2.5 Concurrent Design Procedure for Mechatronic Systems

The design of mechatronic systems requires a systematic development and use of modern design tools.

Design Steps

Table 2.3 shows five important development steps for mechatronic systems, starting from a purely mechanical system and resulting in a fully integrated mechatronic system. Depending on the kind of mechanical system, the intensity of the single development steps is different. For precision mechanical devices, fairly integrated mechatronic systems do exist. The influence of the electronics on mechanical elements may be considerable, as shown by adaptive dampers, anti-lock system brakes, and automatic gears. However, complete machines and vehicles show first a mechatronic design of their elements, and then slowly a redesign of parts of the overall structure as can be observed in the development of machine tools, robots, and vehicle bodies.

Required CAD CAE Tools

The computer aided development of mechatronic systems comprises:

1. constructive specification in the engineering development stage using CAD and CAE tools,

2. model building for obtaining static and dynamic process models,

3. transformation into computer codes for system simulation, and

4. programming and implementation of the final mechatronic software.

Some software tools are described in [31]. A broad range of CAD/CAE tools is available for 2D- and 3D-mechanical design, such as Auto CAD with a direct link to CAM (computer-aided manufacturing), and PADS, for multilayer, printed-circuit board layout. However, the state of computer-aided modeling is not as advanced. Object-oriented languages such as DYMOLA and MOBILE for modeling of large combined systems are described in [31–33]. These packages are based on specified ordinary differential

equations, algebraic equations, and discontinuities. A recent description of the state of computer-aided control system design can be found in [34]. For system simulation (and controller design), a variety of program systems exist, like ACSL, SIMPACK, MATLAB/SIMULINK, and MATRIX-X. These simulation techniques are valuable tools for design, as they allow the designer to study the interaction of components and the variations of design parameters before manufacturing. They are, in general, not suitable for real-time simulation.

Modeling Procedure

Mathematical process models for static and dynamic behavior are required for various steps in the design of mechatronic systems, such as simulation, control design, and reconstruction of variables. Two ways to obtain these models are theoretical modeling based on first (physical) principles and experimental modeling (identification) with measured input and output variables. A basic problem of theoretical modeling of mechatronic systems is that the components originate from different domains. There exists a well-developed domain specific knowledge for the modeling of electrical circuits, multibody mechanical systems, or hydraulic systems, and corresponding software packages. However, a computer-assisted general methodology for the modeling and simulation of components from different domains is still missing [35].

The basic principles of theoretical modeling for system with energy flow are known and can be unified for components from different domains as electrical, mechanical, and thermal (see [36–41]). The mod-eling methodology becomes more involved if material flows are incorporated as for fluidics, thermody-namics, and chemical processes.

A general procedure for theoretical modeling of lumped parameter processes can be sketched as follows [19].

1. Definition of flows

• energy flow (electrical, mechanical, thermal conductance)

• energy and material flow (fluidic, thermal transfer, thermodynamic, chemical)

2. Definition of process elements: flow diagrams

• sources, sinks (dissipative)

• storages, transformers, converters

3. Graphical representation of the process model

• multi-port diagrams (terminals, flows, and potentials, or across and through variables)

• block diagrams for signal flow

• bond graphs for energy flow

4. Statement of equations for all process elements

(i) Balance equations for storage (mass, energy, momentum)

(ii)Constitutive equations for process elements (sources, transformers, converters)(iii)Phenomenological laws for irreversible processes (dissipative systems: sinks)

5. Interconnection equations for the process elements

• continuity equations for parallel connections (node law)

• compatibility equations for serial connections (closed circuit law)

6. Overall process model calculation

• establishment of input and output variables

• state space representation

• input/output models (differential equations, transfer functions)

An example of steps 1–3 is shown in Fig. 2.7 for a drive-by-wire vehicle. A unified approach for processes with energy flow is known for electrical, mechanical, and hydraulic processes with incompressible fluids. Table 2.4 defines generalized through and across variables.

In these cases, the product of the through and across variable is power. This unification enabled the formulation of the standard bond graph modeling [39]. Also, for hydraulic processes with compressible fluids and thermal processes, these variables can be defined to result in powers, as seen in Table 2.4. However, using mass flows and heat flows is not engineering practice. If these variables are used, so-called pseudo bond graphs with special laws result, leaving the simplicity of standard bond graphs. Bond graphs lead to a high-level abstraction, have less flexibility, and need additional effort to generate simulation algorithms. Therefore, they are not the ideal tool for mechatronic systems [35]. Also, the tedious work needed to establish block diagrams with an early definition of causal input/output blocks is not suitable.

