Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton—Preliminary Results

Bao, Xuefeng; Molazadeh, Vahidreza; Dodson, Albert; Dicianno, Brad E.; Sharma, Nitin

doi:10.1109/tmrb.2020.2977416

Cited by 18 publications

(14 citation statements)

References 37 publications

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“…The threshold and saturation current amplitude of the stimulation are defined as the minimal current amplitude that generates observable knee extension torque and the maximal current amplitude that cannot increase knee extension torque, respectively. Both the threshold and saturation current amplitudes were determined by using a set of prior tests ( Bao et al (2020a) ). Due to the large current amplitude range between the threshold level and the saturation level (around 50 mA), a current amplitude modulating protocol with a stimulation frequency of 35 Hz and a pulse width of 400 μs was chosen in this work.…”

Section: Methodsmentioning

confidence: 99%

“…Notably, the NN-based control approach is derived to iteratively increase the feed-forward learning component and decrease the high-level torque generator’s high gain feedback component. The feed-forward learning is an improvement over our recent approach that used a high-gain position tracking controller for high-level torque generation for an experimental study on sit-to-stand tasks ( Bao et al (2020a) ). Unlike most exoskeleton controllers that follow a time-dependent desired joint trajectory ( Ha et al (2012) ; Contreras-Vidal et al (2016) ; Alibeji et al (2018b) ; Bae and Tomizuka (2012) ) or a desired time-dependent or EMG-generated torque trajectory ( Zhang et al (2015) ), the designed NN-based approach follows state-dependent desired joint trajectories known as virtual constraints ( Westervelt et al (2007) ; Gregg and Sensinger (2014) ).…”

Section: Introductionmentioning

confidence: 99%

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Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning

et al. 2021

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A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning

et al. 2021

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“…Semi-active robotic actuation only dissipates or stores kinetic energy created by muscle stimulation when being controlled [138], [139]. Instead, fully active robotic actuation can compensate or resist muscle torque to perform a desired motion [140], [141]. Open-loop control of FES replays a predetermined pattern of stimulation triggered by FSM [142], [143].…”

Section: H Control Of Hybrid Exoskeletonsmentioning

confidence: 99%

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Cao²,

Zhu

2021

IEEE Access

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Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.

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“…For example, in exoskeleton systems, the interplay between robot and human may be characterized as an interaction between teacher and student in a learning process. The teacher (robot) tries to minimize the student (human) error, applying a minimal effort [3]. In the context of autonomous cars, the driver interacts with the automatic controller that aims at reducing the driver's workload and, at the same time, taking prompt actions in case of human failure [4], [5].…”

Section: A Related Workmentioning

confidence: 99%

“…The positive-definite matrix J ∈ R 3×3 denotes the vehicle's inertia matrix. The rotation matrix from {I} to {B} is represented by S ∈ SO (3). Matrix E : R 3 → R 3×3 expresses the relation between the instantaneous rates of change of γ and the instantaneous components of ω.…”

Section: Predicted Healthiness Indexmentioning

confidence: 99%

Towards Safe Human-Quadrotor Interaction: Mixed-Initiative Control via Real-Time NMPC

Carlos

Franchi

Oriolo

2021

IEEE Robot. Autom. Lett.

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This paper presents a novel algorithm for blending human inputs and automatic controller commands, guaranteeing safety in mixed-initiative interactions between humans and quadrotors. The algorithm is based on nonlinear model predictive control (NMPC) and involves using the state solution to assess whether safety-and/or task-related rules are met to mix control authority. The mixing is attained through the convex combination of human and actual robot costs and is driven by a continuous function that measures the rules' violation. To achieve real-time feasibility, we rely on an efficient real-time iteration (RTI) variant of a sequential quadratic programming (SQP) scheme to cast the mixed-initiative controller. We demonstrate the effectiveness of our algorithm through numerical simulations, where a second autonomous algorithm is used to emulate the behavior of pilots with different skill levels. Simulations show that our scheme provides suitable assistance to pilots, especially novices, in a workspace with obstacles while underpinning computational efficiency.

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Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton—Preliminary Results

Cited by 18 publications

References 37 publications

Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning

Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Towards Safe Human-Quadrotor Interaction: Mixed-Initiative Control via Real-Time NMPC

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