Neural-network-based near-time-optimal position control method for DC motor servosystems

1995 ◽  
Vol 142 (5) ◽  
pp. 493-500 ◽  
Author(s):  
V. Yen ◽  
D.Y. Liu ◽  
T.Z. Liu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Dc Motor ◽  
Optimal Position ◽  
Time Optimal

Time-Optimal Coordination Control for the Gear-Shifting Process in Electric-Driven Mechanical Transmission (Dog Clutch) without Impacts

2020 ◽  
Vol 9 (2) ◽  
pp. 155-168
Author(s):  
Ziwang Lu ◽  
◽  
Guangyu Tian ◽  
Keyword(s):  
Control Strategy ◽  
Position Control ◽  
Control Method ◽  
Synchronization Control ◽  
Mechanical Transmission ◽  
Coordination Control ◽  
Optimal Position ◽  
Drive Motor ◽  
Time Optimal ◽  
Optimal Coordination

Torque interruption and shift jerk are the two main issues that occur during the gear-shifting process of electric-driven mechanical transmission. Herein, a time-optimal coordination control strategy between the the drive motor and the shift motor is proposed to eliminate the impacts between the sleeve and the gear ring. To determine the optimal control law, first, a gear-shifting dynamic model is constructed to capture the drive motor and shift motor dynamics. Next, the time-optimal dual synchronization control for the drive motor and the time-optimal position control for the shift motor are designed. Moreover, a switched control for the shift motor between a bang-off-bang control and a receding horizon control (RHC) law is derived to match the time-optimal dual synchronization control strategy of the drive motor. Finally, two case studies are conducted to validate the bang-off-bang control and RHC. In addition, the method to obtain the appropriate parameters of the drive motor and shift motor is analyzed according to the coordination control method.

Time-Optimal Position Control of DC Motor Servo Drive

2019 ◽  
Vol 1 (12) ◽  
pp. 87-90
Author(s):  
Andrzej ANDRZEJEWSKI
Keyword(s):  
Position Control ◽  
Dc Motor ◽  
Servo Drive ◽  
Optimal Position ◽  
Time Optimal

scholarly journals Neural Network Control Design for an Unmanned Aerial Vehicle with a Suspended Payload

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 931 ◽  
Author(s):  
Cai Luo ◽  
Zhenpeng Du ◽  
Leijian Yu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Network Control ◽  
Outdoor Environment ◽  
Test Results ◽  
Cluttered Environments ◽  
Aerial Vehicle ◽  
Backstepping Sliding Mode Control

Unmanned aerial vehicles (UAVs) demonstrate excellent manoeuvrability in cluttered environments, which makes them a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenges for a drone with a package connected by a wire is analysed. During the delivering task, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network-based backstepping sliding mode control method is designed, which is capable of monitoring the drone’s flight path and desired attitude with a suspended cable attached. The convergence of the position and attitude errors together with the Lyapunov function are employed to attest to the robustness of the nonlinear transportation platform. The proposed control system is tested with a simulation and in an outdoor environment. The simulation and open field test results for the UAV transportation platform verify the controllers’ reliability.

NEURAL NETWORK POSITION CONTROL OF DC MOTOR

2016 ◽  
Vol 59 (3) ◽  
pp. 139-144
Author(s):  
A. I. Bobikov ◽  
◽  
A. O. Bozvanov ◽  
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Dc Motor ◽  
Network Position

Hybrid intelligent adaptive controller for tiltrotor UAV

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jafar Tavoosi
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Hybrid Control ◽  
Adaptive Controller ◽  
Content Type ◽  
Flight Mode ◽  
Horizontal Flight ◽  
Aerial Vehicle ◽  
Model Reference Adaptive

PurposeIn this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.Design/methodology/approachIn the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.FindingsThe proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.Originality/valueNovel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

scholarly journals Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA

2018 ◽  
Vol 11 (3) ◽  
pp. 71-78
Author(s):  
Aula N. Abd
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control ◽  
Back Propagation ◽  
Dc Motor ◽  
Motor Speed ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Field Programmable

In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.

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