Adaptive Robust Control of a Linear Motor Driven Precision Industrial Gantry With Improved Cogging Force Compensation

2008 ◽  
Author(s):  
Lu Lu ◽  
Bin Yao ◽  
Zheng Chen ◽  
Qingfeng Wang
Keyword(s):  
Linear Motor ◽  
Adaptive Robust Control ◽  
Force Model ◽  
Practical Applications ◽  
Linear Motors ◽  
Proposed Model ◽  
On Line ◽  
Linear Encoder ◽  
Sinusoidal Functions ◽  
Force Compensation

This paper proposes a new model for cogging forces of linear motor systems. Sinusoidal functions of positions are used to capture the largely periodic nature of cogging forces with respect to position effectively while B-spline functions are employed to account for the additional aperiodic part of cogging forces. This model is experimentally demonstrated to be able to capture both the periodic and non-periodic characteristics of cogging force while having a linear parametrization form which makes effective on-line adaptive compensation of cogging forces possible. A discontinuous projection based desired compensation adaptive robust controller (DCARC) is then constructed for linear motors, which makes full use of the proposed cogging force model for an improved cogging force compensation. Comparative experimental results are obtained on both axes of a linear motor driven Anorad industrial gantry having a linear encoder resolution of 0.5 μm. Experiments are done with each axis running separately to compare the three algorithms: DCARC without cogging force compensation, DCARC with sinusoidal cogging force model compensation, and DCARC with the proposed cogging force model compensation. The results show that DCARC with proposed model compensation achieves the best tracking performance among the three algorithms tested, validating the proposed cogging force model. The excellent tracking performances obtained in experiments also verify the effectiveness of the proposed ARC control algorithms in practical applications.

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.

Accurate Motion Control of Linear Motors With Adaptive Robust Compensation of Nonlinear Electromagnetic Field Effect

2011 ◽  
Author(s):  
Zheng Chen ◽  
Bin Yao ◽  
Qingfeng Wang
Keyword(s):  
Electromagnetic Field ◽  
Steady State ◽  
Motion Control ◽  
Field Effect ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Tracking Performance ◽  
Positioning Systems ◽  
Linear Motors ◽  
Driving Current

Iron-core linear motors have been widely used in high-speed/high-accuracy positioning systems due to the elimination of mechanical transmissions. Many control methodologies have been developed for linear motor motion control, such as H∞ control, adaptive control and sliding mode control. Compensations of various nonlinearities such as frictions and cogging forces have also been carried out to obtain better tracking performance. However, the relationship between the driving current and the resulting motor force has been assumed to be linear, which is invalid for high driving coil currents due to the saturating electromagnetic field effect. This paper focuses on the effective compensation of nonlinear electromagnetic field effect so that the system can be operated at even higher acceleration or heavier load without losing achievable control performance. Specifically, cubic polynomials with unknown weights are used for an effective approximation of the unknown nonlinearity between the electromagnetic force and the driving current. The effectiveness of such an approximation is verified by off-line identification experiments. An adaptive robust control (ARC) algorithm with online tuning of the unknown weights and other system parameters is then developed to account for various uncertainties. Theoretically, the proposed ARC algorithm achieves a guaranteed transient and steady-state performance for position tracking, as well as zero steady-state tracking error when subjected to parametric uncertainties only. Comparative experiments of ARC with and without compensation of electromagnetic nonlinearity done on a linear-motor-driven industrial gantry will be shown. The results show that the proposed ARC algorithm achieves better tracking performance than existing ones, validating the effectiveness of the proposed approach in practical applications.

A Modified Dugoff Tire Model for Combined-slip Forces

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Vehicle Dynamics ◽  
Tire Model ◽  
Magic Formula ◽  
Model Based ◽  
Proposed Model ◽  
Road Friction ◽  
On Line ◽  
Double Lane Change ◽  
Tire Forces ◽  
Tire Models

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.

Heuristic Algorithms for Waste Load Allocation in a River Basin

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1057-1064 ◽  
Author(s):  
Vijay Joshi ◽  
Prasad Modak
Keyword(s):  
Dynamic Programming ◽  
Heuristic Algorithms ◽  
Water Quality Management ◽  
User Preferences ◽  
Theory And Practice ◽  
Waste Load Allocation ◽  
Practical Applications ◽  
Waste Load ◽  
On Line ◽  
The One

Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

scholarly journals Electrostatic Sensor Application for On-Line Monitoring of Wind Turbine Gearboxes

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3574 ◽  
Author(s):  
Huijie Mao ◽  
Hongfu Zuo ◽  
Han Wang
Keyword(s):  
Wind Turbine ◽  
Wear Debris ◽  
Bench Test ◽  
Recognition Method ◽  
Wind Turbine Gearbox ◽  
Practical Applications ◽  
Electrostatic Induction ◽  
Oil Flow ◽  
On Line ◽  
Application Potential

The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The aim of this work was to investigate in detail the application potential of the electrostatic sensor for wind turbine gearboxes. Firstly, a wear debris recognition method based on the electrostatic sensor with two-probes is proposed. Further, with the wind turbine gearbox bench test, the performance of the electrostatic sensor and the effectiveness of the debris recognition method are comprehensively evaluated. The test demonstrates that the electrostatic sensor is capable of monitoring the debris and indicating the abnormality of the gearbox effectively using the proposed method. Moreover, the test also reveals that the background signal of the electrostatic sensor is related to the oil temperature and oil flow rate, but has no relationship to the working conditions of the gearbox. This research brings the electrostatic sensor closer to practical applications.

Internal and Consulting Information Flows in the Process of Knowledge Accumulation

2019 ◽  
Vol 15 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Wiliam Acar ◽  
Rami al-Gharaibeh
Keyword(s):  
Knowledge Creation ◽  
Practical Applications ◽  
Seci Model ◽  
Top Managers ◽  
Deductive Synthesis ◽  
Proposed Model ◽  
Control Framework ◽  
Recent Developments ◽  
And Control ◽  
Knowledge Hierarchy

Practical applications of knowledge management are hindered by a lack of linkage between the accepted data-information-knowledge hierarchy with using pragmatic approaches. Specifically, the authors seek to clarify the use of the tacit-explicit dichotomy with a deductive synthesis of complementary concepts. The authors review appropriate segments of the KM/OL literature with an emphasis on the SECI model of Nonaka and Takeuchi. Looking beyond equating the sharing of knowledge with mere socialization, the authors deduce from more recent developments a knowledge creation, nurturing and control framework. Based on a cyclic and upward-spiraling data-information-knowledge structure, the authors' proposed model affords top managers and their consultants opportunities for capturing, debating and storing richer information – as well as monitoring their progress and controlling their learning process.

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