An experimental PID controller for knee movement restoration with closed loop FES system

Ferrarin, Maurizio; D'Acquisto, E.; Mingrino, A.; Pedotti, A.

doi:10.1109/iembs.1996.657039

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…By the way these collection procedures do not globally change the programme for patients and do not require any specific setup except the one which will be used for the functional neuroprosthesis, simplifying therapists' efforts. This aspect is one important advantage with respect to other controllers discussed in literature, such all those based on PID [ 17 - 19 , 25 , 26 ] as well as model based controllers [ 25 ] and sliding mode controllers [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…The PID controller parameters were first identified using an iterative procedure based on the minimization of Root Mean Square Error (RMSE) [ 25 ], where the initial estimation of the optimization was derived from the Ziegler-Nichols rules [ 26 ]. Then the transfer function of the PID was discretized in view of a digital implementation of the control algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

Pedrocchi

Ferrante

Momi

et al. 2006

J NeuroEngineering Rehabil

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Background: The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

Pedrocchi

Ferrante

Momi

et al. 2006

J NeuroEngineering Rehabil

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“…The trajectory tracking control strategy is responsible for correcting DF during real-time gait. A literature analysis demonstrated that combining a model with a controller provides the best tracking performance, since the controller is able to compensate for any modeling errors that inevitably occur in real-time and the model avoids time delays inherent to the controller [12,13,13,[17][18][19][20][21]. Thus, the chosen dynamic inverse model was combined with a PD controller, in order to help reduce fatigue and compensate for any external disturbances, providing a more robust and effective control strategy.…”

Section: Drop Foot Correction Control Strategymentioning

confidence: 99%

Functional Electrical Stimulation for Gait Rehabilitation

Correia

Martins²,

Santos

2019

IFMBE Proceedings

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Conditions that can lead to a full or partial motor function loss, such as stroke or multiple sclerosis, leave people with disabilities that may interfere severely with lower body movements, such as gait. Drop Foot (DF) is a gait disorder that results in a reduced ability or total inability to contract the Tibialis Anterior (TA) muscle, causing an inability to raise the foot during gait. One of the most effective methods to correct DF is Functional Electrical Stimulation (FES). FES is a technique used to reproduce the activation patterns of functional muscles, in order to create muscular contractions through electrical stimulation of the muscle's nervous tissue.FES has first been introduced in 1961. However, the available commercial FES systems still do not take into account the fact that the gait differs from subject to subject, depending on their physical condition, muscular fatigue and rehabilitation stage. Therefore, they are unable to provide a personalized assistance to the user, delivering constant stimulation pulses that are only based on gait events. Consequently, they promote the early onset of fatigue and generate coarse movements. This dissertation aims to tackle the aforementioned issues by developing a FES system for personalized DF correction, tailored to each individual user's needs through the use of a Neural Network (NN).A Non-Linear Autoregressive Neural Network with Exogenous inputs (NARX Neural Network) was used to model the dynamics of the electrically stimulated TA muscle, in a novel approach that uses both the foot angle and the foot velocity. The model was combined with a Proportional Derivative controller to help compensate for any external disturbances. In order to create more natural movements, reference trajectories were obtained by recording the foot angle and velocity of healthy subjects walking at different speeds.The system has been validated with a healthy subject walking at 3 different speeds on a treadmill: 1 km/h, 1.5 km/h and 2 km/h. It was able

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“…Simple, relevant approaches include Proportional Integral Derivative (PID) control ( [14,15]). However, muscle-tendon dynamics are characterized by a strongly nonlinear behavior ( [16][17][18]), limiting the expected effectiveness of linear control techniques.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Nonlinear Control of a Class of Musculoskeletal Systems

Stolpe

Morel

2023

2023 American Control Conference (ACC)

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An experimental PID controller for knee movement restoration with closed loop FES system

Cited by 10 publications

References 3 publications

Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

Functional Electrical Stimulation for Gait Rehabilitation

Model-Based Nonlinear Control of a Class of Musculoskeletal Systems

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