The Neural Network Position-Control of a Moving Platform Using Electrorheological Valves

2002 ◽  
Vol 124 (3) ◽  
pp. 435-442 ◽  
Author(s):  
Seung-Bok Choi ◽  
Kum-Gil Sung ◽  
Jae-Wook Lee
Keyword(s):  
Neural Network ◽  
Electric Field ◽  
Position Control ◽  
Bridge Circuit ◽  
Silicone Oil ◽  
Hydraulic Cylinder ◽  
Network Control ◽  
Laboratory Model ◽  
Cargo Handling ◽  
Moving Platform

This paper presents position control of a moving platform using a hydraulic bridge circuit associated with electrorheological (ER) valves. Four cylindrical ER valves are designed and manufactured on the basis of electric field-dependent yield stress of an ER field, which is composed of chemically treated starch and silicone oil. The pressure drops of the ER valves are empirically identified with respect to the intensity of the electric field, and the hydraulic bridge circuit with four ER valves is constructed. The hydraulic cylinder system to be controlled by the ER valve bridge circuit is then incorporated with a moving platform of a cargo handling laboratory model. Subsequently, a neural network control scheme is formulated in order to control the position of the moving platform by activating ER valves of the cylinder system. The controller is experimentally realized and position tracking control results for desired trajectories of the moving platform are presented in the time domain.

Position Control of a Cylinder Using a Hydraulic Bridge Circuit With ER Valves

1997 ◽  
Vol 122 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Seung-Bok Choi ◽  
Woo-Yeon Choi
Keyword(s):  
Electric Field ◽  
Position Control ◽  
Bridge Circuit ◽  
Silicone Oil ◽  
Network Control ◽  
Operational Parameters ◽  
Pressure Drops ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Er Fluid

This paper presents the position control of a double-rod cylinder system using a hydraulic bridge circuit with four electro-rheological (ER) valves. After synthesizing a silicone oil-based ER fluid, a Bingham property of the ER fluid is first tested as a function of electric field in order to determine operational parameters for the ER valves. On the basis of the level of the field-dependent yield stress of the composed ER fluid, four cylindrical ER valves are designed and manufactured. Subsequently, step responses for pressure drops of the ER valve are empirically analyzed with respect to the intensity of the electric field. A cylinder system with a cart is then constructed using a hydraulic bridge circuit with four ER valves, and its governing equation of motion is derived. A neural network control scheme incorporating the proportional-integral-derivative (PID) controller is formulated through the feedback error learning method, and experimentally implemented for the position control of the cylinder system. Both regulating and tracking position control responses for square and sinusoidal trajectories are presented in time domain. In addition, a tracking durability of the control system is provided to demonstrate the practical feasibility of the proposed methodology. [S0022-0434(00)00701-2]

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.

Research on Neural Network PID Control Algorithm for a Quadrotor

2015 ◽  
Vol 719-720 ◽  
pp. 346-351 ◽  
Author(s):  
Wei Nan Gao ◽  
Jia Lu Fan ◽  
Yan Nong Li
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Simple Structure ◽  
Position Control ◽  
Network Control ◽  
Pid Algorithm ◽  
Control Scheme ◽  
Challenging Tasks ◽  
Space Function ◽  
Aerial Vehicle

Quadrotor is a kind of popular unmanned aerial vehicle which obtains prime advantages in simple structure, vertically taking off and landing and hovering ability; hence it possesses wide application prospects in reconnaissance and rescue, geological exploration and video surveillance. However, attitude and position control of the quadrotor are challenging tasks because it is an under-actuated system with strong nonlinear, coupling and model uncertainty characteristics. In this paper, the dynamics model and the state space function of the micro-quadrotor are firstly established. Then, a cascade control scheme is proposed to decouple the control system and a multivariate RBF(Radial Basis Function) neural network control PID algorithm is proposed to realize robust control of the quadrotor. This algorithm is not only characterized by simple structure and easy implementation, but also capable of self-adaption and online learning. Simulation results show that the proposed control algorithm performs well in tracking and under disturbances and model uncertainties.

On an effective design approach of cartesian space neural network control for robot manipulators

Robotica ◽  
1997 ◽  
Vol 15 (3) ◽  
pp. 305-312 ◽  
Author(s):  
Seul Jung ◽  
T. C. Hsia
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Traditional Approach ◽  
Complex Problem ◽  
Network Control ◽  
Joint Torque ◽  
Reference Trajectory ◽  
Control Input ◽  
Cartesian Space ◽  
Training Signal

It is well known that computed torque robot control is subjected to performance degradation due to uncertainties in robot model, and application of neural network (NN) compensation techniques are promising. In this paper we examine the effectiveness of neural network (NN) as a compensator for the complex problem of Cartesian space control. In particular we examine the differences in system performance of accurate position control when the same NN compensator is applied at different locations in the controller structure. It is found that using NN to modify the reference trajectory to compensate for model uncertainties is much more effective than the traditional approach of modifying control input or joint torque/force. To facilitate the analysis, a new NN training signal is introduced and used for all cases. The study is also extended to non-model based Cartesian control problems. Simulation results with three-link rotary robot are presented and performances of different compensating locations are compared.

scholarly journals Adaptive Sliding Mode Neural Network Control and Flexible Vibration Suppression of a Flexible Spatial Parallel Robot

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 212
Author(s):  
Qingyun Zhang ◽  
Xinhua Zhao ◽  
Liang Liu ◽  
Tengda Dai
Keyword(s):  
Neural Network ◽  
Elastic Deformation ◽  
Dynamic Equation ◽  
Sliding Mode ◽  
Parallel Robot ◽  
Network Control ◽  
Adaptive Sliding Mode Control ◽  
Neural Network Control ◽  
Adaptive Sliding Mode ◽  
Moving Platform

With the goal of creating a flexible spatial parallel robot system in which the elastic deformation of the flexible link causes a rigid moving platform to produce small vibrations, we proposed an adaptive sliding mode control algorithm based on a neural network. To improve the calculation efficiency, the finite element method was used to discretize the flexible spatial link, and then the displacement field of the flexible spatial link was described based on floating frame of reference coordinates, and the dynamic differential equation of the flexible spatial link considering high-frequency vibrations was established through the Lagrange equation. This was combined with the dynamic equation of the rigid link and the dynamic equation considering small displacements of the rigid movable platform due to elastic deformation, and a highly nonlinear and accurate dynamic model with a rigid–flexible coupling effect was obtained. Based on the established accurate multi-body dynamics model, the driving torque with coupling effects was calculated in advance for feedforward compensation, and the adaptive sliding mode controller was used to improve the tracking performance of the system. The nonlinear error was examined to determine the performance of the neural network’s approximation of the nonlinear system. The trajectory errors of the moving platform in the X-, Y-, and Z-directions were reduced by 12.1%, 38.8%, and 50.34%, respectively. The results showed that the designed adaptive sliding mode neural network control met the control accuracy requirements, and suppressed the vibrations generated by the deformation of the flexible spatial link.

Fuzzy neural network control of the rehabilitation robotic arm driven by pneumatic muscles

2015 ◽  
Vol 42 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Xianzhi Jiang ◽  
Zenghuai Wang ◽  
Chao Zhang ◽  
Liangliang Yang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Position Control ◽  
Fast Response ◽  
Network Control ◽  
Robotic Arm ◽  
Content Type ◽  
Pneumatic Muscles ◽  
Fuzzy Neural ◽  
Rehabilitation Robotic

Purpose – The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN). Design/methodology/approach – The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot. Findings – The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration. Research limitations/implications – This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online. Originality/value – Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

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