Assistive Sliding Mode Control of a Rehabilitation Robot with Automatic Weight Adjustment

Hashemi, Arash; McPhee, John

doi:10.1109/embc46164.2021.9631110

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…In addition, there should be a calibration scenario to adjust the high-level controller model and parameters for each subject. On-board joint angle sensor and EMG sensor, as well as vision-based sensors, will be used to estimate the human joint torque or intention [ 2 , 73 , 74 ].…”

Section: Resultsmentioning

confidence: 99%

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

2022

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The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.

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Section: Resultsmentioning

confidence: 99%

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

2022

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“…This optimisation method generates a population of solutions and iteratively 'evolves' them based on the objective function by sharing traits and randomly assigning new ones. GAs are used to optimize the respective control strategies in [40,[53][54][55][56], while [52] uses GA to estimate friction for the robot dynamic model. GA is used to optimize joint angle and velocity parameters in a control system with a multi-layer perceptron neural network in [40].…”

Section: A Control Optimisationmentioning

confidence: 99%

“…This controller was validated in an experiment with a patient to provide good compliance and low patient-machine interaction torques. GA is used to optimize PD control in [54], and for Sliding Mode Control (SMC) in [55,56].…”

Section: A Control Optimisationmentioning

confidence: 99%

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation—A Systematic Review

Felix Brown,

Quan Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robotassisted rehabilitation and how intelligent controller design can improve them. Seven electronic research databases were searched for articles published in the years 2015 -2022 with articles selected based on relevance to the subject area of intelligent control systems in rehabilitation robotics. It was found that the most common use of intelligent algorithms for control is improving traditional control strategies with optimization and learning techniques. Intelligent algorithms are also commonly used in sensor output mapping, model construction, and for various data learning purposes. Experimental results show that intelligent controllers consistently outperform non-intelligent controllers in terms of transparency, tracking accuracy, and adaptability. Active participation of the patients and lowered interaction forces are consistently mentioned as important factors in improving the rehabilitation outcome as well as the patient experience. However, there are limited examples of studies presenting experimental results with impaired participants suffering limited range of motion, so the effectiveness of therapy provided by these systems is often difficult to quantify. A lack of universal evaluation criteria also makes it difficult to compare control systems outside of articles which use their own comparison criteria.

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“…The resulting controller has been applied to a ball on plate trajectory tracking system. Recently, a genetic algorithm was integrated with an SMC for rehabilitation robot control [121].…”

Section: Data-driven Controller Weights Adjustmentmentioning

confidence: 99%

Multibody dynamics and control using machine learning

et al. 2023

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Artificial intelligence and mechanical engineering are two mature fields of science that intersect more and more often. Computer-aided mechanical analysis tools, often multibody system software, are very versatile and have revolutionalized many industries. However, as shown by the literature presented in this review, combining the advantages of multibody system dynamics and machine learning creates new and exciting possibilities. For example, the multibody method can assist machine learning by providing synthetic data, while machine learning can provide fast and accurate subsystem models. The intersection of both approaches results in surrogate and hybrid modeling techniques, advanced control algorithms, and optimal design applications. A notable example is the development of autonomous systems for vehicles, robots, and mobile machinery. In our review we have found nontrivial, innovative, and even surprising applications of machine learning and multibody dynamics. This review focuses on applying neural networks, mainly deep learning, in connection with the multibody system method. Over one hundred and fifty papers are covered, and three main research areas are identified and introduced: data-driven modeling, model-based control and estimation, and data-driven control. The paper starts with a primer on machine learning and concludes with future research directions. The main goal is to provide a comprehensive and up-to-date review of existing literature to inspire further research.

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Assistive Sliding Mode Control of a Rehabilitation Robot with Automatic Weight Adjustment

Cited by 5 publications

References 26 publications

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation—A Systematic Review

Multibody dynamics and control using machine learning

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