot is designed using Solidworks. Then it is exported to Matlab using Simscape Multibody. The control of the WMR for both models is realized using Matlab/Simulink and aims to ensure efficient tracking of the desired trajectory.FindingsSimulation results show a good similarity between the two models and verify both longitudinal and lateral behaviors of the WMR. This demonstrates the effectiveness of the developed model using the robotic approach and proves that it is sufficiently precise for the design of control schemes.Originality/valueThe motivation to adopt this robotic approach compared to conventional methods is the fact that it makes it possible to obtain models with a reduced number of operations. Furthermore, it allows the facility of implementation by numerical or symbolical programming. This work serves as a reference link for extending this methodology to other types of mobile robots.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multibody dynamics modeling and simulation of a three-wheeled mobile robot using a robotic approach

Khadr

2024

IJIUS

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“…In [15], the authors proposed a brake control system to prevent lateral skidding of commercial aircraft wheels using the backstepping method. In [16], Sidek and his colleagues developed an input-output linearized controller to represent the relationship between the torque at the actuator and the traction function of wheel slippage. Researching in [17,18], the authors treated wheel slippage as a bounded disturbance affecting the control system state.…”

Section: Introductionmentioning

confidence: 99%

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheel Slip for Mobile Robot

Hà,

Thuong,

Thanh

et al. 2023

Preprint

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In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

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“…Moreover, the difference in slip may occur due to vehicle being turned or because of different road conditions for different wheels [3]. When a vehicle is traveling at high speeds and turns, its outer and inner wheels rotate at various speeds, which must be controlled by a controller to achieve a desired turning radius without losing its stability.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization Self-adaptive AI Controller for a Four-wheel Independent Drive Electric Rover

Jayetileke¹,

Mel²,

Ratnayake³

2021

Adv. Technol.

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In this paper, a dynamic optimization self-adaptive controller for a four-wheel independent drive electric rover has been investigated to enhance the dynamic stability. The proposed self-adaptive AI controller is based on dynamic Fuzzy Logic (FL) control mechanism. The dynamic self-adaptive properties have been integrated into the proposed FL controller through a dynamically tuned Particle Swarm Optimization (PSO) mechanism. Nevertheless, the dynamic FL controller and the dynamic PSO mechanism has been synchronized together for every sampling instance k to obtain the optimum performance of the electric rover. In this electric rover, all the four wheels have a fixed orientation and each wheel powered by a 250-Watt Brushless Direct Current (BLDC) motor through separate gear ratio mechanisms to obtain the desired torque and angular velocity. Therefore, the steering mechanism was achieved in this rover through the proposed AI controller, which was based on the differential speed mechanism. However, this paper presents the control methodology and obtained test results related to straight road tests under different slippery road conditions. The rover test results show that on different slippery road conditions the proposed PSO based FL controller has maintained the wheel slip ratio of all the four wheels which was less than 0.35 approximately. Here, the translational speed has been limited to 40 km/hr approximately within its recorded top speed of 90 km/hr while maintaining the desired fix orientation.

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Dynamic modeling and sliding mode control of a wheeled mobile robot assuming lateral and longitudinal slip of wheels

Cited by 11 publications

References 13 publications

Multibody dynamics modeling and simulation of a three-wheeled mobile robot using a robotic approach

Multibody dynamics modeling and simulation of a three-wheeled mobile robot using a robotic approach

Research on Some Control Algorithms to Compensate for the Negative Effects of Model Uncertainty Parameters, External Interference, and Wheel Slip for Mobile Robot

Dynamic Optimization Self-adaptive AI Controller for a Four-wheel Independent Drive Electric Rover

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