Automated Feed-Forward Learning for Pressure-Compensated Mobile Hydraulic Valves With Significant Dead-Zone

2017 ◽  
Author(s):  
Jarmo Nurmi ◽  
Jouni Mattila
Keyword(s):  
Motion Control ◽  
Dead Zone ◽  
Heavy Duty ◽  
Automated Identification ◽  
Forward Models ◽  
Feed Forward ◽  
Loop Control ◽  
Hydraulic Manipulators ◽  
Hydraulic Manipulator ◽  
Hydraulic Valves

Hydraulic manipulators on mobile machines, whose hydraulic actuators are usually controlled by mobile hydraulic valves, are being considered for robotic closed-loop control. A feed-forward-based strategy combining position and velocity feedback has been found to be an effective method for the motion control of pressure-compensated mobile hydraulic valves that have a significant dead zone. The feed-forward can be manually identified. However, manually identifying the feed-forward models for each valve-actuator pair is often very time-consuming and error-prone. For this practical reason, we propose an automated feed-forward learning method based on velocity and position feedback. We present experimental results for a heavy-duty hydraulic manipulator on a forest forwarder to demonstrate the effectiveness of the proposed method. These results motivate the automated identification of velocity feed-forward models for motion control of heavy-duty hydraulic manipulators controlled by pressure-compensated mobile hydraulic valves that have a significant input dead zone.

A Neural Network Strategy for Learning of Nonlinearities Toward Feed-Forward Control of Pressure-Compensated Hydraulic Valves With a Significant Dead Zone

2018 ◽  
Author(s):  
Jarmo Nurmi ◽  
Mohammad M. Aref ◽  
Jouni Mattila
Keyword(s):  
Adaptive Learning ◽  
Dead Zone ◽  
Effective Means ◽  
Previous Method ◽  
Deep Convolutional Neural Networks ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Highly Nonlinear ◽  
Hydraulic Manipulator ◽  
Hydraulic Valves

A velocity feed-forward-based strategy is an effective means for controlling a heavy-duty hydraulic manipulator; in particular, a typical valve-controlled hydraulic manipulator, to compensate for valve dead-zone and other hydraulic valve nonlinearities. Based on our previous work on the adaptive learning of valve velocity feed-forwards, manually labelling and identifying the dead-zones and the other nonlinearities in the velocity feed-forward curves of pressure-compensated hydraulic valves can be avoided. Nevertheless, it may take two to three minutes or more per actuator to identify a pressure-compensated valve’s highly nonlinear velocity feed-forward in real-time with an adaptive approach, which should be reduced for realistic applications. In this paper, inspired by brain signal analysis technologies, we propose a new method based on deep convolutional neural networks comparing with the previous method to significantly reduce this online learning process with the strong nonlinearities of pressure-compensated hydraulic valves. We present simulation results to demonstrate the effectiveness of the deep learning-based learning method compared to the previous results with an adaptive control-based learning.

Nonlinear Model-Based Controller Design for a Hydraulic Rack and Pinion Gear Actuator

2018 ◽  
Author(s):  
Pauli Mustalahti ◽  
Jouni Mattila
Keyword(s):  
Closed Loop ◽  
Controller Design ◽  
Dynamical Behavior ◽  
Control Design ◽  
Closed Loop Control ◽  
Loop Control ◽  
Hydraulic Manipulators ◽  
Pinion Gear ◽  
Hydraulic Manipulator ◽  
Heavy Loads

Hydraulic manipulators are extensively utilized to move heavy loads in many industrial tasks. In commercial applications, a manipulator base is required to rotate a motion range of the full 360°. This is usually implemented by using a hydraulic rack and pinion gear actuator. Due to the manipulator’s long reach and heavy loads, manipulator tip acceleration can produce significant torque to the rotation gear in free-space motion. Imposed by nonlinear dynamical behavior (involving, e.g., the gear backlash and actuator friction) added to high inertia, a system closed-loop control design becomes a challenging task. An advanced closed-loop control enables to increase the automation-level of hydraulic manipulators. This study designs a novel subsystem-dynamics-based controller for a hydraulic rack and pinion gear actuator utilizing the control design principles of the virtual decomposition control (VDC) approach. An adaptive backlash compensation is incorporated in the control design. Furthermore, the proposed controller is implemented in previously-designed state-of-the-art hydraulic manipulator control. The stability of the overall control design is proven. Experiments with a full-scale commercial hydraulic manipulator demonstrate the effectiveness of the proposed adaptive backlash compensation and the overall control performance.

