scholarly journals The Iterative Learning Gain That Optimizes Real-Time Torque Tracking for Ankle Exoskeletons in Human Walking Under Gait Variations

2021 ◽  
Vol 15 ◽  
Author(s):  
Juanjuan Zhang ◽  
Steven H. Collins
Keyword(s):  
Real Time ◽  
Lower Limb ◽  
Energy Flow ◽  
Legged Locomotion ◽  
Iterative Learning ◽  
Torque Control ◽  
Human Walking ◽  
Learning Gain ◽  
Integral Control ◽  
Applied Torque

Lower-limb exoskeletons often use torque control to manipulate energy flow and ensure human safety. The accuracy of the applied torque greatly affects how well the motion is assisted and therefore improving it is always of interest. Feed-forward iterative learning, which is similar to predictive stride-wise integral control, has proven an effective compensation to feedback control for torque tracking in exoskeletons with complicated dynamics during human walking. Although the intention of iterative learning was initially to benefit average tracking performance over multiple strides, we found that, after proper gain tuning, it can also help improve real-time torque tracking. We used theoretical analysis to predict an optimal iterative-learning gain as the inverse of the passive actuator stiffness. Walking experiments resulted in an optimum gain equal to 0.99 ± 0.38 times the predicted value, confirming our hypothesis. The results of this study provide guidance for the design of torque controllers in robotic legged locomotion systems and will help improve the performance of robots that assist gait.

Real Time Implementation of High Performance’s Direct Torque Control of Induction Motor on FPGA

2014 ◽  
Vol 9 (5) ◽  
pp. 919 ◽  
Author(s):  
Saber Krim ◽  
Soufien Gdaim ◽  
Abdellatif Mtibaa ◽  
Mohamed Faouzi Mimouni
Keyword(s):  
Real Time ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control

scholarly journals Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

2021 ◽  
pp. 107754632110191
Author(s):  
Farzam Tajdari ◽  
Naeim Ebrahimi Toulkani
Keyword(s):  
Real Time ◽  
Tracking Error ◽  
Parallel Robot ◽  
Torque Control ◽  
Test Bed ◽  
Linear Quadratic ◽  
Control Effort ◽  
The Real ◽  
Unknown Disturbances ◽  
Quadratic Integral

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

scholarly journals A Modified Model Predictive Torque Control with Parameters Robustness Improvement for PMSM of Electric Vehicles

Actuators ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 132
Author(s):  
Siyu Gao ◽  
Yanjun Wei ◽  
Di Zhang ◽  
Hanhong Qi ◽  
Yao Wei
Keyword(s):  
Real Time ◽  
Torque Ripple ◽  
Torque Control ◽  
Parameter Mismatch ◽  
Modified Model ◽  
Simulation And Experiment ◽  
Drive Systems ◽  
Low Torque ◽  
Time Update ◽  
Model Reference Adaptive

Model predictive torque control with duty cycle control (MPTC-DCC) is widely used in motor drive systems because of its low torque ripple and good steady-state performance. However, the selection of the optimal voltage vector and the calculation of the duration are extremely dependent on the accuracy of the motor parameters. In view of this situation, A modified MPTC-DCC is proposed in this paper. According to the variation of error between the measured value and the predicted value, the motor parameters are calculated in real-time. Meanwhile, Model reference adaptive control (MRAC) is adopted in the speed loop to eliminate the disturbance caused by the ripple of real-time update parameters, through which the disturbance caused by parameter mismatch is suppressed effectively. The simulation and experiment are carried out on MATLAB / Simulink software and dSPACE experimental platform, which corroborate the principle analysis and the correctness of the method.

