Balancing and Trajectory Tracking of Two-Wheeled Mobile Robot Using Backstepping Sliding Mode Control: Design and Experiments

Esmaeili, Nasim; Alfi, Alireza; Khosravi, Hossein

doi:10.1007/s10846-017-0486-9

Cited by 89 publications

(47 citation statements)

References 27 publications

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“…22 Numerous consensus or formation protocols (or algorithms) have been proposed for MASs from various perspectives, with the analysis performed in the framework of Cartesian coordinates or polar coordinates. [23][24][25][26] In contrast to the Cartesian coordinates, the polar coordinate representation of NWMR is more often adopted to describe the collective rotating formation problem for a group of multi-agent dynamic systems, originating from many potential applications such as formation flight of satellites around the earth, circular mobile sensor networks, and spacecraft docking. 27,28 Due to the nonholonomic constraints, the design of cooperative tracking scheme for MNWMRs becomes more challenging than their single-robot counterparts in particular when the communication interactions among the networked robots are taken into account with an increase of group size.…”

Section: Introductionmentioning

confidence: 99%

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Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Liu

Guo

et al. 2020

Intl J Robust & Nonlinear

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This paper proposes a sliding-mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position-tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single-robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method. K E Y W O R D Smulti-nonholonomic mobile robots, polar coordinates, practical consensus tracking, sliding-mode control 1 Int J Robust Nonlinear Control. 2020;30:3831-3847.wileyonlinelibrary.com/journal/rnc

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Section: Introductionmentioning

confidence: 99%

“…17,18 (3) A systematic integration of two-step SMC procedures of position controller and heading direction controller is reasonably used to enhance the SMC performance including stability, robustness, and effectiveness. 17,24,31 The remainder of this paper is organized as follows. The preliminaries are presented in Section 2.…”

Section: Introductionmentioning

confidence: 99%

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Liu

Guo

et al. 2020

Intl J Robust & Nonlinear

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“…Due to the WMR is a typical nonholonomic system, most present researches on the tracking control schemes were base on assuming that the wheel rolling without considering slipping and skidding. A combined kinematic/torque controller was presented with the adaptive backstepping in [5], a finite-time tracking controller with output feedback was developed in [6], a data-based path tracking control algorithm was realized in [7,8], sliding-mode based algorithms [9][10][11] were presented to allow two different types of WMRs to track the reference path. [12][13][14][15] presented several other effective algorithms, such as the neural networks method [12], robust control scheme [13,14], and backstepping approach for path tracking control in [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Trajectory Tracking Control for WMR Considering Skidding and Slipping via Extended State Observer

Wang

Zhou

et al. 2019

Energies

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When the wheeled mobile robot (WMR) is required to perform specific tasks in complex environment, i.e., on the forestry, wet, icy ground or on the sharp corner, wheel skidding and slipping inevitably occur during trajectory tracking. To improve the trajectory tracking performance of WMR under unknown skidding and slipping condition, an adaptive sliding mode controller (ASMC) design approach based on the extended state observer (ESO) is presented. The skidding and slipping is regarded as external disturbance. In this paper, the ESO is introduced to estimate the lumped disturbance containing the unknown skidding and slipping, parameter variation, parameter uncertainties, etc. By designing a sliding surface based on the disturbance estimation, an adaptive sliding mode tracking control strategy is developed to attenuate the lumped disturbance. Simulation results show that higher precision tracking and better disturbance rejection of ESO-ASMC is realized for linear and circular trajectory than the ASMC scheme. Besides, experimental results indicate the ESO-ASMC scheme is feasible and effective. Therefore, ESO-ASMC scheme can enhance the energy efficiency for the differentially driven WMR under unknown skidding and slipping condition.

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“…However, a whole control sequence can be assessed according to objective mea-sures: for example, by the average speed, time for completing a lap in a race, or other appropriate criteria. This situation is the same for other control problems connected with robotics, including walking [9] and balancing [10] robots, as well as in many others [11]. In these problems, also usually exist some criteria for assessment (for example, time spent to pass the challenge), which would help to assess how desirable these control actions are.…”

Section: Introductionmentioning

confidence: 99%

Continuous Control With a Combination of Supervised and Reinforcement Learning

Kangin

Pugeault

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Reinforcement learning methods have recently achieved impressive results on a wide range of control problems. However, especially with complex inputs, they still require an extensive amount of training data in order to converge to a meaningful solution. This limits their applicability to complex input spaces such as video signals, and makes them impractical for use in complex real world problems, including many of those for video based control. Supervised learning, on the contrary, is capable of learning on a relatively limited number of samples, but relies on arbitrary hand-labelling of data rather than taskderived reward functions, and hence do not yield independent control policies. In this article we propose a novel, modelfree approach, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments. We use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands against millions or tens of millions) comparing to the state-of-theart reinforcement learning techniques on similar data, and at the same time overcomes both supervised and reinforcement learning approaches in terms of quality. Additionally, we demonstrate applicability of the method to MuJoCo control problems.

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Balancing and Trajectory Tracking of Two-Wheeled Mobile Robot Using Backstepping Sliding Mode Control: Design and Experiments

Cited by 89 publications

References 27 publications

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Adaptive Sliding Mode Trajectory Tracking Control for WMR Considering Skidding and Slipping via Extended State Observer

Continuous Control With a Combination of Supervised and Reinforcement Learning

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