Improved Pole-placement Control with Feed-forward Dead Zone Compensation for Position Tracking of Electro-Pneumatic Actuator System Improved Pole-placement Control with Feed-forward Dead Zone Compensation for Position Tracking of Electro-Pneumatic Actuato

2021 ◽  
Vol 20 (2) ◽  
pp. 25-32
Author(s):  
Noorhazirah Sunar ◽  
Mohd Fua’ad Rahmat ◽  
Ahmad ‘Athif Mohd Fauzi ◽  
Zool Hilmi Ismail ◽  
Siti Marhanis Osman ◽  
...  
Keyword(s):  
Dead Zone ◽  
Pneumatic Actuator ◽  
Pole Placement ◽  
Position Tracking ◽  
Zone Model ◽  
Feed Forward ◽  
The Dead ◽  
Chattering Effect ◽  
Pole Placement Controller ◽  
Pole Placement Control

Dead-zone in the valve degraded the performances of the Electro-Pneumatic Actuator (EPA) system.  It makes the system difficult to control, become unstable and leads to chattering effect nearest desired position.  In order to cater this issue, the EPA system transfer function and the dead-zone model is identified by MATLAB SI toolbox and the Particle Swarm Optimization (PSO) algorithm respectively.  Then a parametric control is designed based on pole-placement approach and combine with feed-forward inverse dead-zone compensation.  To reduce chattering effect, a smooth parameter is added to the controller output.  The advantages of using these techniques are the chattering effect and the dead-zone of the EPA system is reduced.  Moreover, the feed-forward system improves the transient performance.  The results are compared with the pole-placement control (1) without compensator and (2) with conventional dead-zone compensator.  Based on the experimental results, the proposed controller reduced the chattering effect due to the controller output of conventional dead-zone compensation, 90% of the pole-placement controller steady-state error and 30% and 40% of the pole-placement controller with conventional dead-zone compensation settling time and rise time.

Heat Release Experimental Analysis for RCCI Combustion Optimization

2018 ◽  
Author(s):  
V. Ravaglioli ◽  
F. Ponti ◽  
F. Carra ◽  
M. De Cesare
Keyword(s):  
Low Temperature ◽  
Experimental Analysis ◽  
Closed Loop ◽  
Combustion Stability ◽  
Closed Loop Control ◽  
Combustion Control ◽  
Transient Conditions ◽  
Feed Forward ◽  
Loop Control ◽  
Injection Parameters

Over the past years, the increasingly stringent emission regulations for Internal Combustion Engines (ICE) spawned a great amount of research in the field of combustion control optimization. Nowadays, optimal combustion control has become crucial, especially to properly manage innovative Low Temperature Combustion (LTC) strategies, usually characterized by high instability, cycle-to-cycle variability and sensitivity to slight variations of injection parameters and thermal conditions. Many works demonstrate that stability and maximum efficiency of LTC strategies can be guaranteed using closed-loop control strategies that vary the standard injection parameters (mapped during the base calibration activity) to keep engine torque and center of combustion (CA50) approximately equal to their target values. However, the combination of standard base calibration and closed-loop control is usually not sufficient to accurately control Low Temperature Combustions in transient conditions. As a matter of fact, to properly manage LTC strategies in transient conditions it is usually necessary to investigate the combustion methodology of interest and implement specific functions that provide an accurate feed-forward contribution to the closed-loop controller. This work presents the experimental analysis performed running a light-duty compression ignited engine in dual-fuel RCCI mode, the goal being to highlight the way injection parameters and charge temperature affect combustion stability and ignition delay. Finally, the paper describes how the obtained results can be used to define the optimal injections strategy in the analyzed operating points, i.e. the combination of injection parameters to be used as a feed-forward for a closed-loop combustion control strategy.

