ANFIS and MPC controllers for a reconfigurable lower limb exoskeleton

Rodríguez, Carlos Armando; Ponce, Pedro; Molina, Arturo

doi:10.1007/s00500-016-2321-9

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…As most actuators have a large gear ratio and significant damping, the position control is usually straightforward. More advanced techniques exist [288][289][290][291][292], although such high positioning accuracy is generally not required for exoskeletons, because the structure is often slightly flexible and makes the legs movement less precise anyway. Moreover, relying on highly precise movements is not practical when there is some level of variability in the environment and the user can also affect the movement (e.g.…”

Section: Position/speed Controller (Pos)mentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

192

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Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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Section: Position/speed Controller (Pos)mentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

192

View full text Add to dashboard Cite

show abstract

“…In the on-going years utilizing intelligence control, for example, neural network, fuzzy control, neuro fuzzy, that they can control nonlinear frameworks that would be troublesome or difficult to demonstrate mathematically. For example, Lianfang Tian et al utilized a neural system approach for the movement control of compelled adaptable controller's robots [16], [17]. The authors have built up a controlled knee and lower leg prosthesis model, in which they utilized PID and ANFIS control [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system

Raheema

Kadhim

Hussein

2021

IJEECS

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<div>This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.</div>

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“…The NFC is designed with two inputs as error and error derivative. The results show that the NFC is robust and converges faster than others [22]. The NFC parameters have been trained by different methods in literature as gradient descent method, Least Square Estimation (LSE), genetic algorithms, etc.…”

Section: Introductionmentioning

confidence: 99%

Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

Öztürk

Özkol

2021

SN Appl. Sci.

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In this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

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ANFIS and MPC controllers for a reconfigurable lower limb exoskeleton

Cited by 9 publications

References 22 publications

Review of control strategies for lower-limb exoskeletons to assist gait

Review of control strategies for lower-limb exoskeletons to assist gait

Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system

Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

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