The Application of Adaptive Backstepping Sliding Mode for Hybrid Humanoid Robot Arm Trajectory Tracking Control

Qin, Li; Liu, Fucai; Liang, Lihuan

doi:10.1155/2014/307985

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…P α is the position vector of Wolf , P β the position vector of Wolf and P δ the position vector of Wolf . The general formulas of A and are listed in (8) and ( 9) respectively.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

“…In the literature [7], a five degree of freedom hybrid robot model predictive trajectory tracking controller for milling is designed, and the control law of calculating torque is planned to improve the performance of trajectory tracking. In the literature [8], a novel hybrid humanoid robot arm is the controlled object, an sliding mode controller based on the adaptive backstepping approach is introduced under considering the parameter uncertainties and disturbances, and finally the superiority of the trajectory tracking controller is verified by experiments. In the literature [9], the adaptive controller based on the output is proposed for a Delta robot, which is applied to implement the trajectory tracking and is helpful to gain the fast tracking of the reference trajectory.…”

Section: Introductionmentioning

confidence: 99%

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Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Zhang¹,

Ming²

2021

IJCIS

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A dynamic Grey Wolf Optimizer (GWO) is proposed, noted as DGWO2, and a novel dynamic improved GWO algorithm is obtained after transferring the mutation operator and the eliminating-reconstructing mechanism to the DGWO2, noted as DMR-GWO2. A Type-2 fuzzy predictive compensation PID trajectory tracking controller based on the DMR-GWO2 is proposed to attain the tracking targets for the trajectory control of Par4 parallel robot. In the trajectory tracking controller, the DMR-GWO2 is designed for optimizing the Type-2 fuzzy logic controller which is in parallel with the PID controller to speed up the response of the parallel robot and to get the high adaptive capacity of the whole system. One input of the Type-2 fuzzy logic controller is the tracking error, while the other input is the sum of the derivative of the tracking error and the change rate of the expected angle. Finally, the experiments verify the effectiveness of the designed trajectory tracking controller and the tracking errors are small.

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“…P α is the position vector of Wolf , P β the position vector of Wolf and P δ the position vector of Wolf . The general formulas of A and are listed in (8) and ( 9) respectively.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Zhang¹,

Ming²

2021

IJCIS

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show abstract

“…Because of the difficulties in deriving an exact mathematical model and the complexity of traditional fuzzy controller, we have combined the single-input fuzzy logic controller and adaptive fuzzy sliding mode controller. 34,35 An assumption should be pointed out that the desired trajectory of every joint of the lower extremity exoskeleton has been inferred. The tracking error can be defined as follows…”

Section: Controller Designmentioning

confidence: 99%

Single-input adaptive fuzzy sliding mode control of the lower extremity exoskeleton based on human–robot interaction

Jin

Zhu

et al. 2017

Advances in Mechanical Engineering

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This article introduces a human–robot interaction controller toward the lower extremity exoskeleton whose aim is to improve the tracking performance and drive the exoskeleton to shadow the wearer with less interaction force. To acquire the motion intention of the wearer, two subsystems are designed: the first is to infer the wearer is in which phase based on floor reaction force detected by a multi-sensor system installed in the sole, and the second is to infer the motion velocity based on the multi-axis force sensor and admittance model. An improved single-input fuzzy sliding mode controller is designed, and the adaptive switching controller is combined to promote the tracking performance considering system uncertainties. Adaptation laws are designed based on the Lyapunov stability theorem. Therefore, the stability of the single-input adaptive fuzzy sliding mode control can be guaranteed. Finally, the proposed methods are applied to the lower extremity exoskeleton, especially in the swing phase. Its effectiveness is validated by comparative experiments.

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“…This architecture implies different challenges in relation to system interconnection, event synchronization, related control loops, and fault diagnosis, among others [ 15 , 16 , 17 ]. Specifically, the use of closed-loop control methodologies that consider the non-linear dynamics of the system at its different operating points is required [ 17 , 18 , 19 ]. In this sense, the coordinated movements entail having to make use of controllers capable of understanding the dynamic of these system and sampling periods that can be variable [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…In other studies [ 25 , 26 ], the authors proposed slide mode control for robot path tracking, first for a quadrocopter and then for controlling the torque in each joint of the robot in order that the angle coordinates of each link coincide with the desired values. In [ 19 ], a Backstepping control proposal is presented for a robotic arm with satisfactory results in trajectory tracking obtained in simulation.…”

Section: Introductionmentioning

confidence: 99%

Non Linear Control System for Humanoid Robot to Perform Body Language Movements

Gomez-Quispe

Pérez-Zúñiga

Arce

et al. 2023

Sensors

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In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements.

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The Application of Adaptive Backstepping Sliding Mode for Hybrid Humanoid Robot Arm Trajectory Tracking Control

Cited by 11 publications

References 10 publications

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Single-input adaptive fuzzy sliding mode control of the lower extremity exoskeleton based on human–robot interaction

Non Linear Control System for Humanoid Robot to Perform Body Language Movements

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