Study on Twin-Rotary Motion Control System Based on Siemens Technology CPU

2012 ◽  
Vol 229-231 ◽  
pp. 2201-2204
Author(s):  
Cun Hai Pan ◽  
Hui Li ◽  
Su Mei Du ◽  
Wei Gao
Keyword(s):  
Control System ◽  
Motion Control ◽  
Rotary Motion ◽  
Three Dimension ◽  
Motion Control System ◽  
Loop Control ◽  
Closed Loop Control System ◽  
Loop Control System ◽  
Dimension Space ◽  
The Cost

A twin-rotary motion control system was built based on a cam technology and Siemens S7-300T PLC in this paper. The system can position accurately in a three-dimension space using a twin-servo closed loop control system and can real-time monitor various parameters of positioning system by HMI (Human Machine Interface). It also can automatically collect various parameter information and judge the type of fault.At the same time, the degree of automation has been raised and the cost of production was reduced.

DUTY-CYCLED SPINNING BASED 3D MOTION CONTROL APPROACH FOR BEVEL-TIPPED FLEXIBLE NEEDLE INSERTION

2018 ◽  
Vol 18 (07) ◽  
pp. 1840017 ◽  
Author(s):  
QIN YAO ◽  
XUMING ZHANG
Keyword(s):  
Motion Control ◽  
Control Method ◽  
Tracking Error ◽  
Open Loop ◽  
Loop Control ◽  
3D Motion ◽  
Control Approach ◽  
Target Error ◽  
The Mean ◽  
Simulation Results

Flexible needle has been widely used in the therapy delivery because it can advance along the curved lines to avoid the obstacles like important organs and bones. However, most control algorithms for the flexible needle are still limited to address its motion along a set of arcs in the two-dimensional (2D) plane. To resolve this problem, this paper has proposed an improved duty-cycled spinning based three-dimensional (3D) motion control approach to ensure that the beveled-tip flexible needle can track a desired trajectory to reach the target within the tissue. Compared with the existing open-loop duty-cycled spinning method which is limited to tracking 2D trajectory comprised of few arcs, the proposed closed-loop control method can be used for tracking any 3D trajectory comprised of numerous arcs. Distinctively, the proposed method is independent of the tissue parameters and robust to such disturbances as tissue deformation. In the trajectory tracking simulation, the designed controller is tested on the helical trajectory, the trajectory generated by rapidly-exploring random tree (RRT) algorithm and the helical trajectory. The simulation results show that the mean tracking error and the target error are less than 0.02[Formula: see text]mm for the former two kinds of trajectories. In the case of tracking the helical trajectory, the mean tracking error target error is less than 0.5[Formula: see text]mm and 1.5[Formula: see text]mm, respectively. The simulation results prove the effectiveness of the proposed method.

Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

2018 ◽  
Vol 32 (15) ◽  
pp. 5041-5052 ◽  
Author(s):  
Georgios N. Kouziokas ◽  
Alexander Chatzigeorgiou ◽  
Konstantinos Perakis
Keyword(s):  
Groundwater Level ◽  
Public Management ◽  
Meteorological Data ◽  
Forward Models ◽  
Feed Forward

scholarly journals High-accuracy motion control of a motor servo system with dead-zone based on a single hidden layer neural network

2020 ◽  
Vol 30 (1) ◽  
pp. 27-44
Author(s):  
Jian Hu ◽  
Shupeng Cao ◽  
Chenchen Xu ◽  
Jianyong Yao ◽  
Zhiwei Xie
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Dead Zone ◽  
Servo System ◽  
High Accuracy ◽  
Hidden Layer
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