FIGURE 2.10 Real-time simulation: hybrid structures. (a)
Hardware-in-the-loop simulation. (b) Control prototyping. and the sensors are simulated. The reason is that actuators and the control hardware very often form one integrated subsystem or that actuators are difficult to model precisely and to simulate in real time. (The use of real sensors together with a simulated process may require considerable realization efforts, because the physical sensor input does not exist and must be generated artificially.) In order to change or redesign some functions of the control hardware or software, a bypass unit can be connected to the basic control hardware. Hence, hardware-in-the-loop simulators may also contain partially simulated (emulated) control functions. The advantages of the hardware-in-the-loop simulation are generally: design and testing of the control hardware and software without operating a real process (―moving the process field into the laboratory‖); testing of the control hardware and software under extreme environmental conditions in the laboratory (e.g., high/low temperature, high accelerations and mechanical shocks, aggressive media, electro-magnetic compatibility); . testing of the effects of faults and failures of actuators, sensors, and computers on the overall system;operating and testing of extreme and dangerous operating conditions; reproducible experiments, frequently repeatable; easy operation with different man-machine interfaces (cockpit-design and training of operators); and saving of cost and development time. Control Prototyping For the design and testing of complex control systems and their algorithms under real-time constraints, a real-time controller simulation (emulation) with hardware (e.g., off-the-shelf signal processor) other than the final series production hardware (e.g., special ASICS) may be performed. The process, the actuators, and sensors can then be real. This is called control prototyping (Fig. 2.10(b)). However, parts of the process or actuators may be simulated, resulting in a mixture of
HIL-simulation and control prototyping. The advantages are mainly: early development of signal processing methods, process models, and control system structure, including algorithms with high level software and high performance off-the-shelf hardware; testing of signal processing and control systems, together with other design of actuators, process parts, and sensor technology, in order to create synergetic effects; .2002 CRC Press LLC
图2.10:混合结构的实时仿真。(一)半实物仿真。(二)控制原型。和传感器模拟。原因是,执行器和控制硬件往往形成一个综合子系统或执行器难以精确模型和实时模拟。(使用真正的传感器与模拟过程可能需要相当大的努力实现,因为物理传感器输入不存在,必须人为地产生。)为了改变或重新设计的某些功能的控制硬件或软件,一个旁路装置可以连接到基本控制硬件。因此,半实物仿真也可能包含部分模拟(仿真)控制功能。优势的半实物仿真一般是:设计和测试控制的硬件和软件操作过程(―移动过程领域的实验室‖);检测控制硬件和软件的极端环境条件下在实验室(例如,高/低温,高加速度和机械冲击,积极媒体,电磁兼容性);。测试的影响,错误和失败执行器,传感器,和计算机对整个系统的运行和测试;极端和危险的工作条件;重复性实验,经常重复的;易于操作的人机界面(cockpit-design和操作人员培训);和节省开发时间和成本。控制原型的设计和测试复杂的控制系统及其算法实时限制,实时控制器模拟(仿真)和硬件(例如,现成的信号处理器)比其他的最后系列生产硬件(例如,专用的ASIC)可以执行。这个过程中,执行器和传感器,可以是真实的。这被称为控制原型(图2.10(a))。然而,部分过程或执行器可以模拟,产生一个混合hil-simulation和控制原型。其优点是:早期发展的信号处理方法,过程模型,控制系统结构,包括算法具有高水平和高性能现成的硬件;测试信号处理和控制系统,与其他设计机构,过程,和传感器技术,以便产生协同效应.2002 CRC出版社有限责任公司
.reduction of models and algorithms to meet the requirements of
cheaper mass production hardware; and defining the specifications for final hardware and software. Some of the advantages of HIL-simulation also hold for control prototyping. Some references for real-time simulation are [48,49].
减少模型和算法。符合要求的便宜批量生产五金;和确定的规格最
终硬件和软件。一些优点hil-simulation保持控制原型。一些参考实时仿真[48,49]
References 参考资料
1. Kyura, N. and Oho, H., Mechatronics—an industrial perspective.
IEEE/ASME Transactions on Mechatronics, 1(1):10–15.
