Abstract

The application of computer aided design techniques to product design and development is now common place throughout most areas of industry. There are many techniques and systems available either as general tools or specific tailor made solutions. The function of these tools normally falls into two main categories, these are visualisation techniques and structural analysis. Visualisation techniques may range from simple computer drawing packages to complex virtual reality systems, structural design and analysis techniques include methods such as finite element analysis. This paper will describe the next logical stage in this process and that is the modelling of the dynamic behaviour of products and systems. The paper will describe the development of such models and include a discussion of the techniques involved. The integration of these design methodologies in a mechatronic design process will be discussed and the results of two real studies will be included.

Introduction

In recent years engineering systems have become ever more complex and greater performance demands have been placed on them. This coupled with shorter design cycles has meant that a greater reliance is placed on testing during the design stage. This testing requires the modelling and verification of not only the static physical performance of a piece of equipment but its dynamic performance. This process is not however limited to new designs but can be used to optimise existing designs. This is also within the Mechatronic methodology for the product development process.

The Mechatronic and Engineering Design Process

The success and the profitability of products and processes in the world market is increasingly dependent upon the ability to utilise available technologies and to integrate engineering disciplines such as electrical/electronics, computing and optical technologies into a wide range of primarily mechanical artifacts. Traditional over the wall sequential process philosophy is fast being replaced by adopting this integrated and interdisciplinary approach at the design phase and is referred to as Mechatronics. The multi-disciplinary activity of integrating Mechanical, Electrical/Electronic and Computer Engineering in the development of enhanced technological products, can and does lead to significant improvements in performance and reduced development time and costs. Many every day products have benefited from this approach - machine tools, process automation, robotics, road vehicles, domestic products and bioengineering.

In Japan, mechatronics has evolved and is being driven by a combination of social, economic and market needs. To remain competitive businesses must manufacture high added value products with, improved performance, increased variety and functionality, greater reliability with shortened time to market and reduced direct labour requirements[1].

The traditional sequential design process necessitates continual feedback to the design phase in the product life cycle to obtain a final marketable product and results in a long lead time. This is certainly true for businesses whose products rely heavily on prototype build and test for example Daresbury Rutherford Laboratory and GEC Alsthom. The key to success lies in the integration of a variety of enabling technologies for both products and processes. The use of system modelling assists in the application of the appropriate technology to the product development process.

System modelling

Any engineering equipment or device may be represented as a system which responds to input stimuli and produces predictable responses or outputs. The systems involved may be broken down into their component parts and mathematical expressions developed to simulate the relationship between outputs and inputs. This mathematical relationship is referred to as the transfer function. The decomposition of an engineering system into its component parts has a number of advantages, the development of the complete system model is greatly simplified, the effects of each part of the system may be quantified and the effect of modifications can be easily tested[2]. The power of modern computer simulation packages also further simplifies this process and allows complex systems to be modelled efficiently. The package used in the work described in this paper is Simulink which is a graphical extension to the Mathlab mathematical processing package. This allows system elements to be represented directly in block diagram form.

Case Study 1: A Servo Control System[3]

Introduction:

This study involved the analysis of a monochromator servo control system used at the Daresbury laboratory. The Synchrotron Radiation Source (SRS) at the Daresbury Laboratory is a source of high energy radiation from infra-red to hard X-rays. In the SRS, electrons and positrons are accelerated, in a circular path, to close to the speed of light. The circular path is controlled by dipole magnets and as the charged particles pass through the field of the magnets a continuous spectrum of radiation is emitted. The spectral range of the radiation depends on the energy of the particles and the field strength of the magnets. The resulting energy is highly columnated and linearly polarised. The generated radiation is broadband but normally radiation of a single frequency is required, the desired frequency is selected using a crystal monochromator.

A crystal monochromator consists of two slabs of silicon mounted on a rotating axle in a high vacuum chamber. The SRS radiation beam passes between the slabs and by varying the angle of the crystal to the beam, then according to the Bragg angle, wavelengths are either absorbed or reflected. The positioning of the monochromator relative to the radiation beam is critical and requires accuracies of the order of 0.1 milli-degrees. The system is required to produce very small angular increments and to scan at a constant velocity. The monochromator is constructed around a precision rotary axle with a high vacuum feedthrough system. The axle is driven by a DC servo motor and the position of the stage is monitored by two encoders. One is mounted on the motor shaft and the second, a Heidenheim ROD 800 rotary encoder, is coupled by a precision coupling to the monochromator axle. The mechanism is controlled by a McLennon PIDF controller. The existing system was set up empirically during installation but the true interrelationships of the elements of the system were unknown. A model of the system was developed to rectify this and to allow optimisation of the control system parameters.

