scholarly journals Nonlinear Synchronous Control for H-Type Gantry Stage Used in Electric VehiclesManufacturing

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2305
Author(s):  
Ran Chen ◽  
Zongxia Jiao ◽  
Liang Yan ◽  
Yaoxing Shang ◽  
Shuai Wu
Keyword(s):  
Tracking Error ◽  
Tracking Performance ◽  
System Model ◽  
Lyapunov Analysis ◽  
End Effector ◽  
Mechanical Coupling ◽  
Synchronous Control ◽  
Linear Motors ◽  
Proposed Model ◽  
Synchronous Error

The H-type gantry stage (HGS) is widely used in electric vehicle manufacturing and other fields. However, resulting from the existence of mechanical coupling, the synchronous control problem of HGS always troubles many engineers. Most synchronization schemes were either engaged in improving each motor’s tracking performance or committed to pure motion synchronization only. However, tracking and synchronous performance are interconnected, because of the mechanical coupling. In this paper, a rigid assumed system model of HGS, concerning the effects of mid-beam rotary inertia, mid-beam stiffness, and end-effector movement, is presented. Based on the proposed model, an adaptive robust synchronous control based on a rigid assumed model (ARSCR) is proposed to improve both synchronous and tracking performance of the HGS. From the Lyapunov analysis, the proposed ARSCR can achieve the convergence of synchronous error and tracking error, simultaneously. An HGS driven by dual linear motors is built and used to perform the experimental verification. The experimental results indicate the effectiveness of the proposed method.

Modeling and Synchronous Control of Dual Mechanically Coupled Linear Servo System

2014 ◽  
Vol 137 (4) ◽  
Author(s):  
Wu-Sung Yao
Keyword(s):  
Machine Tool ◽  
High Speed ◽  
Dynamic Stiffness ◽  
System Modeling ◽  
Coupled System ◽  
Mechanical Coupling ◽  
Synchronous Control ◽  
Linear Motors ◽  
Operation Speed ◽  
Control Scheme

This paper presents a system modeling technique for a high-speed gantry-type machine tool driven by linear motors. One feed axis of the investigated machine tool is driven by the joint thrust from two parallel linear motors. These two parallel motors are coupled mechanically to form the Y-axis while another standalone motor fixed to a support forms the X-axis. The components in the X-axis, which is treated as the mechanical coupling, are carried by the slides of the Y-axis motors. This configuration is applied to improve the dynamic stiffness of the system and operation speed/acceleration. However, the precise synchronous control of the two parallel and coupled motors would be the major challenge. To overcome this challenge, a multivariable system identification method is developed in this paper. This method is used to construct an accurate system mathematical model for the target coupled system. A synchronous control scheme is then applied to the model obtained using the proposed technique. The performance of the system is experimentally verified with a high-speed S-curve motion profile. The results substantiate the constructed system model and demonstrate the effectiveness of the control scheme.

Multiparameter Real-World System Identification using Iterative Residual Tuning

2021 ◽  
pp. 1-12
Author(s):  
Adam Allevato ◽  
Mitch W Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just 5 optimization iterations.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

Intelligent Synchronous Control for Gantry Position Stage

2014 ◽  
Vol 620 ◽  
pp. 317-320
Author(s):  
Po Huan Chou ◽  
Faa Jeng Lin ◽  
Wen Chuan Chen ◽  
Ying Min Chen
Keyword(s):  
External Disturbance ◽  
Back Propagation ◽  
Field Oriented Control ◽  
Servo Drive ◽  
Control Approach ◽  
Synchronous Control ◽  
Linear Motors ◽  
On Line ◽  
On Line Learning ◽  
Servo Drive System

A cross-coupled proportional-integral-derivative neural network (PIDNN) control is proposed in this study for the synchronous control of a dual linear motors servo system which is installed in a gantry position stage. First, the dynamics of the field-oriented control PMLSM servo drive with a lumped uncertainty, which contains parameter variations, external disturbance and friction force, is introduced. Then, to achieve accurate trajectory tracking performance with robustness, an intelligent control approach using PIDNN is proposed for the field-oriented control PMLSM servo drive system. In the proposed approach, the on-line learning algorithms of the PIDNN are derived using back-propagation (BP) method to guarantee the convergence of the network. Finally, some experimental results are illustrated to depict the validity of the proposed control approach.

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

2020 ◽  
Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan
Keyword(s):  
Trajectory Tracking ◽  
Wind Waves ◽  
Tracking Error ◽  
Tracking Performance ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Surface Vessels ◽  
Waves And Currents ◽  
Predictive Controller ◽  
Disturbance Model

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

Tracking Control of a Nonholonomic Mobile Robot Using Neural Network

2015 ◽  
pp. 631-661 ◽  
Author(s):  
Şahin Yildirim ◽  
Sertaç Savaş
Keyword(s):  
Neural Networks ◽  
Mobile Robot ◽  
Tracking Error ◽  
Back Propagation ◽  
Open Loop ◽  
Tracking Performance ◽  
Back Propagation Algorithm ◽  
Tracking Errors ◽  
Nonholonomic Mobile Robot ◽  
Feed Forward Neural Networks

The goal of this chapter is to enable a nonholonomic mobile robot to track a specified trajectory with minimum tracking error. Towards that end, an adaptive P controller is designed whose gain parameters are tuned by using two feed-forward neural networks. Back-propagation algorithm is chosen for online learning process and posture-tracking errors are considered as error values for adjusting weights of neural networks. The tracking performance of the controller is illustrated for different trajectories with computer simulation using Matlab/Simulink. In addition, open-loop response of an experimental mobile robot is investigated for these different trajectories. Finally, the performance of the proposed controller is compared to a standard PID controller. The simulation results show that “adaptive P controller using neural networks” has superior tracking performance at adapting large disturbances for the mobile robot.

scholarly journals A New Nonlinear Active Disturbance Rejection Control for the Cable Car System to Restrain the Vibration

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xinyan Hu ◽  
Lina Li
Keyword(s):  
Disturbance Rejection ◽  
System Model ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
The People ◽  
Disturbance Rejection Control ◽  
Proposed Model ◽  
Spring System ◽  
Linear Invariant ◽  
Mass Spring

The safety of the cable car system is very important for the lives of the people. But, it is easily affected by the environment such as the wind which causes the cable car system to have strong vibration disturbance, thus degrading the safety of the cable car system. In this paper, a new nonlinear active disturbance rejection control (ADRC) is proposed to restrain the vibration of the cable car. First, a new two-mass-spring system model is utilized to establish the cable car system model. The new translation vibration nonlinear model is derived by a linear-invariant two-mass-spring system. Then, a special nonlinear fal• is designed to restrain the vibration, and a new high-order nonlinear ADRC is presented for the cable car system. Finally, simulation results verify the feasibility and accuracy of the proposed model.

Multiparameter Real-World System Identification Using Iterative Residual Tuning

2020 ◽  
Author(s):  
Adam Allevato ◽  
Mitch Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation — where we outperform two baselines — and on a real system, where we achieve marble tracking error of 4.02% after just 5 optimization iterations.

Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System

2002 ◽  
Author(s):  
J. Q. Gong ◽  
Bin Yao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Tracking Error ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Operating Conditions ◽  
Tracking Performance ◽  
Motor Drive ◽  
Output Tracking ◽  
On Line

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.

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