2. Schweitzer, G., Mechatronik-Aufgaben und L.sungen.
VDI-Berichte Nr. 787. VDI-Verlag, Düsseldorf, 1989.
3. Ovaska, S. J., Electronics and information technology in high
range elevator systems. Mechatronics, 2(1):89–99, 1992.
4. IEEE/ASME Transactions on Mechatronics, 1996.
5. Harashima, F., Tomizuka, M., and Fukuda, T.,
Mechatronics—―What is it, why and how?‖ An editorial. IEEE/ASME Transactions on Mechatronics, 1(1):1–4, 1996.
6. Schweitzer, G., Mechatronics—a concept with examples in
active magnetic bearings. Mechatronics, 2(1):65–74, 1992.
7. Gausemeier, J., Brexel, D., Frank, Th., and Humpert, A.,
Integrated product development. In Third Conf. Mechatronics and Robotics, Paderborn, Germany, Okt. 4–6, 1995. Teubner, Stuttgart, 1995.
8. Isermann, R., Modeling and design methodology for
mechatronic systems. IEEE/ASME Transactions on Mechatronics, 1(1):16–28, 1996.
9. Mechatronics: An International Journal. Aims and Scope.
Pergamon Press, Oxford, 1991.
10. Mechatronics Systems Engineering: International Journal on
Design and Application of Integrated Electromechanical Systems. Kluwer Academic Publishers, Nethol, 1993.
11. IEE, Mechatronics: Designing intelligent machines. In Proc.
IEE-Int. Conf. 12–13 Sep., Univ. of Cambridge, 1990.
12. Hiller, M. (ed.), Second Conf. Mechatronics and Robotics.
September 27–29, Duisburg/Moers, Germany, 1993. Moers, IMECH, 1993.
13. Isermann, R. (ed.), Integrierte mechanisch elektroni-sche
Systeme. March 2–3, Darmstadt, Germany, 1993. Fortschr.-Ber. VDI Reihe 12 Nr. 179. VDI-Verlag, Düsseldorf, 1993.
14. Lückel, J. (ed.), Third Conf. Mechatronics and Robotics,
Paderborn, Germany, Oct. 4–6, 1995.Teubner, Stuttgart, 1995.
15. Kaynak, O., .zkan, M., Bekiroglu, N., and Tunay, I. (eds.),
Recent advances in mechatronics. In Proc. Int. Conf. Recent Advances in Mechatronics, August 14–16, 1995, Istanbul, Turkey.
16. Kitaura, K., Industrial mechatronics. New East Business Ltd.,
in Japanese, 1991.
17. Bradley, D. A., Dawson, D., Burd, D., and Loader, A. J.,
Mechatronics-Electronics in Products and Processes. Chapman and Hall, London, 1991.
18. McConaill, P. A., Drews, P., and Robrock, K. H., Mechatronics
and Robotics I. IOS-Press, Amsterdam, 1991.
19. Isermann, R., Mechatronische Systeme. Springer, Berlin,
1999.
20. Isermann, R., Lachmann, K. H., and Matko, D., Adaptive
Control Systems, Prentice-Hall, London, 1992.
21. Isermann, R., Supervision, fault detection and fault diagnosis
methods—advanced methods and applications. In Proc. XIV IMEKO World Congress, Vol. 1, pp. 1–28, Tampere, Finland, 1997.
22. Isermann, R., Supervision, fault detection and fault diagnosis
methods—an introduction, special section on supervision, fault detection and diagnosis. Control Engineering Practice, 5(5):639–652, 1997.
23. Isermann, R. (ed.), Special section on supervision, fault
detection and diagnosis. Control Engineering Practice, 5(5):1997. .2002 CRC Press LLC
24. Saridis, G. N., Self Organizing Control of Stochastic Systems.
Marcel Dekker, New York, 1977.
