Sliding Mode Control with Super-Twisting Algorithm for Surge Oscillation of Mooring Vessel System

Lee, Sang‐Do; Lee, Bo-Kyeong; You, Sam–Sang

doi:10.7837/kosomes.2018.24.7.953

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…where U is a positive constant, which should be sufficiently large to assure good tracking performance. The term u2 = −1.1Utanh(σ) describes the leakage of the "super-twisting" second-order sliding controller [40]. In addition, the tanh(σ) is used instead of sign(σ) because it creates a smoother control signal.…”

Section: Controller Designmentioning

confidence: 99%

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

2022

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Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).

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Section: Controller Designmentioning

confidence: 99%

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

2022

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“…The SMC is based on variable structure control: the structure of the control system is changed, and the sliding mode is realized on the designed sliding plane (or line). Because the discontinuous control input of the SMC is independent of the system parameters of the nominal model, this control method is robust against model uncertainties and disturbances [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Robust Temperature Control of a Variable-Speed Refrigeration System Based on Sliding Mode Control with Optimal Parameters Derived Using the Genetic Algorithm

Lee

Jeong

2021

Energies

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A sliding mode control (SMC) technique based on a state observer with a Kalman filter and feedforward controller was established for a variable-speed refrigeration system (VSRS) to ensure robust control against model uncertainties and disturbances, including noise. The SMC was designed using a state-space model transformed from a practical transfer function model, which was derived by conducting dynamic characteristic experiments. Fewer parameters affecting the model uncertainty were required to be identified, which facilitated modeling. The state observer for estimating the state variables was designed using a Kalman filter to ensure robustness against noise. A feedforward controller was added to the control system to compensate for the deterioration in the transient characteristics due to the saturation function used to avoid chattering. A genetic algorithm was used to alleviate the trial and error involved in determining the design parameters of the saturation function and select optimal values. Simulations and experiments were conducted to verify the control performance of the proposed SMC. The results show that the proposed controller can realize robust temperature control for a VSRS despite stepwise changes in the reference and external heat load, and avoid the trial and error process to design parameters for the saturation function.

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“…Since the goal of the orthosis is to assist in the execution of the elbow bending movement, it is then intended to minimize the angular error ( θ) in order to ensure the desired movement (θ d ). θ = θ d − θ e As in [193], [194], [195] and [196], one can define a sliding surface (σ) of the type:…”

Section: Control Requirements Parameters and Objectivesmentioning

confidence: 99%

“…To ensure that the controller works properly, the stability of the system as a whole must be proven, including the introduction of the controller. So, as described in [193], [194], [195] , [197] and [196], it is sufficient to find a Lyapunov function that can show the stability of the controller. From [198], [199] and [200] we can write:…”

Section: Stability Proofmentioning

confidence: 99%

Development, Construction, Analysis and Control of Upper Limb Orthosis Using Bio-Signals

BARBOSA¹

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Sliding Mode Control with Super-Twisting Algorithm for Surge Oscillation of Mooring Vessel System

Cited by 8 publications

References 15 publications

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

Robust Temperature Control of a Variable-Speed Refrigeration System Based on Sliding Mode Control with Optimal Parameters Derived Using the Genetic Algorithm

Development, Construction, Analysis and Control of Upper Limb Orthosis Using Bio-Signals

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