A New Control Approach to Nonlinear Systems Undergoing Changes in a System Parameter

2000 ◽  
Author(s):  
Hyuk C. Nho ◽  
Peter Meckl
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Sudden Change ◽  
Torque Control ◽  
Tracking Performance ◽  
Control Architecture ◽  
Model Based ◽  
Payload Uncertainty ◽  
Payload Mass ◽  
Computed Torque

Abstract Conventional model-based computed torque control fails to produce good trajectory tracking performance in the presence of payload uncertainty and modeling error. The problem is how to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. A feedforward (multilayer) neural network is trained off-line to capture the nonlinear inverse dynamics of the system. The network is placed in the feedforward path to minimize tracking error. The network receives the same input signals as conventional computed torque as well as the payload mass estimate, which comes from a fuzzy logic mass estimator. The fuzzy logic, trained off-line to optimize the membership function, is developed to estimate the changing payload mass. The fuzzy logic estimator is based on joint acceleration error to improve the speed of detection and estimation of payload mass change. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experiment results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty. The results of the control architecture are also compared with those of a model-based control architecture. This approach can be employed in any nonlinear mechanical system with a sudden change in a parameter.

scholarly journals Mechatronic Model Based Computed Torque Control of a Parallel Manipulator

10.5772/5650 ◽  
2008 ◽  
Vol 5 (1) ◽  
pp. 14 ◽  
Author(s):  
Zhiyong Yang ◽  
Jiang Wu ◽  
Jiangping Mei ◽  
Jian Gao ◽  
Tian Huang
Keyword(s):  
Parallel Manipulator ◽  
Torque Control ◽  
Model Based ◽  
Computed Torque Control ◽  
Computed Torque

Study of the Electricity-Draw Control System in Electrical Engineering Based on Neural Network DTC

2013 ◽  
Vol 676 ◽  
pp. 209-212
Author(s):  
Lu Huan Shi ◽  
Yao Hui Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Strategy ◽  
Dynamic Performance ◽  
Asynchronous Motor ◽  
Sudden Change ◽  
Direct Torque Control ◽  
Torque Control ◽  
Electric Locomotive ◽  
Low Velocity

In the electricity draws control system, the change of Low-velocity area of Rs will bring about a series of problem, especially the stator current and flux, will cause the distortion of the speed pulse vibration. The test discussed control scheme and optimization designs of asynchronous draw motors from exchange transmission electric locomotive operation characteristic demand. It adopts control strategy of neural network direct torque control (DTC) to control electricity draw the locomotive, to analyze the reacting of starting and sudden change of load, verifying this method may effectively improve the dynamic performance of the asynchronous motor, got up the very good inhibitory action to the low speed area torque pulsation. Thus the simulation results have proven the neural network DTC control strategy feasibility.

Decentralized LPV Gain-Scheduled PD Control of a Robotic Manipulator

2009 ◽  
Author(s):  
H. Abbas ◽  
S. M. Hashemi ◽  
H. Werner
Keyword(s):  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Controller Design ◽  
Robotic Manipulator ◽  
Low Complexity ◽  
Torque Control ◽  
Tracking Performance ◽  
Pd Controller ◽  
Model Based ◽  
Gain Scheduled

In this paper, low-complexity linear parameter-varying (LPV) modeling and control of a two-degrees-of-freedom robotic manipulator is considered. A quasi-LPV model is derived and simplified in order to facilitate LPV controller synthesis. An LPV gain-scheduled, decentralized PD controller in linear fractional transformation form is designed, using mixed sensitivity loop shaping to take — in addition to high tracking performance — noise and disturbance rejection into account, which are not considered in model-based inverse dynamics or computed torque control schemes. The controller design is based on the existence of a parameter-dependent Lyapunov function — employing the concept of quadratic separators — thus reducing the conservatism of design. The resulting bilinear matrix inequality (BMI) problem is solved using a hybrid gradient-LMI technique. Experimental results illustrate that the LPV controller clearly outperforms a decentralized LTI-PD controller and achieves almost the same accuracy as a model-based inverse dynamics and a full-order LPV controllers in terms of tracking performance while being of significantly lower complexity.

Computed Torque Control of a Caterpillar Robot Manipulator Using Neural Network

2016 ◽  
Vol 15 ◽  
pp. 106-118 ◽  
Author(s):  
Mehran Rahmani ◽  
Ahmad Ghanbari
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulator ◽  
Setting Time ◽  
Torque Control ◽  
Learning Ability ◽  
Control Effort ◽  
Better Than ◽  
Computed Torque

This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

Three-Level DTC Based on Fuzzy Logic and Neural Network of Sensorless DSSM Using Extended Kalman Filter

2015 ◽  
Vol 5 (4) ◽  
pp. 453 ◽  
Author(s):  
Elakhdar Benyoussef ◽  
Abdelkader Meroufel ◽  
Said Barkat
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Synchronous Machine ◽  
Double Star ◽  
Torque Control ◽  
Operation Conditions ◽  
Stator Flux

This paper presents a direct torque control is applied for salient-pole double star synchronous machine without mechanical speed and stator flux linkage sensors. The estimation is performed using the extended Kalman filter known by it is ability to process noisy discrete measurements. Two control approaches using fuzzy logic DTC, and neural network DTC are proposed and compared. The validity of the proposed controls scheme is verified by simulation tests of a double star synchronous machine. The stator flux, torque, and speed are determined and compared in the above techniques. Simulation results presented in this paper highlight the improvements produced by the proposed control method based on the extended Kalman filter under various operation conditions.

scholarly journals Fuzzy Torque Control of the Bionic Flexible Manipulator Actuated by Pneumatic Muscle Actuators

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Kai Liu ◽  
Yining Chen ◽  
Jiaqi Xu ◽  
Yang Wu ◽  
Yonghua Lu ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Control Algorithm ◽  
Kinematic Model ◽  
Torque Control ◽  
Tracking Performance ◽  
Flexible Manipulator ◽  
Strong Nonlinearity ◽  
Tracking Accuracy ◽  
Pneumatic Muscle ◽  
Computed Torque

Abstract A bionic flexible manipulator driven by pneumatic muscle actuator (PMA) can better reflect the flexibility of the mechanism. Current research on PMA mainly focuses on the modeling and control strategy of the pneumatic manipulator system. Compared with traditional electro-hydraulic actuators, the structure of PMA is simple but possesses strong nonlinearity and flexibility, which leads to the difficulty in improving the control accuracy. In this paper, the configuration design of a bionic flexible manipulator is performed by human physiological map, the kinematic model of the mechanism is established, and the dynamics is analyzed by Lagrange method. A fuzzy torque control algorithm is designed based on the computed torque method, where the fuzzy control theory is applied. The hardware experimental system is established. Through the co-simulation contrast test on MATLAB and ADAMS, it is found that the fuzzy torque control algorithm has better tracking performance and higher tracking accuracy than the computed torque method, and is applied to the entity control test. The experimental results show that the fuzzy torque algorithm can better control the trajectory tracking movement of the bionic flexible manipulator. This research proposes a fuzzy torque control algorithm which can compensate the error more effectively, and possesses the preferred trajectory tracking performance.

scholarly journals Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

2017 ◽  
Vol 6 (2) ◽  
pp. 79
Author(s):  
Sudheer H ◽  
Kodad SF ◽  
Sarvesh B
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Duty Ratio ◽  
Time Period ◽  
Hysteresis Controller ◽  
Remaining Time ◽  
Selection Of

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

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