Developing the Model:

For the development of the model the system was considered as two separate elements, the controller and the plant. A Simulink model of the Mc Lennon PIDF controller was supplied by the manufacturers for direct use in the system model. The plant model was further divided into the load (monochromator rotary stage) and the servo motor system. To model the load the equation of motion for the entire monochromator system was derived. The components of the system included the motor armature, gear box, rotary table, drive shaft and the mounting plate for the crystals. For each of the plant components the inertia (J) and viscous friction (f) were calculated along with the gear box ratios. A high level of viscous friction is present in the system due to the vacuum seals fitted where the axle passes through the wall of the vacuum chamber. The after derivation of the transfer functions for the monochromator axle, motor and servo amplifier the total plant model, including velocity feedback was developed. After calculation of the system constants the model was programmed into Simulink and combined with the controller model.

Testing the model:

The model was used to allow the system to be tested with various different parameters and the limits of stability and performance investigated. To validate the model a number of comparisons were performed with the real system. Overall the agreement between the model and the real system was good but instability occurred in the real system at lower gains than predicted by the model. This was probably due to errors in the viscous friction of the system. The value used in the model was extrapolated from experimental data provided by the controller manufacturer.

Conclusions:

The most significant results obtained so far are that the major effect on operation of the system is the motor armature inertia. This suggests that system performance could be improved by the installation of a lighter motor rather than a bigger more powerful one. Further work is now in progress to improve the model to provide a closer correlation to the real system. A further mechanical factor that has been identified is wind up in the monochromator axle which is to be included in the model.

Case Study 2: A Railway locomotive[4].

Introduction:

A similar modelling approach has been applied to the simulation of the dynamic behaviour of a locomotive traction system. The primary reason for the project was to discover whether transverse vibration of the locomotive body, known to be about 8Hz would affect the control system which has a dynamic response of about 5-6Hz. The project was carried out in conjunction with GEC Alsthom.

Developing and Testing the Model:

The model was developed in two parts, the locomotive body and the traction control system. The body was considered as two rigid links pivoted around the bogie mounting points, the bogies being modeled separately. The motors and traction control system were then developed and combined with the locomotive model. For the purposes of this work it was assumed that the two rails were identical so only one rail was modeled. The resulting model was programmed into the Mathlab environment for testing and analysis. The model was tested by simulating the effect of the locomotive encountering a 2cm step whilst travelling at 30Km/hr.

Results:

The results of the simulations showed that the traction system was capable of controlling the fluctuations in motor torque present due to the breakthrough of transverse body vibrations.

Conclusion

The case studies described in this paper have shown how systems can be modeled and tested dynamically. In the cases studied systems already in existence and under design have been modeled and tested. A major advantage of systems modelling is that when applied to systems already in existence the effect of modifications can be tested without the risk of damage to, and loss of use of, the equipment. These case studies also highlight the importance of the mechatronic design methodology where the interaction of all system components must be considered from the earliest design stage. This approach reduces lead times and ensures long term product reliability.

Acknowledgement

The authors would like to thank Dr Barry Dobson and all the members of the Daresbury Laboratory staff involved in this work for their help and advice with the first case study. We would also like to thank the staff of GEC Altsthom for their assistance in the second case study.

References

1. "Mechatronics - the Japanese Way". IMedchE Report on the Overseas Science and Technology Mission to Japan, November 1993.

2. Gautier M,. Robet P. and Bergmann C., "Modelling and Simulation of DC Motor-Chopper Drive for Robots" IEEE, Vol 5. 1993, pp 212-217

3. J Demille, "Modelling of a Servo Control System", MSc. Thesis, De Montfort 'University, 1994.

4. J Macdonald, ":Aspects of Linearized mathematical Modelling & Simulation of a Locomotive, MSc. Thesis. de Montfort University, 1994.

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