Nonlinear Control of Wheeled Mobile Robots

Dixon, Warren E.; Dawson, D.M.; Zergeroğlu, Erkan; Behal, Aman

doi:10.1007/bfb0113116

Cited by 66 publications

(4 citation statements)

References 17 publications

(25 reference statements)

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“…Definitions for parameters of wheeled mobile robot are shown in Table 1. Assuming the nonholonomic constraint and no slipping, dynamic equation is given by follows [9].…”

Section: Wheeled Mobile Robot Model Object and Tracking Error Equationmentioning

confidence: 99%

“…Then, the tracking error equation is given by the following equations [9]. If ( ) u t can be used as the control input, the tracking error system Eq.8 can be stabilized by using the following controller [9].…”

Section: Wheeled Mobile Robot Model Object and Tracking Error Equationmentioning

confidence: 99%

“…Especially for the velocity measurement, the required accuracy may not be achieved in practical applications due to the existence of noises. The control schemes without velocity measurement have been proposed [9][10][11]. These controllers require some accurate knowledge of robot parameters such as the position of gravity center and radius of wheel.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wheeled Mobile Robot Tracking Control without Velocity Measurement

Wang

Tong

Xing

2013

AMM

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In this paper, a new robust trajectory tracking control scheme for wheeled mobile robots without velocity measurement is proposed. In the proposed controller, the velocity observer is used to estimate the velocity of wheeled mobile robot. The dynamics of wheeled mobile robot is considered to develop the controller. The proposed controller has the following features: i) The proposed controller has good robustness performance; ii) It is easy to improve tracking performance by setting only one design parameters.

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“…Definitions for parameters of wheeled mobile robot are shown in Table 1. Assuming the nonholonomic constraint and no slipping, dynamic equation is given by follows [9].…”

Section: Wheeled Mobile Robot Model Object and Tracking Error Equationmentioning

confidence: 99%

Section: Wheeled Mobile Robot Model Object and Tracking Error Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wheeled Mobile Robot Tracking Control without Velocity Measurement

Wang

Tong

Xing

2013

AMM

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“…In addition to the unique tasks that each mobile robot is intended for [1]- [7], dynamic control problems and path planning are critical when autonomous navigation is being considered. Planning and control problems are frequently dealt separately in the literature using multi-layer schemes [8]- [13]. If the connection between the layers is not handled properly in certain circumstances, the total output might show performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot

Ismael,

Almaged,

Abdulla

2024

Journal of Robotics and Control

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This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors. Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles.

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