An adaptive controller for human lower extremity exoskeleton robot

Hasan, Sk; Dhingra, Anoop K.

doi:10.1007/s00542-020-05207-8

Cited by 14 publications

(11 citation statements)

References 43 publications

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“…The trajectory tracking performance of the Sliding Mode Controller [18], Computed Torque Controller [15], Adaptive Controller [16], LQR [22] and Model Reference Computed Torque Controller [17] is shown in Figure 28. All the abovementioned controllers provide very high trajectory tracking performances.…”

Section: Performance Verification Of the Rbf Network-based Controller...mentioning

confidence: 99%

“…A linearised plant can be controlled by any type of linear control algorithm. The following are some examples of modelbased robot schemes that are often utilised in robot control applications: computer torque control [15], adaptive control [16], model reference computed torque control [17] and sliding mode control [18]. As it is impossible to precisely quantify the payload of an exoskeleton robot, the robot controller needs to be strong enough to handle parametric variations.…”

Section: Introductionmentioning

confidence: 99%

“…A robot can be controlled by linear, non-linear, robust, optimal and adaptive control systems. Most established control approaches [15][16][17][19][20][21][22] are based on model-based control strategies. For linearisation and control, different parts of the robot's dynamic equation of motion are used.…”

Section: Introductionmentioning

confidence: 99%

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Radial basis function‐based exoskeleton robot controller development

Hasan

2022

IET Cyber-Syst and Robotics

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The realisation of a model-based controller for a robot with a higher degree of freedom requires a substantial amount of computational power. A high-speed CPU is required to maintain a higher sampling rate. Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured. The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program. In this study, a radial basis function (RBF) neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskeleton robot. A realistic friction model is used for modelling joint friction. High trajectory tracking accuracies have been obtained. Evidence of computational efficiency has been observed. The stability analysis of the developed controller is presented. Analysis of variance is used to assess the controller's resilience to parameter variation. To show the effectiveness of the developed controller, a comparative study was performe between the developed RBF network-based controller and Sliding Mode Controller, Computed Torque Controller, Adaptive controller, Linear Quadratic Regulator and Model Reference Computed Torque Controller.

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Section: Performance Verification Of the Rbf Network-based Controller...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radial basis function‐based exoskeleton robot controller development

Hasan

2022

IET Cyber-Syst and Robotics

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Section: Adaptive Algorithmsmentioning

confidence: 99%

“…The adaptive control consists of a system that could adapt and predict performance in real-time during movements with high accuracy (Hasan and Dhingra, 2021). This type of system can regulate the operation of the interface, from the operating parameters extracted in real-time to reach the best mode of performance (Ljung et al, 2012;Hasan and Dhingra, 2021). Adaptive algorithms can automatically adjust the parameters to categorize individual movement patterns with fewer data and hardware requirements (Chemuturi et al, 2013;Allard et al, 2016).…”

Section: Adaptive Algorithmsmentioning

confidence: 99%

Adaptive Algorithms as Control Strategies of Smart Upper Limb Orthosis: A Protocol for a Systematic Scoping Review

et al. 2021

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Introduction: Adaptive algorithms for controlling orthosis emerged to overcome significant problems with automatic biosignal classification and personalized rehabilitation. Smart orthoses are evolving fast and need a better human-machine interaction performance since biosignals, feedback, and motor control dynamically change and must be adaptive. This manuscript outlines a scoping review protocol to systematically review the smart upper limb (UL) orthoses based on adaptive algorithms and feasibility tests.Materials and Methods: This protocol was developed based on the York framework. A field-specific structure was defined to achieve each phase. Eleven scientific databases (PubMed, Web of Science, SciELO, Koreamed, Jstage, AMED, CENTRAL, PEDro, IEEE, Scopus, and Arxiv) and five patent databases (Patentscope, Patentlens, Google Patents, Kripis, J-platpat) were searched. The developed framework will extract data (i.e., orthosis description, adaptive algorithms, tools used in the usability test, and benefits to the general population) from the selected studies using a rigorous approach. Data will be described quantitatively using frequency and trend analysis methods. Heterogeneity between the included studies will be assessed using the Chi-test and I-statistic. The risk of bias will be summarized using the latest Prediction Model Study Risk of Bias Assessment Tool.Discussion: This review will identify, map, and synthesize the advances about the description of adaptive algorithms for control strategies of smart UL orthosis using data extracted from patents and articles.

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