An exoskeleton joint output force control technology based on improved ADRC

For the purpose of reducing power consumption of a leg exoskeleton for augmenting human performance, a novel hybrid hydraulic system (HHS) which includes a unidirectional servo valve and a solenoid on-off valve is excogitated, and its energy saving control is studied in this paper. Inspired by the varieties of contact force between human leg and ground during walking, the unidirectional servo valve and the solenoid on-off valve are only activated in the stance phase and the swing phase, respectively. In the stance phase, a robust repetitive learning scheme is presented by using the backstepping technique for the unidirectional servo valve, aiming to track the periodic human leg movement, and in the swing phase, an onoff control is proposed for the solenoid valve to release the pressure in the hydraulic cylinder so that the exoskeleton leg is bent by the human leg passively. The proposed control strategy is implemented in an ARM-based embedded microprocessor and the control performance is verified via experiment on the developed exoskeleton robot. The experimental results show that the power consumption of the proposed system is almost 30% less than that of systems with bidirectional hydraulic system. INDEX TERMS Leg exoskeleton, robust repetitive learning, hybrid hydraulic system, energy saving.

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“…In (26),p 2 (t) is used to deal with the periodic difference p 2 (t) = p in (10). Now, it follows from (25) and (26) thaṫ…”

Section: Controller Design and Main Resultsmentioning

confidence: 99%

“…Theorem 1: Consider the leg exoskeleton driven by the HHS in (11), under Assumption (13), with the robust RLC (26). When the design parameters…”

Section: Controller Design and Main Resultsmentioning

confidence: 99%

Robust Repetitive Learning-Based Trajectory Tracking Control for a Leg Exoskeleton Driven by Hybrid Hydraulic System

et al. 2020

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“…In this paper a NLSEF based on Equation ( 17 ) given by J. Han [ 16 ] and Wu Qing Xu [ 41 ] in Equation ( 18 ) is used. A non-linear state error feedback function fal ( . )…”

Section: Active Disturbance Rejection Control For Lower Limb Robotmentioning

confidence: 99%

“…Stability is checked by input to state (ISS) framework where modification and experiments prove ARDC’s effectiveness. In another work [ 41 ], ADRC deals with nonlinearities like pressure fluctuation and friction during the control of exoskeleton, a new function is introduced to avoid shaking at inflation point during non-linear state error feedback (NLSEF). A sinusoidal tracking for exoskeleton joint output is compared with ADRC, and for disturbance rejection, it is compared with PID, where ADRC with NLSEF shows better results than ADRC.…”

Section: Introductionmentioning

confidence: 99%

Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton

Aole¹,

Elamvazuthi

Waghmare³

et al. 2020

Sensors

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Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) strategy is a systematic way out from a robust control paradox with possibilities and promises. In this modern era, we always try to find the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition observer-based control strategies is an added advantage where it uses a state estimation method which reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as a combination of linear extended state observer (LESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two degrees of freedom, Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at the hip and other at the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant, and harmonic disturbances, robustness against parameter variations, and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research.

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“…30 Its popularity has increased in the last few decades in various applications like power electronics, 31 motion control, 32,33 process control, 34,35 flight control, 36 and in the area of rehabilitation. 12,[37][38][39][40] ADRC plays an important part in the active elimination of disturbance either internal or external. 41,42 It is based on error driven rather than model-based control law; this property makes ADRC a unique controller and is considered a replacement for PID, only requires rough estimate of plant dynamics.…”

Section: Introductionmentioning

confidence: 99%

Non-linear active disturbance rejection control for upper limb rehabilitation exoskeleton

Aole¹,

Elamvazuthi

Waghmare³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Trajectory tracking in upper limb rehabilitation exercises is utilized for repeatability of joint movement to improve the patient’s recovery in the early stages of rehabilitation. In this article, non-linear active disturbance rejection control as a combination of non-linear extended-state observer and non-linear state error feedback is used for the sinusoidal trajectory tracking control of the two-link model of an upper limb rehabilitation exoskeleton. The two links represent movements like flexion/extension for both the shoulder joint and the elbow joint in the sagittal plane. The Euler–Lagrange method was employed to acquire a dynamic model of an upper limb rehabilitation exoskeleton. To examine the efficacy and robustness of the proposed method, four disturbances cases in simulation studies with 20% parameter variation were applied. It was found that the non-linear active disturbance rejection control is robust against disturbances and achieves better tracking as compared to proportional–integral–derivative and existing conventional active disturbance rejection control method.

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An exoskeleton joint output force control technology based on improved ADRC

Cited by 11 publications

References 7 publications

Robust Repetitive Learning-Based Trajectory Tracking Control for a Leg Exoskeleton Driven by Hybrid Hydraulic System

Robust Repetitive Learning-Based Trajectory Tracking Control for a Leg Exoskeleton Driven by Hybrid Hydraulic System

Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton

Non-linear active disturbance rejection control for upper limb rehabilitation exoskeleton

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