State of the Art in Predictive Control of Wheeled Mobile Robots

Harasim, Patryk; Trojnacki, Maciej

doi:10.14313/jamris_1-2016/5

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…To overcome the above-mentioned problems, one can consider the model-based predictive control (MPC). It is one of the advanced techniques that now has a huge impact on the development of control systems and on research in feedback control areas and has achieved remarkable success in the practical field [20], [21]. This success of the MPC is attributed to many reasons.…”

Section: Introductionmentioning

confidence: 99%

Enhanced MPC for Omnidirectional Robot Motion Tracking Using Laguerre Functions and Non-Iterative Linearization

El-Sayyah

Saad

2022

IEEE Access

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To cope with the computational complexity of the traditional model predictive control, and to reduce the error of the linearization and prediction processes, this paper presents an improved model predictive control algorithm, based on Laguerre functions, for the motion tracking of an omnidirectional mobile robot with non-iterative linearization. To design the controller, the kinematic modeling of the threewheeled omnidirectional robot was first performed. Next, the model predictive algorithm was developed using Laguerre functions to parametrize the control signals. At each sampling instant of the online optimization, a linearization along the predicted trajectory, based on the duality principle between optimal control and stochastic filtering, was carried out to deal with the nonlinearities of the system. This non-iterative linearization provides better approximation of the nonlinear behavior which improves the prediction process and the tracking performance, with lower computational burden due to the use of the Laguerre functions. The new controller is applied to solve the trajectory-tracking problem of an omnidirectional robot. A comparative study between the proposed controller, the conventional model predictive control, and the nonlinear model predictive approach is made. Simulation results confirm that the new controller outperform the latter ones regarding tracking accuracy with considerably low computational effort. The feasibility of the controller is demonstrated by real-time experiment on the Robotino-Festo omnidirectional mobile robot.

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Section: Introductionmentioning

confidence: 99%

Enhanced MPC for Omnidirectional Robot Motion Tracking Using Laguerre Functions and Non-Iterative Linearization

El-Sayyah

Saad

2022

IEEE Access

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“…In the literature, there are many control algorithms applied to autonomous vehicles and mobile robots. There has been a long history in solving significant problems in this field, for example, autonomous path tracking [7][8][9], obstacle avoidance [10][11][12], control for mobile robots [13][14][15], and steering control [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Fuzzy Heading Control for a Multi-Wheeled Combat Vehicle

Ouda

Mohamed

2021

IJRCS

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This paper introduces the design and the implementation of a heading angle tracking controller using fuzzy logic for a scaled Autonomous Multi-Wheeled Combat Vehicle (AMWCV) to navigate in outdoor environments. The challenge of designing this control system is to control the steering of the front four wheels individually to obtain the correct heading angle of the vehicle. The main contribution of the paper can be summarized in the fact that it is designing four fuzzy controllers for navigation and tracking the desired heading angle while at the same time while controlling the steering of the front four wheels individually. The AMWCV is capable of forwarding and backward movement where all eight wheels are powered individually. The different heading angles are used and simulated using MATLAB software to evaluate the performances of the developed algorithms. In addition, the performance of the developed controllers is validated in the presence of noise and disturbance in order to evaluate the robustness of the controller's Simulation results show the performances and demonstrate that the developed controllers are effective in predicting the desired heading angle changes.

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