Iterative Learning Impedance Control in Gait Rehabilitation Robot

2014 ◽  
Vol 494-495 ◽  
pp. 1084-1087
Author(s):  
Fu Cheng Cao ◽  
Hai Xin Sun ◽  
Li Rong Wang
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Impedance Control ◽  
Iterative Learning ◽  
Gait Rehabilitation ◽  
Control Law ◽  
Rehabilitation Robot ◽  
Simulation Results ◽  
Impedance Parameters

An iterative learning impedance control algorithm is presented to control a gait rehabilitation robot. According to the circumstances of the patient, the appropriate rehabilitation target impedance parameters are set. With the adoption of iterative learning control law, the impedance error in the closed loop is guaranteed to converge to zero and the iterative trajectories follow the desired trajectories over the entire operation interval. The effectiveness of the proposed method is shown through numerical simulation results.

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

scholarly journals An Accelerating Iterative Learning Control Based on an Adjustable Learning Interval

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Dongqi Ma ◽  
Hui Lin
Keyword(s):  
Convergence Rate ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Convergence Conditions ◽  
Monotonic Convergence ◽  
Number Of Iterations ◽  
Controlled Object

An iterative learning control algorithm with an adjustable interval is proposed for nonlinear systems to accelerate the convergence rate of iterative learning control. Forλ-norm, the monotonic convergence of ILC was analyzed, and the corresponding convergence conditions were obtained. The results showed that the convergence rate was mainly determined by the controlled object, the control law gain, the correction factor, and the iteration interval size and that the control law gain was corrected in real time in the modified interval and the modified interval shortened as the number of iterations increased, further accelerating the convergence. The numerical simulation shows the effectiveness of the proposed method.

scholarly journals Robust Design of Terminal ILC withH∞Mixed Sensitivity Approach for a Thermoforming Oven

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Guy Gauthier ◽  
Benoit Boulet
Keyword(s):  
Surface Temperature ◽  
Robust Design ◽  
Industrial Application ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Plastic Sheet ◽  
Sensitivity Approach ◽  
Simulation Results

This paper presents a robust design approach for terminal iterative learning control (TILC). This robust design uses theH∞mixed-sensitivity technique. An industrial application is described where TILC is used to control the reheat phase of plastic sheets in a thermoforming oven. The TILC adjusts the heater temperature setpoints such that, at the end of the reheat cycle, the surface temperature map of the plastic sheet will converge to the desired one. Simulation results are included to show the effectiveness of the control law.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

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