Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

Ren, Bin; Luo, Xurong; Chen, Jiayu

doi:10.3390/app9112251

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…Hence, it is not suitable to achieve excellent control performance by applying traditional control methods [20]. In [21] , the adaptive iterative learning control (AILC) method is proposed , which can effectively reduce such error without sacrificing the iteration efficiency. In [22], a model-free control method is presented to estimate the knee joint angles of the exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Wang

Song

2019

Applied Sciences

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Aiming at a pneumatic artificial muscle (PAM) lower extremity exoskeleton, a control mechanism based on hybrid phase sliding mode control (SMC) is proposed. First of all, the human gait cycle is mainly divided into the swing phase and stance phase, and the lower extremity exoskeleton phase models are established by the Euler–Lagrange method, respectively. Secondly, the lower limb exoskeleton is inevitably affected in the diverse working environment, and the exoskeleton model has nonlinear and strong coupling characteristics, which both increase the control difficulty. In this situations, a robust sliding mode control method is designed based on an Extended State Observer (ESO). Thirdly, the pneumatic muscle takes time to contract and relax, and then the joint input torque cannot jump when the gait phase changes, hence, the smoothing switching of the assistive control scheme is introduced to solve it. The smoothing switching time is detected by a phase detector, and the phase detector is designed by the plantar pressure information. Finally the comparative simulation shows that this control strategy has the advantages of fast time, high control precision and no jump during control torque switching. Pneumatic artificial muscle contraction rate curve shows that the pneumatic muscles’ motion range meets the control requirement of the exoskeleton.

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Section: Introductionmentioning

confidence: 99%

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Wang

Song

2019

Applied Sciences

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“…Extending previously developed gait generating and optimizing algorithms, this study incorporates gait features as musculoskeletal models to develop controllers and calibrate controller parameters (Ren, Liu, et al. 2019; Ren, Luo, et al., 2019). Figure 1 shows a gait cycle taking the right foot as the reference.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Longman (2000) proposed an iterative learning algorithm with a repetitive moving pattern to reduce tracking errors; Bouakrif (2011) proposed an iterative learning algorithm with the forgetting factor, which can ignore uncertainties and adjust the control signal throughout the movement with Lyapunov‐like positive definite sequence; and Ren, Luo, et al. (2019) developed a gait‐based iterative learning controller for the single‐leg LLEs and reported that the iterative learning algorithm is suitable for developing agile mechanical systems.…”

Section: Introductionmentioning

confidence: 99%

Gait trajectory‐based interactive controller for lower limb exoskeletons for construction workers

Ren¹,

Luo²,

et al. 2021

Computer aided Civil Eng

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Lower limb exoskeletons (LLEs) are sets of mechanical devices used to support the action of human lower limbs. This recently developed technology has unprecedented potential in the construction industry by increasing the strength, endurance, and other physical capabilities of construction workers. For safety considerations, LLEs need reliable and responsive controllers to closely match their mechanical operation with human gait in a synchronous manner. This research proposes the use of physical human–robot interactive (pHRI) controllers that are suitable for construction tasks. The proposed pHRI integrates a gait trajectory‐based musculoskeletal model with iterative control algorithms. To minimize the trajectory tracking error between LLEs and human lower limbs, the gait dynamic was modeled as a spring damping and impedance model for supporting and swing phases. An iterative adaptive controller was developed for trajectory tracking and predication. To validate the proposed model, an in‐lab experiment to simulate typical construction activities was conducted, allowing us to assess tracking error. The experiment results suggest that the proposed model can minimize the trajectory tracking error to a level acceptable for safe operation. The iterative controllers allow fast error convergence for different construction scenarios with proper calibration. Therefore, the proposed pHRI iterative controllers are reliable and suitable for complicated activities within the dynamic working conditions intrinsic to construction sites.

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“…e equipment provides assistance for human walking, enhances human walking ability and speed, and relieves human fatigue under high load and long-time walking. Motion control algorithm is the core part of the control system of exoskeleton robot [2]. e key technology of the control algorithm is to recognize the gait information of the wearer and detect the wearer's motion intention.…”

Section: Introductionmentioning

confidence: 99%

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

2021

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At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

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Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

Cited by 15 publications

References 21 publications

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Design of Hybrid Phase Sliding Mode Control Scheme for Lower Extremity Exoskeleton

Gait trajectory‐based interactive controller for lower limb exoskeletons for construction workers

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

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