scholarly journals An Approach to Applying Feedback Error Learning for Functional Electrical Stimulation Controller: Computer Simulation Tests of Wrist Joint Control

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Takashi Watanabe ◽  
Keisuke Fukushima
Keyword(s):  
Computer Simulation ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Inverse Dynamics ◽  
Wrist Joint ◽  
Open Loop ◽  
Joint Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Feedback error-learning (FEL) controller that consists of a proportional-integral-derivative (PID) controller and an artificial neural network (ANN) had applicability to functional electrical stimulation (FES). Because of the integral (reset) windup, however, delay or overshoot sometimes occurred in feedback FES control, which was considered to cause inappropriate ANN learning and to limit the feasibility of the FEL controller for FES to controlling 1-DOF movements stimulating 2 muscles. In this paper, an FEL-FES controller was developed applying antireset windup (ARW) scheme that worked based on total controller output. The FEL-FES controller with the ARW was examined in controlling 2-DOF movements of the wrist joint stimulating 4 muscles through computer simulation. The developed FEL-FES controller was found to realize appropriately inverse dynamics model and to have a possibility of being used as an open-loop controller. The developed controller would be effective in multiple DOF movement control stimulating several muscles.

scholarly journals Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Takashi Watanabe ◽  
Yoshihiro Sugi
Keyword(s):  
Computer Simulation ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Wrist Joint ◽  
Training Data ◽  
Joint Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Feedforward Controller ◽  
Error Learning

Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device.

scholarly journals Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

2016 ◽  
Vol 26 (3) ◽  
Author(s):  
Francisco Resquín ◽  
Jose Gonzalez-Vargas ◽  
Jaime Ibáñez ◽  
Fernando Brunetti ◽  
José Luis Pons
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Control Strategy ◽  
Motor Impairment ◽  
Research Field ◽  
Robotic System ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Passive Exoskeleton

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

Functional Electrical Stimulation (FES) Control for Restoration and Rehabilitation of Motor Function

2012 ◽  
pp. 80-93
Author(s):  
Takashi Watanabe ◽  
Naoto Miura
Keyword(s):  
Electrical Stimulation ◽  
Motor Function ◽  
Functional Electrical Stimulation ◽  
Wearable Sensors ◽  
Integrated System ◽  
Rehabilitation Training ◽  
Cord Injury ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Functional Movements

Functional electrical stimulation (FES) has been studied and clinically applied to restoring or assisting motor functions lost due to spinal cord injury or cerebrovascular disease. Electrical stimulation without control of functional movements is also used for therapy or in rehabilitation training. In recent years, one of the main focuses of FES studies has been its application for rehabilitation of motor function. In this review, the authors first present the basics of applying electrical stimulation to the neuromuscular system for motor control. Then, two methods of FES control are discussed: controllers for FES based on feedback error learning (FEL) and on cycle-to-cycle control of limb movements. The FEL-FES controller can be practical in FES applications that need to control the musculoskeletal system that involves various nonlinear characteristics and delay in its responses to electrical stimulation. The cycle-to-cycle control is expected to be effective in controlling repetitive movements for rehabilitation training. Finally, a study on ankle dorsiflexion control during the swing phase using an integrated system of FES control and motion measurement with wearable sensors for rehabilitation is presented.

Trajectory control of robotic manipulators by using a feedback-error-learning neural network

Robotica ◽  
1995 ◽  
Vol 13 (5) ◽  
pp. 449-459 ◽  
Author(s):  
Zaryab Hamavand ◽  
Howard M. Schwartz
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Robot Manipulator ◽  
A Priori ◽  
Trajectory Control ◽  
A Priori Knowledge ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Priori Knowledge

SummaryThis paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.

scholarly journals Feedback Error Learning with Sliding Mode Control for Functional Electrical Stimulation: Elbow Joint Simulation

2019 ◽  
Vol 8 (12) ◽  
pp. 2971-2982
Keyword(s):  
Adaptive Control ◽  
Electrical Stimulation ◽  
Sliding Mode Control ◽  
Upper Limb ◽  
Elbow Joint ◽  
Sliding Mode ◽  
Mode Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Motivation: Upper-limb motor impairment is one of the most common consequences after Stroke. Limited capability for performing reaching and grasping movements hinders the execution of most activities of daily living. Consequently, the quality lives of the affected individuals are severely compromised. Due to these facts, the recovery of the upper limb functional capabilities is currently one of the keystones of the rehabilitation therapy. Background: Researchers are developing new methods and technologies to boost the outcomes of rehabilitation therapy. A hybrid robotic system has been proposed as a promising rehabilitation technology that combines a passive device (Armeo Spring exoskeleton) to support the arm weight against gravity with a Functional Electrical Stimulation (FES) system to execute the reaching task. This system provides to patients the possibility of training specifically and intensive exercises. Objective: The main objective of this paper is to investigate the performance and robustness of a Feedback Error learning (FEL) scheme mixed with sliding mode control (SMC) to control the FES. Methods: We implemented a nonlinear model describing the muscle response to FES and the dynamic behavior of the elbow joint. Using this model we carried out a simulation study to compare four control strategies: computed torque control (CTC), sliding mode Control (SMC), and adaptive feedback control using FEL: ANN+ CTC and FEL: ANN+SMC. We tested these controllers in two different simulation conditions: In the absence and presence of fatigue. To check the performance of the controllers, we compared the root means square (RMSE) of tracking error and the Normalized RMS of muscle stimulation for various range of movement (ROM). Results: All four controllers achieved good tracking performance in the absence of perturbations. When introducing muscle fatigue, good tracking performance is given essentially by the adaptive control ANN+SMC. Conclusion: Among the proposed approaches, we conclude that the adaptive control (FEL: ANN + SMC) is the most efficient and robust controller, which has been proven by calculating RMSE.

Nonlinear Discrete-time feedback error learning with PI Controller for AC servo motor

2008 ◽  
Author(s):  
Noppanan Suwanjatuporn ◽  
Mes Napaamporn ◽  
Waree Kongprawechnon ◽  
Sirisak Wongsura
Keyword(s):  
Discrete Time ◽  
Pi Controller ◽  
Servo Motor ◽  
Ac Servo ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Ac Servo Motor
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