Electrically Stimulated Lower Limb using a Takagi-Sugeno Fuzzy Model and Robust Switched Controller Subject to Actuator Saturation and Fault under Nonideal Conditions

Nunes, Willian Ricardo Bispo Murbak; Alves, Uiliam Nelson Lendzion Tomaz; Sanches, Marcelo Augusto Assunção; Teixeira, Marcelo Carvalho Minhoto; Carvalho, Aparecido Augusto de

doi:10.1007/s40815-021-01115-9

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…To evaluate the coupling of the muscle controller and joint controller, a simulation was conducted for multi-level references of muscles and joints. Multiple references were used for angle reference in [32,43]. Relation (33) shows the multiple angle reference.…”

Section: Assessment Of Control Structure Performancementioning

confidence: 99%

Muscle Fatigue Regulation through Muscle Activation Control in a Knee Hybrid Exoskeleton: Simulation Study

Ghajari,

Moghaddam,

Kobravi

et al. 2023

Machines

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The knee hybrid exoskeleton is a system that aids in the rehabilitation of patients with mobility disorders. It comprises a powered exoskeleton and functional electrical stimulation, which moves the knee joint by stimulating the muscles. However, electrical stimulation of muscles can lead to muscle fatigue. For the first time, this article investigates the regulation of muscle fatigue by controlling muscle activation. To control muscle activation, an innovative adaptive controller for FES is designed. The adaptation law is designed utilizing a time-varying estimation of the muscle activation time parameter. The proportional-integral controller is designed to regulate the knee joint angle utilizing an electrical motor. The proportional-integral controller gains are calculated using an optimization method. A cooperative control structure is presented to use the electrical motor and functional electrical stimulation simultaneously. The muscle activation error is uniformly ultimately bounded, and its boundedness is proven through Lyapunov analysis; the error bound is also determined. The simulation results showed knee joint angle regulation and muscle fatigue regulation. The proposed control method results were compared with those based on model predictive control and switching control, which showed significant improvement in the joint angle error and muscle fatigue. The proposed method is appropriate for practical implementation based on the obtained results.

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Section: Assessment Of Control Structure Performancementioning

confidence: 99%

Muscle Fatigue Regulation through Muscle Activation Control in a Knee Hybrid Exoskeleton: Simulation Study

Ghajari,

Moghaddam,

Kobravi

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…According to the characteristics of unit backward operator z À1 , multiplying z Ài on both sides of formula (7) and z Àn on both sides of formula (8), respectively, it is not difficult to get…”

Section: Identification the Dynamic Linear Subsystem Parametersmentioning

confidence: 99%

“…1–3 Also, the importance of the nonlinear dynamical systems is reflected in model prediction, classification, and decision making and so on. Over the last decades, a wide variety of effective modeling methodologies have been developed for approximating nonlinear systems, such as Volterra series, 4 neural networks, 5,6 support vector machines, 7 fuzzy logic systems, 8 and block-oriented nonlinear systems. 9–13 Among these developed modeling methods, the block-oriented systems have attracted a lot of interest owing to prominent modeling ability.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification for Hammerstein nonlinear system with polynomial and state space model

Cao

2022

Measurement and Control

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This study investigates a two-stage parameter identification algorithm for the Hammerstein nonlinear system based on special test signals. The studied Hammerstein nonlinear system has a static nonlinear subsystem represented by polynomial basis function and a dynamic linear subsystem described by canonical observable state space model, and special test signals composed of binary signals and random signals are applied to parameter identification separation of the nonlinear subsystem and linear subsystem. The detailed identification procedures consist of two main steps. Firstly, using the characteristics that binary signals do not excite the static nonlinear subsystem, the dynamic linear subsystem parameters are identified through recursive least squares algorithm based on input-output data of binary signals. Secondly, unmeasurable state variables of the identified system are replaced with estimated values, thus the nonlinear subsystem parameters are obtained using recursive least squares algorithm with the help of input-output data of random signals. The efficiency and accuracy of proposed identification scheme are confirmed on experiment results of a numerical simulation and a practical nonlinear process, and experimental simulation results show that the developed two-stage identification algorithm has excellent predictive performance for identifying the Hammerstein nonlinear state space systems.

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“…Recently, some novel representation strategies—such as Lipschitz uncertain state space system (Allahverdi et al, 2020), neural networks (Dong et al, 2021; Ma and Yang, 2021), fuzzy systems (Willian et al, 2021; Zangeneh et al, 2020), fuzzy neural network (Zhou et al, 2020), and block-oriented nonlinear systems (Castro et al, 2017; Degachi et al, 2020; Li et al, 2021; Mohsen and Mohammad, 2019; Saif et al, 2020)—have been proposed to improve or construct the nonlinear system. In the literature (Allahverdi et al, 2020), the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system were addressed.…”

Section: Introductionmentioning

confidence: 99%

Modeling and identification for practical nonlinear process using neural fuzzy network–based Hammerstein system

Zheng

Cao

2023

Transactions of the Institute of Measurement and Control

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To address the strong nonlinearity and unknown disturbance in practical nonlinear process, an identification scheme of neural fuzzy network (NFN)–based Hammerstein nonlinear system using multi-signals is developed in this paper. The proposed Hammerstein system has a static nonlinear subsystem approximated by NFN and a dynamic linear subsystem described by autoregressive exogenous system (ARX). First, the nonlinear subsystem and the linear subsystem are separated and identified by the designed multi-signals, and then parameters of the linear subsystem and noise model are identified simultaneously by making use of recursive extended least squares approach, which is effective for compensating the error caused by output noise. Furthermore, in order to cope with unmeasurable variable issue of the identified system, auxiliary model technology is developed, and the nonlinear subsystem parameters are estimated by applying derived auxiliary model recursive extended least squares approach. Experimental results of three typical nonlinear processes verify the effectiveness and accuracy of the proposed method, and the simulation results show that the proposed method can obtain higher identification accuracy than other optimization algorithms.

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Electrically Stimulated Lower Limb using a Takagi-Sugeno Fuzzy Model and Robust Switched Controller Subject to Actuator Saturation and Fault under Nonideal Conditions

Cited by 6 publications

References 53 publications

Muscle Fatigue Regulation through Muscle Activation Control in a Knee Hybrid Exoskeleton: Simulation Study

Muscle Fatigue Regulation through Muscle Activation Control in a Knee Hybrid Exoskeleton: Simulation Study

Parameter identification for Hammerstein nonlinear system with polynomial and state space model

Modeling and identification for practical nonlinear process using neural fuzzy network–based Hammerstein system

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