Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain–Machine Interfaces

Li, Zhijun; Li, Junjun; Zhao, Shuai; Yuan, Ye; Kang, Yu; Chen, C. L. Philip

doi:10.1109/tnnls.2018.2872595

Cited by 119 publications

(34 citation statements)

References 33 publications

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“…It has been proved that the fuzzy neural network has a nonlinear function approachability with arbitrary precision [43]. Then, we obtain the new control law: (27) where u eq denotes the same designed as in Equation 16, and u NN denotes compensation term. Definê ξ(x, w) as the fuzzy approximation function of ξ(t), andξ(x, w) can be described aŝ…”

Section: Adaptive Fuzzy Neural Network Compensatormentioning

confidence: 99%

“…where Ω is the set of w. Combining Equations (27), (16), (25), and (10), the system error function can be attained:…”

Section: Adaptive Fuzzy Neural Network Compensatormentioning

confidence: 99%

“…Then, the time-delay control using TDE technique is developed in many robot systems [26][27][28][29][30]. TDC is generally recognized as a simple, efficient, effective, robust model-free scheme.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

et al. 2020

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Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in ± 0 . 5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human–machine hybrid control model.

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Section: Adaptive Fuzzy Neural Network Compensatormentioning

confidence: 99%

“…where Ω is the set of w. Combining Equations (27), (16), (25), and (10), the system error function can be attained:…”

Section: Adaptive Fuzzy Neural Network Compensatormentioning

confidence: 99%

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Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

et al. 2020

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“…The product model, based on the emotional experience, as shown in Figure 2, is obtained by studying users and the environment through user experiences such as group interviews, contextual records, and user experiments. The circular development process, which is design-test-modify, can be applied to guide for nursing and rehabilitation products as a quantifiable indicator [30].…”

Section: Product Design Based On Emotional Experiencementioning

confidence: 99%

Novel Design and Adaptive Fuzzy Control of a Lower-Limb Elderly Rehabilitation

Zhang

Chen

et al. 2020

Electronics

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Design and control of a lower-limb exoskeleton rehabilitation of the elderly are the main challenge for health care in the past decades. In order to satisfy the requirements of the elderly or disabled users, this paper presents a novel design and adaptive fuzzy control of lower-limb empowered rehabilitation, namely MOVING UP. Different from other rehabilitation devices, this article considers active rehabilitation training devices. Firstly, a novel product design method based on user experience is proposed for the lower-limb elderly exoskeleton rehabilitation. At the same time, in order to achieve a stable operation control for the assistant rehabilitation system, an adaptive fuzzy control scheme is discussed. Finally, the feasibility of the design and control method is validated with a detailed simulation study and the human-interaction test. With the booming demand in the global market for the assistive lower-limb exoskeleton, the methodology developed in this paper will bring more research and manufacturing interests. Electronics 2020, 9, 343 2 of 17 the effectiveness of rehabilitation training. A wearable upper limb therapy robot based on Pneumatic Muscle Actuator (PMA) was introduced in Reference [13]. This device has five degrees of freedom, works similarly to human muscle functions, has high compliance and safety. Besides, Reference [14] developed a cable drive for the exoskeleton, which has seven degrees of freedom and can be used in medical, remote control, test research, and other fields.In general, the structure of the exoskeleton rehabilitation device is simple, but it is challenging to carry out rehabilitation training for the local body parts, and the rehabilitation effect is not ideal [15,16]. The research of the end-effector rehabilitation device is mainly used in limb rehabilitation, remote control, and other fields, and its research direction focuses on improving freedom, ensuring safety and reducing complexity, and so forth. Existing exoskeleton rehabilitation devices are complicated and expensive, and they are mostly used in experiments and difficult to achieve marketization. Both types of rehabilitation devices have their own advantages and disadvantages. It is necessary to select a suitable rehabilitation device type in specific applications and match different training modes to achieve a better training effect [17].The early version in Reference [18] developed by the Federal Institute of Technology in Zurich, was passive gait training. Continuous passive movement (CPM) machines and simple rehabilitation treadmills, which are widely used in clinical practice, generally only provide passive training modes. An ankle rehabilitation robot based on steady-state visual evoked potentials was discussed in Reference [19], which combined SSVEP signals with a virtual reality environment and was able to determine the subject's intention to perform passive training. In addition, for rule-based control structures, a human-machine interface was presented in Reference [20]. The robot manipulator (RM) can pe...

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“…QFD is a user-driven product development method that uses a systematic and standardized approach to investigate and analyze user needs [40,41]. HOQ is a tool component that implements this method during product development.…”

Section: Product Innovation Modelmentioning

confidence: 99%

Neural Approximation Enhanced Predictive Tracking Control of a Novel Designed Four-Wheeled Rollator

Zhang

Fan

et al. 2019

Applied Sciences

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In the past few decades, the research of assistant mobile rollators for the elderly has attracted more and more investigation attention. In order to satisfy the needs of older people or disabled patients, this paper develops a neural approximation based predictive tracking control scheme to improve and support the handicapped through the novel four-wheeled rollator. Firstly, considering the industrial product theory, a novel Kano-TRIZ-QFD engineering design approach is presented to optimize the mechanical structure combined with humanistic care. At the same time, in order to achieve a stable trajectory tracking control for the assistant rollator system, a neural approximation enhanced predictive tracking control is discussed. Finally, autonomous tracking mobility of the presented control scheme has received sufficient advantage performance in position and heading angle variations under the external uncertainties. As the market for the medical device of the elderly rollators continues to progress, the method discussed in this article will attract more investigation and industry concerns.

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Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain–Machine Interfaces

Cited by 119 publications

References 33 publications

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

Novel Design and Adaptive Fuzzy Control of a Lower-Limb Elderly Rehabilitation

Neural Approximation Enhanced Predictive Tracking Control of a Novel Designed Four-Wheeled Rollator

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