Data-Driven Predictive Control of a Pneumatic Ankle Foot Orthosis

Ülkir, Osman; Akgün, Gazi; Nasab, Ahad; Kaplanoğlu, Erkan

doi:10.4316/aece.2021.01007

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Thus, this paper increased the number of neurons in the input layer, which decreased the MSE for both types of ANN, the FFANN and the CFFANN. This can be seen in Figure 11 [19,20]. Several experiments were implemented to check the effects of applying the DML.…”

Section: Resultsmentioning

confidence: 99%

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Performance Evaluation of ANN Models for Prediction

Khrisat¹,

Alqadi²

2023

ATAIML

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One of the biggest problems that humans are faced with today is pollution and climate change. Pollution is not a new phenomenon and remains a leading cause of diseases and deaths. Mining, industrialization, exploration and urbanization caused global pollution, whose burdens are shared by developed and undeveloped countries alike. Awareness and stricter laws in the developed countries have contributed to environmental protection. Although all countries have paid attention to pollution, the impact and severity of its long-term consequences are being felt. There is a cause-and-effect link between the pollution of air, water and soil and the environment. This research aimed to prove that the main function of the philosophy of science is to have a functional understanding of knowledge, which views knowledge as a tool for prediction. Prediction is the function or mission of science or the goal that must be achieved if the scientific project is successful. In other words, prediction is the final harvest of description and interpretation. In addition, science is primarily concerned with the prediction of events that have occurred in the universe. A mature prediction is what science provides to validate scientific models. This paper introduced the concepts of using machine learning techniques to enhance the prediction process results. Pollution data set and the negative effects of polluted air data were used. We built, trained and tested various models in order to find the optimal model, which could enhance the results of the prediction process.

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Section: Resultsmentioning

confidence: 99%

“…ANN models can easily solve the prediction problem [19] by increasing the number of neurons in the input layer [20], or by adding extra hidden layers [21], which leads to more computations. Therefore, the Mean Square Error (MSE) between the targets and the calculated outputs is shown in Figure 3.…”

Section: Deep Machine Learningmentioning

confidence: 99%

Performance Evaluation of ANN Models for Prediction

Khrisat¹,

Alqadi²

2023

ATAIML

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“…exoskeletons have made breakthroughs in various fields such as the assistive mode, motion intent recognition and adaptive control algorithm. Osman ULKIR et al [9]. proposed a datadriven predictive control (DDPC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Terrains Assistive Force Parameter Optimization Method for Soft Exoskeleton

Sun

Jing

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Due to the complexity of terrain in natural environments, the soft exoskeleton cannot adaptively adjust parameters to achieve the optimal performance. To this end, a design for a soft exoskeleton assistive force parameter optimization method on multi-terrains is presented in this paper. Firstly, the core control parameters are determined by analyzing the system's motion dynamics. Then, the collected data from inertial measurement unit (IMU) is transferred to the convolutional neural network (CNN) to recognize the certain terrain. In the meanwhile, the control parameters corresponding to the different terrains are optimized by the Bayesian algorithm. Finally, the optimal assistive force parameters are transferred to the system for improving the performance of the soft exoskeleton. The experiment is conducted on three participants, wherein the net metabolic rates of the subjects are compared with and without the assistive force. The final results show that the metabolic rates of the subjects reduce the average value of 19.6% on flat ground, 11.6% on walking uphill, and 12.7% on walking upstairs. The experimental results confirm the effectiveness of the proposed method.

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“…In this instance, the control algorithm should find the best solution—not just any solution—for the targeted task while taking the constraints into account. Algorithms for model predictive control can be employed to achieve this [ 2 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification

Kaplanoğlu

Akgün

2022

Sensors

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This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the controller while performing the controller design. These features of the control algorithm eliminate an essential problem for rehabilitation robots in terms of easy customization and safe repetitive rehabilitation tasks that can be planned within certain constraints. Experiments were carried out with a designed hand rehabilitation system under repetitive and various therapy tasks. Real-time experiment results demonstrate the feasibility and efficiency of the proposed control approach to rehabilitation systems.

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Data-Driven Predictive Control of a Pneumatic Ankle Foot Orthosis

Cited by 9 publications

References 28 publications

Performance Evaluation of ANN Models for Prediction

Performance Evaluation of ANN Models for Prediction

Multi-Terrains Assistive Force Parameter Optimization Method for Soft Exoskeleton

Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification

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