25. Saridis, G. N. and Valavanis, K. P., Analytical design of
intelligent machines. Automatica, 24:123– 133, 1988.
26. .str.m, K. J., Intelligent control. In Proc. European Control Conf.,
Grenoble, 1991.
27. White, D. A. and Sofge, D. A. (eds.), Handbook of Intelligent
Control. Van Norstrad, Reinhold,
New York, 1992.
28. Antaklis, P., Defining intelligent control. IEEE Control Systems,
Vol. June: 4–66, 1994.
29. Gupta, M. M. and Sinha, N. K., Intelligent Control Systems.
IEEE-Press, New York, 1996.
30. Harris, C. J. (ed.), Advances in Intelligent Control. Taylor &
Francis, London, 1994.
31. Otter, M. and Gruebel, G., Direct physical modeling and
automatic code generation for mechatronics simulation. In Proc. 2nd Conf. Mechatronics and Robotics, Duisburg, Sep. 27–29, IMECH, Moers, 1993.
32. Elmquist, H., Object-oriented modeling and automatic formula
manipulation in Dymola, Scandin. Simul. Society SIMS, June, Kongsberg, 1993.
33. Hiller, M., Modelling, simulation and control design for large
and heavy manipulators. In Proc. Int. Conf. Recent Advances in Mechatronics. 1:78–85, Istanbul, Turkey, 1995.
34. James, J., Cellier, F., Pang, G., Gray, J., and Mattson, S. E.,
The state of computer-aided control
system design (CACSD). IEEE Transactions on Control Systems,
Special Issue, April 6–7 (1995).
35. Otter, M. and Elmqvist, H., Energy flow modeling of
mechatronic systems via object diagrams. In Proc. 2nd MATHMOD, Vienna, 705–710, 1997.
36. Paynter, H. M., Analysis and Design of Engineering Systems.
MIT Press, Cambridge, 1961.
37. MacFarlane, A. G. J., Engineering Systems Analysis. G. G.
Harrop, Cambridge, 1964.
38. Wellstead, P. E., Introduction to Physical System Modelling.
Academic Press, London, 1979.
39. Karnopp, D. C., Margolis, D. L., and Rosenberg, R. C., System
Dynamics. A Unified Approach. J. Wiley, New York, 1990.
40. Cellier, F. E., Continuous System Modelling. Springer, Berlin,
1991.
41. Gawtrop, F. E. and Smith, L., Metamodelling: Bond Graphs
and Dynamic Systems. Prentice-Hall, London, 1996.
42. Eykhoff, P., System Identification. John Wiley & Sons, London,
1974.
43. Elmqvist, H., A structured model language for large continuous
systems. Ph.D. Dissertation, Report CODEN: LUTFD2/(TFRT-1015) Dept. of Aut. Control, Lund Institute of Technology, Sweden, 1978.
44. Elmqvist, H. and Mattson, S. E., Simulator for dynamical
systems using graphics and equations for modeling. IEEE Control Systems Magazine, 9(1):53–58, 1989.
45. Isermann, R., Identifikation dynamischer Systeme. 2nd Ed.,
Vol. 1 and 2. Springer, Berlin, 1992.
46. Ljung, L., System Identification: Theory for the User.
Prentice-Hall, Englewood Cliffs, NJ, 1987.
47. Isermann, R., Ernst, S., and Nelles, O., Identification with
dynamic neural networks—architectures, comparisons, applications—Plenary. In Proc. IFAC Symp. System Identification (SYSID’97), Vol. 3, pp. 997–1022, Fukuoka, Japan, 1997.
48. Hanselmann, H., Hardware-in-the-loop simulation as a
standard approach for development, customization, and production test, SAE 930207, 1993.
49. Isermann, R., Schaffnit, J., and Sinsel, S.,
Hardware-in-the-loop simulation for the design and testing of engine control systems. Control Engineering Practice, 7(7):643–653, 1999. .2002 CRC Press LLC
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