Development towards object-oriented modeling is on the way, where objects with terminals (cuts) are defined without assuming a causality in this basic state. Then, object diagrams are graphically represented, retaining an intuitive understanding of the original physical components [43,44]. Hence, theoretical modeling of mechatronic systems with a unified, transparent, and flexible procedure (from the basic components of different domains to simulation) are a challenge for further development. Many compo­nents show nonlinear behavior and nonlinearities (friction and backlash). For more complex process parts, multidimensional mappings (e.g., combustion engines, tire behavior) must be integrated.

For verification of theoretical models, several well-known identification methods can be used, such as correlation analysis and frequency response measurement, or Fourier- and spectral analysis. Since some parameters are unknown or changed with time, parameter estimation methods can be applied, both, for models with continuous time or discrete time (especially if the models are linear in the parameters) [42,45,46]. For the identification and approximation of nonlinear, multi-dimensional characteristics,

TABLE 2.4 Generalized Through and Across Variables for Processes with Energy Flow

System	Through Variables		Across Variables
Electrical Magnetic Mechanical	Electric current Magnetic Flow	I F	Electric voltage Magnetic force	U Q
• translation • rotation Hydraulic Thermodynamic	Force Torque Volume flow Entropy flow	F M V˙	Velocity Rotational speed Pressure Temperature	w ω p T

FIGURE 2.7 Different schemes for an automobile (as required for drive-by-wire-longitudinal control): (a) scheme

of the components (construction map), (b) energy flow diagram (simplified), (c) multi-port diagram with flows and

potentials, (d) signal flow diagram for multi-ports.

artificial neural networks (multilayer perceptrons or radial-basis-functions) can be expanded for non­linear dynamic processes [47].

Real-Time Simulation

Increasingly, real-time simulation is applied to the design of mechatronic systems. This is especially true if the process, the hardware, and the software are developed simultaneously in order to minimize iterative development cycles and to meet short time-to-market schedules. With regard to the required speed of computation simulation methods, it can be subdivided into

1. simulation without (hard) time limitation,

2. real-time simulation, and

3. simulation faster than real-time.

Some application examples are given in Fig. 2.8. Herewith, real-time simulation means that the simulation of a component is performed such that the input and output signals show the same time-dependent values as the real, dynamically operating component. This becomes a computational problem for pro­cesses which have fast dynamics compared to the required algorithms and calculation speed.

FIGURE 2.8 Classification of simulation methods with regard to speed and application examples.

FIGURE 2.9 Classification of real-time simulation.

Different kinds of real-time simulation methods are shown in Fig. 2.9. The reason for the real-time requirement is mostly that one part of the investigated system is not simulated but real. Three cases can be distinguished:

1. The real process can be operated together with the simulated control by using hardware other than the final hardware. This is also called “control prototyping.”

2. The simulated process can be operated with the real control hardware, which is called “hardware­in-the-loop simulation.”

3. The simulated process is run with the simulated control in real time. This may be required if the final hardware is not available or if a design step before the hardware-in-the-loop simulation is considered.

Hardware-in-the-Loop Simulation

The hardware-in-the-loop simulation (HIL) is characterized by operating real components in connection with real-time simulated components. Usually, the control system hardware and software is the real system, as used for series production. The controlled process (consisting of actuators, physical processes, and sensors) can either comprise simulated components or real components, as seen in Fig. 2.10(a). In general, mixtures of the shown cases are realized. Frequently, some actuators are real and the process 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.

FIGURE 2.10 Real-time simulation: hybrid structures. (a) Hardware-in-the-loop simulation. (b) Control prototyping.

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;

• 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].

References

1. Kyura, N. and Oho, H., Mechatronics—an industrial perspective. IEEE/ASME Transactions on Mecha-tronics, 1(1):10–15.

2. Schweitzer, G., Mechatronik-Aufgaben und Lösungen. 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 Transac-tions 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., Özkan, 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 Engineer-ing Practice, 5(5):1997.

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.Åström, 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.

3

System Interfacing, Instrumentation, and Control Systems

3.1 Introduction The Mechatronic System • A Home/Office Example