An Intelligent Control Method Based on Fuzzy Logic for a Robotic Testing System for the Human Spine

2005 ◽  
Vol 127 (5) ◽  
pp. 807-812 ◽  
Author(s):  
Lianfang Tian
Keyword(s):  
Fuzzy Logic ◽  
Measurement Errors ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Testing System ◽  
Human Spine ◽  
Wide Range ◽  
Robotic Testing ◽  
Force Difference ◽  
Robotic Testing System

In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine. An FLC is a real-time expert system that can emulate part of a human operator’s knowledge by using a set of action rules. The FLC provides simple but robust solutions that cover a wide range of system parameters and can cope with significant disturbances. It can be viewed as a heuristic and modular way of defining a nonlinear, table-based control system. In this study, an FLC is developed which uses the force difference and the change in force difference as the input parameters, and the displacement as the output parameter. A rule-table based on these parameters is designed for the controller. Experiments on a physical model composed of springs demonstrate the improved performance of the proposed method.

Validation of a High-Payload Robotic/UFS Testing System for Studying of Joint Motion

2007 ◽  
Author(s):  
Noah H. Lorang ◽  
Lauren A. Hellmann ◽  
Changfu Wu ◽  
Savio L.-Y. Woo
Keyword(s):  
Scientific Data ◽  
Testing System ◽  
Orthopedic Surgeons ◽  
Wide Range ◽  
Force Moment ◽  
Robotic Testing ◽  
Six Degree Of Freedom ◽  
Reconstructive Techniques ◽  
Robotic Testing System ◽  
High Payload

Robotic technology has been adopted for studying the biomechanics of the knee joint by our research center and others since 1993 to gain a fundamental understanding of the knee, as well as to provide orthopedic surgeons with scientific data on the efficacy of various reconstructive techniques [1–4]. A robotic testing system generally consists of a six degree-of-freedom (DOF) robotic manipulator with a universal force-moment sensor (UFS) attached to the end effector [5]. This testing system offers a wide range of motion with precision path and position repeatability.

scholarly journals Acromioclavicular joint reconstruction implants have differing ability to restore horizontal and vertical plane stability

2021 ◽  
Author(s):  
Mohamed Alkoheji ◽  
Hadi El-Daou ◽  
Jillian Lee ◽  
Adrian Carlos ◽  
Livio Di Mascio ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Acromioclavicular Joint ◽  
Vertical Plane ◽  
Testing System ◽  
Bonferroni Correction ◽  
Joint Reconstruction ◽  
Fresh Frozen ◽  
Robotic Testing ◽  
Robotic Testing System ◽  
Anterior Posterior

Abstract Purpose Persistent acromioclavicular joint (ACJ) instability following high grade injuries causes significant symptoms. The importance of horizontal plane stability is increasingly recognised. There is little evidence of the ability of current implant methods to restore native ACJ stability in the vertical and horizontal planes. The purpose of this work was to measure the ability of three implant reconstructions to restore native ACJ stability. Methods Three groups of nine fresh-frozen shoulders each were mounted into a robotic testing system. The scapula was stationary and the robot displaced the clavicle to measure native anterior, posterior, superior and inferior (A, P, S, I) stability at 50 N force. The ACJ capsule, conoid and trapezoid ligaments were transected and the ACJ was reconstructed using one of three commercially available systems. Two systems (tape loop + screw and tape loop + button) wrapped a tape around the clavicle and coracoid, the third system (sutures + buttons) passed directly through tunnels in the clavicle and coracoid. The stabilities were remeasured. The data for A, P, S, I stability and ranges of A–P and S–I stability were analyzed by ANOVA and repeated-measures Student t tests with Bonferroni correction, to contrast each reconstruction stability versus the native ACJ data for that set of nine specimens, and examined contrasts among the reconstructions. Results All three reconstructions restored the range of A–P stability to that of the native ACJ. However, the coracoid loop devices shifted the clavicle anteriorly. For S–I stability, only the sutures + buttons reconstruction did not differ significantly from native ligament restraint. Conclusions Only the sutures + buttons reconstruction, that passed directly through tunnels in the clavicle and coracoid, restored all stability measures (A, P, S, I) to the native values, while the tape implants wrapped around the bones anteriorised the clavicle. These findings show differing abilities among reconstructions to restore native stability in horizontal and vertical planes. (300 words)

scholarly journals Execution of BLDC Motor using Fuzzy Logic Controller on Propulsion Application for Hybrid Vehicle System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3989-3993
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Torque Ripple ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Rotor Speed ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Sensor Control

This research Paper proposes the Brushless DC motors control (BLDC) could accomplish higher execution looking into effectiveness in examination for old brushed DC motor controlling which is difficult to control because it requires a phase for switching circuit. This work proposes a fuzzy logic control for brushless DC motor for axis based on Hall Effect by applying sensor control system and also it produces brushless motor for rearranging the three phase conduction mode model. At long last this paper may be with create efficient control methodologies on enhance driving dynamics on the mechanical dynamic consider of propulsion method. The recommended control method stabilizes those controls services (speeds) done by controller of brushless DC motor drive (BLDC). On behalf of settling 2 wheels also physical favorable circumstances of BLDC motors are associated straight forwardly of the tires by improving the rotor speed. The parameters such as power factor, rotor speed, torque ripple, EMF is compensated & simulation results are tabulated.