Piezoresistive Polymer-Based Materials for Real-Time Assessment of the Stump/Socket Interface Pressure in Lower Limb Amputees

2017 ◽  
Vol 17 (7) ◽  
pp. 2182-2190 ◽  
Author(s):  
Armando Ferreira ◽  
Vitor Correia ◽  
Emilia Mendes ◽  
Claudia Lopes ◽  
Jose Filipe Vilela Vaz ◽  
...  
Keyword(s):  
Real Time ◽  
Lower Limb ◽  
Interface Pressure ◽  
Socket Interface ◽  
Lower Limb Amputees ◽  
Time Assessment

scholarly journals Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 152
Author(s):  
Dongqi Ma ◽  
Hui Lin
Keyword(s):  
Permanent Magnet ◽  
Iterative Learning Control ◽  
Mathematical Proof ◽  
Permanent Magnet Synchronous Motor ◽  
Learning Control ◽  
Synchronous Motor ◽  
Iterative Learning ◽  
Learning Gain ◽  
Periodic Load ◽  
P Type

To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for the system, we determine the next iterative learning interval and conduct real-time correction on the learning gain. For the learning interval, as the number of iterations increases, the actual interval that needs correction constantly shortens, accelerating the convergence speed. Second, we analyze the specific structure of the controller while applying reasonable assumptions pertaining to its condition. Using the λ-norm, we analyze and apply our mathematical knowledge to obtain a strict mathematical proof on the P-type iterative learning control and obtain the condition of convergence for the controller. Finally, we apply the proposed method for periodic ripple inhibition of the torque rotation speed of the permanent magnet synchronous motor and establish the system model; we use the periodic load torque to simulate the ripple torque of the synchronous motor. The simulation and experimental results indicate the effectiveness of the method.

Eliminating Structural Error in Real-Time Adjustable Four-Bar Path Generators Using Iterative Learning Control

2004 ◽  
Author(s):  
Hong-Jen Chen ◽  
Richard W. Longman ◽  
Meng-Sang Chew
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Path Generation ◽  
Structural Error ◽  
Four Bar Linkage

Fundamental concepts of Iterative Learning Control (ILC) are applied to path generating problems in mechanisms. As an illustration to such class of problems, an adjustable four-bar linkage is used. The coupler point of a four-bar traces a coupler curve that will in general deviate from the desired coupler path. Except at the precision points, the coupler curve will exhibit some structural error, which is the deviation from the specified curve. The structural error will repeat itself every cycle at exactly the same points over the range of interest. Since ILC is a methodology that was developed to handle similar repetitive errors in control systems, it is believed that it will be well served to apply it to this class of problems. Results show that ILC can be simple to implement, and it is found to be very well suited for such path generation problems.

scholarly journals Design of Supervisory Control for Speed Control of Wind Turbine using Torque-Control Method Based on Buck Converter

2020 ◽  
Vol 190 ◽  
pp. 00019
Author(s):  
Katherin Indriawati ◽  
Choirul Mufit ◽  
Andi Rahmadiansah
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Supervisory Control ◽  
Control Method ◽  
Speed Control ◽  
Buck Converter ◽  
Torque Control ◽  
Control Mode ◽  
Rotor Speed ◽  
Integral Control

The variation of wind speed causes the electric power generated by the turbine also varies. To obtain maximum power, the rotor speed of wind turbines must be optimally rated. The rotor speed can be controlled by manipulating the torque from the generator; this method is called Torque Control. In that case, a DC-DC converter is needed as the control actuator. In this study, a buck converter-based supervisory control design was performed on the Horizontal-axis wind turbines (HAWT). Supervisory control is composed of two control loops arranged in cascade, and there is a formula algorithm as the supervisory level. The primary loop uses proportional control mode with a proportional gain of 0.3, whereas in the secondary loop using proportional-integral control mode with a proportional gain of 5.2 and an integral gain of 0.1. The Supervisory control has been implemented successfully and resulted in an average increase in turbine power of 4.1 % at 5 m s–1 and 10.58 % at 6 m s–1 and 11.65 % at 7 m s–1, compared to wind turbine systems without speed control.

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