scholarly journals Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

2019 ◽  
Vol 9 (11) ◽  
pp. 2251 ◽  
Author(s):  
Bin Ren ◽  
Xurong Luo ◽  
Jiayu Chen
Keyword(s):  
Lower Limb ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Human Robot Interaction ◽  
Adaptive Iterative Learning Control ◽  
Interaction Torque ◽  
Lower Limb Exoskeleton ◽  
The Impact

The lower limb exoskeleton is a wearable human–robot interactive equipment, which is tied to human legs and moves synchronously with the human gait. Gait tracking accuracy greatly affects the performance and safety of the lower limb exoskeletons. As the human–robot coupling systems are usually nonlinear and generate unpredictive errors, a conventional iterative controller is regarded as not suitable for safe implementation. Therefore, this study proposed an adaptive control mechanism based on the iterative learning model to track the single leg gait for lower limb exoskeleton control. To assess the performance of the proposed method, this study implemented the real lower limb gait trajectory that was acquired with an optical motion capturing system as the control inputs and assessment benchmark. Then the impact of the human–robot interaction torque on the tracking error was investigated. The results show that the interaction torque has an inevitable impact on the tracking error and the proposed adaptive iterative learning control (AILC) method can effectively reduce such error without sacrificing the iteration efficiency.

Adaptive Iterative Learning Control for Robot Manipulators with Initial Resetting Errors

2011 ◽  
Vol 130-134 ◽  
pp. 265-269 ◽  
Author(s):  
Jian Ming Wei ◽  
Yun An Hu
Keyword(s):  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Error Function ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Unknown Parameters ◽  
Adaptive Iterative Learning Control ◽  
Suitable Parameter ◽  
Parameter Values

In this paper, an adaptive iterative learning control is presented for robot manipulators with unknown parameters, performing repetitive tasks. In order to overcome the initial resetting errors, an auxiliary tracking error function is introduced. The adaptive algorithm is derived along the iteration axis to search for suitable parameter values. The technical analysis shows convergence of the tracking errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed controller.

Backstepping Adaptive Iterative Learning Control for Robotic Systems

2013 ◽  
Vol 284-287 ◽  
pp. 1759-1763
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Fuzzy Neural Network ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks ◽  
Fuzzy Neural

A backstepping adaptive iterative learning control for robotic systems with repetitive tasks is proposed in this paper. The backstepping-like procedure is introduced to design the AILC. A fuzzy neural network is applied for compensation of the unknown certainty equivalent controller. Using a Lyapunov like analysis, we show that the adjustable parameters and internal signals remain bounded, the tracking error will asymptotically converge to zero as iteration goes to infinity.

scholarly journals Adaptive Iterative Learning Control for Complex Dynamical Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Xiuqing Hao ◽  
Junmin Li
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Reference Signal ◽  
Learning Control ◽  
Iterative Learning ◽  
Complex Dynamical Networks ◽  
Time Interval ◽  
Adaptive Iterative Learning Control ◽  
Dynamical Networks ◽  
Composite Energy

A new adaptive iterative learning control scheme is proposed for complex dynamical networks with repetitive operation over a fixed time interval. By designing difference type updating laws for unknown time-varying parameters and coupling strength, the state of each node in complex dynamical networks can track the reference signal. By constructing a composite energy function, a sufficient condition of the convergence of tracking error sequence is achieved in the iteration domain. Finally, a numerical example is given to show the effectiveness of the designed method.

scholarly journals Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Guan ◽  
Lan Zhou ◽  
YouShen Cao
Keyword(s):  
Motion Control ◽  
Lower Limb ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Knee Joints ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot

At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise

2019 ◽  
Vol 42 (2) ◽  
pp. 259-271
Author(s):  
Yan Geng ◽  
Xiaoe Ruan
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Measurement Noise ◽  
System Matrix ◽  
Adaptive Iterative Learning Control ◽  
Matrix Estimation

This paper investigates an adaptive iterative learning control (AILC) scheme for a class of switched discrete-time linear systems with stochastic measurement noise. For the case when the subsystems dynamics are unknown and the switching rule is arbitrarily fixed, the iteration-wise input-output data-based system lower triangular matrix estimation is derived by means of minimizing an objective function with a gradient-type technique. Then, the AILC is constructed in an interactive form with system matrix estimation for the switched linear systems to track the desired trajectory. Based on the derivation of the boundedness of the estimation error of system matrix, by virtue of norm theory and statistics technique, the tracking error and the covariance matrix of the tracking error are derived to be bounded, respectively. Finally, the AILC concept is extended to nonlinear systems by utilizing linearization techniques. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.

An Output Based Adaptive Iterative Learning Control with Particle Swarm Optimization for Robotic Systems

2013 ◽  
Vol 479-480 ◽  
pp. 737-741
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Swarm Optimization

We consider an output based adaptive iterative learning control (AILC) for robotic systems with repetitive tasks in this paper. Since the joint velocities are not measurable, a sliding window of measurements and an averaging filter approach are used to design the AILC. Besides, the particle swarm optimization (PSO) is used to adjust the learning gains in the learning process to improve the learning performance. Finally, a Lyapunov like analysis is applied to show that the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

Iterative Learning Control for a Class of Time-Delay Systems with Time-Varying Uncertainties

2013 ◽  
Vol 310 ◽  
pp. 428-434
Author(s):  
Ye Lei Zhao
Keyword(s):  
Time Delay ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Delay Systems ◽  
Time Varying ◽  
Adaptive Iterative Learning Control ◽  
Control Approach

An adaptive iterative learning control algorithm is proposed for a class of known time-delay nonlinear system with unknown time-varying parameter. A parameter separation technique is used to deal with time-delay problem. The control approach presented in this paper can guarantee that the tracking error converges to zero uniformly on the iteration interval as the iteration number approaches to infinity. A simulation example is provided to illustrate the efficiency of the proposed control algorithm.

Iterative Learning Control for a Type of Modified Smith Predictor

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Dongzuo Tian ◽  
Xingyong Song
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Smith Predictor ◽  
Single Input Single Output ◽  
Single Output ◽  
Modified Smith Predictor ◽  
Frequency Domain Techniques ◽  
The Impact

Abstract This article proposes a novel iterative learning control (ILC) design for a type of modified Smith predictor, in particular, to control a single-input single-output unstable plant or integral process with a time delay. Frequency domain techniques are applied to synthesize the learning control law, and a sufficient condition is given to ensure robust convergence of the tracking error. Robustness of the system is studied, considering a multiplicative uncertainty. Moreover, the impact of the load disturbance over successive iterations is investigated as well. To this end, a numerical example is given to demonstrate the efficacy of the proposed approach.

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