Design of Sliding Mode Based Differential Flatness Control of Leg-Wheel Hybrid Robot

Mauledoux, Mauricio; Mejía-Ruda, Edilberto; Sánchez, Oscar Fernando Avilés; Dutra, Max Suell; Arias, Alejandra Rojas

doi:10.4028/www.scientific.net/amm.835.681

Cited by 3 publications

(2 citation statements)

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“…As suggested by Mauledoux [38], to guarantee that the sliding surface σ r = 0 is attractive, we can enforce the dynamics of σ r as follows:…”

Section: Flatness-based Sliding Tracking Controlmentioning

confidence: 99%

Robust Tracking Control of Wheeled Mobile Robot Based on Differential Flatness and Sliding Active Disturbance Rejection Control: Simulations and Experiments

Abadi,

Ayeb,

Labbadi

et al. 2024

Sensors

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This paper proposes a robust tracking control method for wheeled mobile robot (WMR) against uncertainties, including wind disturbances and slipping. Through the application of the differential flatness methodology, the under-actuated WMR model is transformed into a linear canonical form, simplifying the design of a stabilizing feedback controller. To handle uncertainties from wheel slip and wind disturbances, the proposed feedback controller uses sliding mode control (SMC). However, increased uncertainties lead to chattering in the SMC approach due to higher control inputs. To mitigate this, a boundary layer around the switching surface is introduced, implementing a continuous control law to reduce chattering. Although increasing the boundary layer thickness reduces chattering, it may compromise the robustness achieved by SMC. To address this challenge, an active disturbance rejection control (ADRC) is integrated with boundary layer sliding mode control. ADRC estimates lumped uncertainties via an extended state observer and eliminates them within the feedback loop. This combined feedback control method aims to achieve practical control and robust tracking performance. Stability properties of the closed-loop system are established using the Lyapunov theory. Finally, simulations and experimental results are conducted to compare and evaluate the efficiency of the proposed robust tracking controller against other existing control methods.

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“…As suggested by Mauledoux [38], to guarantee that the sliding surface σ r = 0 is attractive, we can enforce the dynamics of σ r as follows:…”

Section: Flatness-based Sliding Tracking Controlmentioning

confidence: 99%

Robust Tracking Control of Wheeled Mobile Robot Based on Differential Flatness and Sliding Active Disturbance Rejection Control: Simulations and Experiments

Abadi,

Ayeb,

Labbadi

et al. 2024

Sensors

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show abstract

“…As suggested by mauledoux 23 , to guarantee that the sliding surface 𝜎 𝑟 = 0 is attractive, we can enforce the dynamics of 𝜎 𝑟 as follows :…”

Section: Proposed Robust Controller Via Flatness and Sliding Controlmentioning

confidence: 99%

Robust Tracking Control of Wheeled Mobile Robot Subject to Uncertainties

Abadi,

labbadi

2023

Preprint

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The paper suggests a compound control that combines nonlinear flatness, active disturbance rejection control (ADRC), and sliding mode control (SMC). By employing the differential flatness methodology, the standard under-actuated wheeled mobile robot model is converted into a fully actuated one. Utilizing this model as a basis, a sliding feedback controller is suggested to address the issue of uncertainties associated with wheel slip and wind. However, as the uncertainties increase, a higher control input is required, resulting in an undesired chattering phenomenon. To reduce chattering in SMC, a boundary layer surrounding the switching surface is employed, and a continuous law is implemented within the boundary. The boundary layer width plays an important role in improving robustness and eliminating chatter. Indeed, increasing the thickness of the boundary layer significantly reduces chattering, but it may lead to a loss of robustness performance achieved by the discontinuous control provided by SMC. To resolve this problem, active disturbance rejection control is combined with boundary layer sliding mode control. When utilizing the ADRC method, the lumped uncertainties are estimated via an extended state observer and eliminated within the feedback loop. The newly obtained feedback control combines the advantages of boundary layer SMC and ADRC to achieve practical control and robust tracking performance. The stability properties exhibited by the closed-loop system are rigorously established through the application of Lyapunov theory. In conclusion, a series of simulations has been conducted to compare and evaluate the efficiency of the presented robust tracking controller against other existing control methods.

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