Fuzzy PD-Type Iterative Learning Control of a Single Pneumatic Muscle Actuator

Ke, Da; Ai, Qingsong; Meng, Wei; Zhang, Congsheng; Liu, Quan

doi:10.1007/978-3-319-65292-4_70

Cited by 5 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of establishing the dynamics model of lower limb exoskeleton robot, the tracking error is adjusted to the learning signal to improve a certain control target and realize the tracking of the desired trajectory. Reference [17] used high-speed camera to carry out clinical gait experimental analysis and obtained the expected joint motion curves of hip joint and knee joint of lower limb exoskeleton robot in sagittal plane as shown in Figure 3.…”

Section: Complexitymentioning

confidence: 99%

“…e exoskeleton must be wearable, so the bone shape and the distribution of degrees of freedom must be studied before the configuration design. rough the study of the mechanism of human lower limb bones, the lower limb bones are composed of thigh bones, calf bones, and foot bones, which are, respectively, formed by three parts of skeletal joints, knee joints, and step joints, and the entire lower limbs are formed by two series mechanisms in parallel [17]. In order to study the movement of the human body, ergonomics and clinical medicine usually use the sagittal plane, frontal plane, and horizontal plane to describe.…”

Section: E Proposed Control Targets For Stormwater Runoff Control In mentioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Complexitymentioning

confidence: 99%

Section: E Proposed Control Targets For Stormwater Runoff Control In mentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

show abstract

“…This supports the implementation of some form of iterative learning control (ILC), which is well suited for rehabilitation. ILC is a typical data-driven method for controlling repetitively running object, which improves the tracking performance by introducing error data for updating the input of subsequent iterations [16]. However, the unstable function approximation and limited calculation speed of many ILCs make it impractical for PAMs-driven rehabilitation systems [17].…”

Section: Takedownmentioning

confidence: 99%

“…So the high-order estimation algorithm is expressed by (16) based on the derivation of (15) below and ˆ( ( ) ) / ( ) 0…”

Section: B High-order Ppd Estimation Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence

Zuo

et al. 2020

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power to weight ratio features. However, the nonlinearity and time-varying nature of PAMs makes it challenging to maintain highperformance tracking control. In this paper, a High-Order Pseudo-Partial Derivative based Model-Free Adaptive Iterative Learning Controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations, meanwhile, a high-order estimation algorithm is designed to estimate the pseudo-partial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller is verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFA ILC can track the desired trajectory with improved convergence and tracking performance. Index Terms-Pneumatic artificial muscle, model-free adaptive control, iterative learning control, convergence. I. INTRODUCTION NEUMATIC artificial muscle (PAM) is a tube-like actuator that largely mimics biological human muscle functions [1]. Compared to traditional electrical motors and hydraulic actuators, the lightweight, high compliance and high power-to-weight ratio of PAMs [2] have fueled their popularity among assistive exoskeletons and rehabilitation robots, such as the upper limb exoskeleton series RUPERT [3] and the lower limb orthotics KAFO [4]. However, unlike the conventional actuators adopted in Lokomat [5] and ArmeoPower [6], the nonlinear and time-varying nature of PAMs may cause Manuscript

show abstract

Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems

Huang,

Huang

2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

The data-driven high-order pseudo-partial derivative-based model-free adaptive iterative learning control (HOPPD-MFAILC) is always slow to converge and difficult to have excellent tracking results. To address the problem, an improved high-order pseudo-partial derivative-based model-free adaptive iterative learning control (iHOPPD-MFAILC) with fast convergence is proposed. First, to reduce the impact of the initial value of the pseudo-partial derivative (PPD) on the convergence speed of the algorithm, the initial PPD is corrected by introducing the high-order model estimation error. Second, to reduce the influence of system noise on the control performance, the original HOPPD-MFAILC control law is improved by introducing time-varying iterative proportional and time-varying iterative integral terms. Then, the convergence of the proposed improved control algorithm is demonstrated by theoretical analysis. Finally, simulations and experiments on the ball screw motion system show that the proposed iHOPPD-MFAILC can track the desired trajectory better. In addition, iHOPPD-MFAILC has better robustness in the noisy environment and achieves better convergence as well as trajectory tracking performance under different initial PPD conditions. The proposed control scheme has excellent application potential in precision motion control.

show abstract

Fuzzy PD-Type Iterative Learning Control of a Single Pneumatic Muscle Actuator

Cited by 5 publications

References 15 publications

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

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

High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence

Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems

Contact Info

Product

Resources

About