scholarly journals Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

2020 ◽  
Vol 20 (3) ◽  
pp. 1653
Author(s):  
M.Z. Ismail ◽  
M.H.N. Talib ◽  
Z. Ibrahim ◽  
J. Mat Lazi ◽  
Z. Rasin
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Adaptive Controller ◽  
Tuning Parameter ◽  
Model Reference ◽  
Wide Range ◽  
Speed Performance ◽  
Self Tuning ◽  
Model Reference Adaptive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the   speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>

scholarly journals Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

2015 ◽  
Vol 64 (2) ◽  
pp. 291-314 ◽  
Author(s):  
Maziar Izadbakhsh ◽  
Alireza Rezvani ◽  
Majid Gandomkar
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Hybrid System ◽  
Pitch Angle ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Control Technique ◽  
Power Curves

Abstract In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor

2014 ◽  
Vol 573 ◽  
pp. 155-160
Author(s):  
A. Pandian ◽  
R. Dhanasekaran
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Fuzzy Controller ◽  
Direct Torque Control ◽  
Operating Conditions ◽  
Torque Control ◽  
Constant Speed ◽  
Three Phase

This paper presents improved Fuzzy Logic Controller (FLC) of the Direct Torque Control (DTC) of Three-Phase Induction Motor (IM) for high performance and torque control industrial drive applications. The performance of the IM using PI Controllers and general fuzzy controllers are meager level under load disturbances and transient conditions. The FLC is extended to have a less computational burden which makes it suitable for real time implementation particularly at constant speed and torque disturbance operating conditions. Hybrid control has advantage of integrating a superiority of two or more control techniques for better control performances. A fuzzy controller offers better speed responses for startup and large speed errors. If the nature of the load torque is varied, the steady state speed error of DTC based IM drive with fuzzy logic controller becomes significant. To improve the performance of the system, a new control method, Hybrid fuzzy PI control is proposed. The effectiveness of proposed method is verified by simulation based on MATLAB. The proposed Hybrid fuzzy controller has adaptive control over load toque variation and can maintain constant speed.

scholarly journals Robust Longitudinal Aircraft- Control Based on an Adaptive Fuzzy-Logic Algorithm

2002 ◽  
Vol 7 (1) ◽  
pp. 187
Author(s):  
Abdel- Latif Elshafei
Keyword(s):  
Fuzzy Logic ◽  
Feedback Linearization ◽  
Fuzzy Logic Controller ◽  
Control Law ◽  
Adaptive Fuzzy ◽  
Highly Nonlinear ◽  
Short Period ◽  
Wide Range ◽  
Modeling Errors ◽  
Adaptive Fuzzy Logic

To study the aircraft response to a fast pull-up manoeuvre, a short period approximation of the longitudinal model is considered. The model is highly nonlinear and includes parametric uncertainties. To cope with a wide range of command signals, a robust adaptive fuzzy logic controller is proposed. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, robustifying and adaptive components are included in the control law to compensate for modeling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearity. The second system is an adaptive one that compensates for modeling errors. The derivation of the control law based on a dynamic game approach is given in detail. Stability of the closed-loop control system is also verified. Simulation results based on an F16-model illustrate a successful tracking performance of the proposed controller.   

scholarly journals Dimmable High Power LED Driver Using Fuzzy Logic Controller

2021 ◽  
Vol 5 (2) ◽  
pp. 45
Author(s):  
Rizky Fatur Rochman ◽  
Eka Prasetyono ◽  
Rachma Prilian Eviningsih
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Constant Current ◽  
Led Driver ◽  
Flyback Converter ◽  
Compact Size ◽  
Wide Range ◽  
Driver Circuit ◽  
Converter Topology ◽  
Lighting Loads

The use of lighting loads is one of the crucial matters which increases every year. The increasing use then leads to the development of brighter and longer-lasting sources. In addition, the conventional use of lighting loads today, which only emit light at its maximum intensity, does not allow the consumers to adjust the brightness level as needed. Consequently, this condition may cause energy wastage. The LED lighting system is gaining popularity as it is widely used in a wide range of applications. The advantages of LEDs, such as its compact size and varied lamp colors, replace conventional lighting sources. The linear setting of the driver topology using the flyback converter was aimed to control the LEDs with a constant current in order to adjust the variation of the LED light intensity. The closed-loop driver circuit with flyback converter topology was designed as an LED driver with a given load specification from the LED string. A dimmable feature was included for adjusting the intensity of the light produced by the LEDs. Eventually, the fuzzy logic controller (FLC) method was applied to the integrated change setting to obtain a dynamic response.

scholarly journals Integrated Design of Adaptive & Fuzzy Logic Control for Trajectory Tracking of 2 DOF Quadrotor

2021 ◽  
Author(s):  
Mustefa Jibril
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Fuzzy Controller ◽  
Vertical Plane ◽  
Integrated Design ◽  
Adaptive Fuzzy ◽  
Quadrotor Uav

Accurate and precise trajectory tracking is crucial for a quadrotor to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the Adaptive and Fuzzy logic controller. The Adaptive fuzzy controller is implemented to govern the behavior of two degrees of freedom quadrotor UAV. The proposed controller allows controlling the movement of UAVs to track a given trajectory in a 2D vertical plane. The Fuzzy Logic system provides an automatic adjustment of the Adaptive parameters to reduce tracking errors and improve the quality of the controller. The results showed perfect behavior for the control law to control a quadrotor trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with Fuzzy and Proportional integral derivative (PID) control methods.

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