A novel iterative learning path-tracking control for nonholonomic mobile robots against initial shifts

Zhao, Yang; Zhou, Fengyu; Li, Yan; Wang, Yugang

doi:10.1177/1729881417710634

Cited by 15 publications

(15 citation statements)

References 31 publications

(43 reference statements)

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“…Lui et al in [25] proposed a robust teleoperation control for two slave DDWMRs which cooperatively grasp and transport a deformable object while an operator, at the master site, receives visuo-haptic feedback. Zhao et al in [26] designed a robust iterative learning control that exploits feedback-aided P-type learning terms to enhance the stability of the system when initial shift is considered. Rao et al in [27] developed a control that combines fuzzy logic, neural network, and adaptive neuro-fuzzy inference system techniques with an integrated safe boundary algorithm to solve the tracking task.…”

Section: Introductionmentioning

confidence: 99%

“…The previous literature shows that, in general, the control design for the trajectory tracking task in DDWMRs has been tackled from five directions linked to the kinematics/dynamics of the mechanical structure: (1) by considering only the kinematics of the mechanical structure [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37], (2) by taking into account the kinematics of the mechanical structure and the dynamics of the actuators [38,39,40], (3) by using the kinematic model of the mechanical structure along with the dynamics of the actuators and power stage [5,6,7], (4) by using only the dynamics of the mechanical structure [41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59], and (5) by considering the dynamics of the mechanical structure and the actuators [60,61]. Considering the aforementioned perspectives, the present paper is particularly motivated by (3), that is, when the mathematical models of the three subsystems composing a DDWMR are used in control design.…”

Section: Introductionmentioning

confidence: 99%

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Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers

García-Sánchez

Tavera-Mosqueda

Silva‐Ortigoza

et al. 2018

Sensors

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By using the hierarchical controller approach, a new solution for the control problem related to trajectory tracking in a differential drive wheeled mobile robot (DDWMR) is presented in this paper. For this aim, the dynamics of the three subsystems composing a DDWMR, i.e., the mechanical structure (differential drive type), the actuators (DC motors), and the power stage (DC/DC Buck power converters), are taken into account. The proposed hierarchical switched controller has three levels: the high level corresponds to a kinematic control for the mechanical structure; the medium level includes two controls based on differential flatness for the actuators; and the low level is linked to two cascade switched controls based on sliding modes and PI control for the power stage. The hierarchical switched controller was experimentally implemented on a DDWMR prototype via MATLAB-Simulink along with a DS1104 board. With the intention of assessing the performance of the switched controller, experimental results associated with a hierarchical average controller recently reported in literature are also presented here. The experimental results show the robustness of both controllers when parametric uncertainties are applied. However, the performance achieved with the switched controller introduced in the present paper is better than, or at least similar to, performance achieved with the average controller reported in literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers

García-Sánchez

Tavera-Mosqueda

Silva‐Ortigoza

et al. 2018

Sensors

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“…Due to their simple and reliable propulsion mechanism, DDMRs are adopted in almost all research fields: pathtracking, [5][6][7][8][9] trajectory-planning, [10][11][12][13][14] position-estimation, 15,16 navigation control [17][18][19] and multi-robot control. [20][21][22] Robot models are the indispensable basis for the various applications mentioned earlier.…”

Section: Introductionmentioning

confidence: 99%

“…Generally speaking, kinematics models are more popular in applications, not only for estimation 15,16 and planning, 11,13,14 but also for control. 8,17,18,20 Most kinematics models involve differential equations due to the nonholonomic constraints of DDMRs, 7,14,15,19,22 while some kinematics models are based on geometric relations that rely on the instantaneous centre of curvature or trajectory approximation at each sampling instant. [16][17][18]20 Besides the robot kinematics, it is important to consider the robot dynamics when highspeed movement and/or heavy-load operations are required in realistic work fields.…”

Section: Introductionmentioning

confidence: 99%

Steering-angle computation for the multibody modelling of differential-driving mobile robots with a caster

Ángeles²,

Zou

et al. 2018

International Journal of Advanced Robotic Systems

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Since many off-the-shelf motor drives are supplied with complete control capability in the current, velocity and position loop, the robot model in the navigation control architecture can be oriented either to kinematics, interfaced with the velocity loop, or to dynamics, with the motor-current loop. Moreover, no constraints are imposed by a caster on the mobility of differential-driving mobile robots. Hence, a reduced model, containing only the platform, is sufficient for navigation control based only on the robot kinematics. However, if the multibody system model is used for navigation control based on the robot dynamics, to cope with the demands of high-speed manoeuvres and/or heavy-load operations, then the caster kinematics, especially the knowledge of the steering angle, is required to calculate the inertia matrix and the terms of Coriolis and centrifugal forces. While this angle can be measured by means of dedicated encoders to be installed for casters, the computation technique based on the existing tachometers, already mounted on the motor shafts for the servo control of the two driving wheels, is proved to be sufficient. Both a thorough kinematics model and a multibody dynamics model, including the platform and all different wheels, are formulated here for differential-driving mobile robots. Computational methods based on velocity compatibility and rigid body twists are proposed to estimate the steering angle. Simulation results of the differential-driving mobile robot moving on a smooth trajectory show the feasibility of the steering-angle computational scheme, which obviates the need of installing caster encoders. Moreover, a performance comparison on system modelling is implemented via simulation, between the differential-driving mobile robot model with and without caster dynamics. This further validates the importance of the dynamic effects of casters on the whole system model. Therefore, the multibody modelling approach for casters with the steering-angle computation technique can facilitate the navigation control architecture under dynamics conditions.

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“…The basic idea of these algorithms is based on the parameters measured by the sensor and on drawing of an environmental map around the mobile robot, with the aim of avoiding a collision between the robot and the wall object in its path. 4 Zhao et al 20 describe the novel iterative learning path-tracking control for non-holonomic mobile robots against initial shifts. Matik and Uricek 8 and others describe approach called simultaneous localization and mapping (SLAM) based on simultaneous localization and mapping.…”

Section: Introductionmentioning

confidence: 99%

Development of simulation software for mobile robot path planning within multilayer map system based on metric and topological maps

Kuric

Bulej

Sága

et al. 2017

International Journal of Advanced Robotic Systems

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This article deals with methods of navigation and mapping of mobile robots in an indoor environment, for example, laboratories, building corridors, and so on. It explains the proposed solution of global navigation in more detail through the application of potential field method and its transformation into the topological map. Two separate software tools were designed for simulation of wheeled mobile robot behavior in the authors' workplace. The first software uses the metric form of space representation and it can simulate tactic level of global navigation, while the second one deals with its transformation into the topological map and can be used for strategic level of global navigation. This is how we can get the so-called multilayer map system suitable for different tasks of mobile robot navigation and path planning.

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A novel iterative learning path-tracking control for nonholonomic mobile robots against initial shifts

Cited by 15 publications

References 31 publications

Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers

Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers

Steering-angle computation for the multibody modelling of differential-driving mobile robots with a caster

Development of simulation software for mobile robot path planning within multilayer map system based on metric and topological maps

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