Higher order sliding mode control of a parallel robot

Oumer, Ahmed Mehamed; Hunde, Andinet

doi:10.1109/afrcon.2015.7331962

Cited by 2 publications

(2 citation statements)

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“…However, the control quality is degraded if the system’s parametric uncertainties and noise components exist, leading to the explosion of terms’ phenomena. Another commonplace nonlinear feedback control method is sliding mode control (SMC) (Hoang and Vuong, 2018; Lv et al, 2017; Oumer and Hunde, 2015) because of its advantageous features, including fast dynamic response and robustness to external disturbances and uncertain parameters. Nevertheless, the chattering phenomenon generated by the SMC is always a concerning problem that potentially damages the control performance, especially in the hardware system.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the chattering phenomenon generated by the SMC is always a concerning problem that potentially damages the control performance, especially in the hardware system. Hence, higher-order SMC (Oumer and Hunde, 2015) is an upgraded approach that can attenuate chattering problems and guarantee finite-time convergence. Besides, the combination of backstepping and SMC is another strategy to enhance the performance by taking advantage of both controllers (Thi et al, 2019; Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

Mạnh

Nguyen

Duy

et al. 2022

Transactions of the Institute of Measurement and Control

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The paper proposes an adaptive Lyapunov-based nonlinear model predictive control (MPC) to cope with the problems in nonlinear systems subjecting to system constraints and unknown disturbances of the parallel car driving simulator. Commonly, standard nonlinear controllers could guarantee the overall system stability for the parallel structure. However, the constraints tend to impact the control performance and stability adversely. Therefore, MPC plays a vital role in the proposed technique to explicitly consider all the practical constraints and simultaneously enhance the system’s robustness. Nevertheless, the accuracy of the modeling process has a significant effect on the MPC performance, and thus, the convergence cannot be guaranteed in the presence of the model uncertainties. To overcome this problem, by the merit of the fuzzy adaptive law, the control system takes the disturbances and unmodelled parameters into account. Moreover, the feasibility and stability of the approach, which is the fundamental problem of MPC, are ensured according to the Lyapunov-based nonlinear controller, backstepping aggregated with sliding mode control (SMC), and hence inherit advantages of these controls. Simulation results show the efficiency and superior constituted controllers of the proposed method.

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Section: Introductionmentioning

confidence: 99%