Robotics, rapid control prototyping and "dSpace" hardware.
Dolga, Valer ; Dolga, Lia ; Filipescu, Hannelore 等
1. INTRODUCTION
Intensive and extensive development of robotics involved various
approaches for designing and improving controllers that are particularly
destined to robot systems. Nonlinear features with many uncertainties
characterize the comprehensive inquiries of the mechatronic system
embodied by the robotic manipulator. Conventional methodologies for
controller development became insufficient: either they hardly solved
the problem, or they did not find a solution for dynamic modelling.
Designing a feedback control system typically requires several
steps: identifying a dynamic model from experimental data, using
computer aided design tools to construct the controller, verifying the
design using computer simulations and finally, implementing the
controller. Knowledgeable reference articles and books propose a
solution for the specified question: a multivariable non-linear coupled
dynamic system with uncertainties (Tagagi, 1992)--(Neo & Er, 1996).
The experts' investigations regarded both controller design
methodologies that should consider various uncertainties--dynamic fuzzy
neural networks controller (Low, 2004) and rapid prototyping of
model-based robot controllers (Bona, 2003) or rapid prototyping with
dSPACE hardware (Ridley, 2004; Gattringer, 2006; Ionescu et al., 2008).
The subject of rapid control prototyping was one of the main tasks
within the frame of the national excellence research grant
"Simulation, Control and Testing Platform with Applications in
Mechatronics "ConMec"" (Dolga et al., 2007).
The paper presents the authors' realizations in developing
applicative platforms with comprehensive tasks of rapid control
prototyping for robotic systems.
2. RAPID CONTROL PROTOTYPING APPROACH
The idiom of "rapid prototyping" is extensive and
outlines remarkable facilities in creating new products; it provides the
possibility to demonstrate the veracity of a concept or an idea.
Previous studies outlined the superiority of hardware solutions, for
which several variants were considered. The authors applied a
multi-attribute decision making process with six evaluation criteria
(the processor type, the memory, the inputs and the outputs, the signal
conditioning, the operating mode and the platform sensitivity). The
results revealed the dSPACE framework (Dolga et al., 2007) advantages,
because dSPACE equipment perfectly matches the requisite of using the
same software and hardware platform for controller design, simulation
and implementation processes (Figure 1).
Figure 2 shows a typical software development process for an
Electronics Control Unit (ECU).
Prototyping can help answer the following questions: will the
design work properly, can the design be produced economically, which
approach can be taken to get from concept to product, how can
prototyping support product design specification. The main benefits of
prototyping are: better communication with the user, improved design
through feedback and iteration, provides training / learning medium.
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3. SOLUTIONS FOR ANALYSIS PLATFORMS
The first developed platform allows applying the concept of rapid
control prototyping for a multi-axes robot (Figure 3).
While Matlab and the SIMULINK block diagram environment are useful
for control design and analysis, the dSPACE-DS1005 provides the means
for acquiring system identification data and implementing discrete-time
controllers for analogue plants (a 5 axes Yamaha robot was bought). The
dSPACE software ControlDesk is a graphical user interface that offers
the functions to control monitor and automate experiments and make the
controllers progress more efficient.
Another platform with a similar goal was focused on the analysis of
an autonomous mobile robot which operates in obstructive environments
(Figure 4). Therefore, the controller takes into consideration multiple
uncertainty elements; for that reason, a fuzzy algorithm was provided
with the aim of developing an appropriate controller (Figure 5).
The sensing elements supply the data required by the fuzzy
controller. The CCD video sensor connected to a computer system together
with the data processing in Matlab/ Image Processing Toolbox deliver the
desired position [PHI]i. The mobile robot position transducer gives the
real navigation angle [PHI].
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
[FIGURE 5 OMITTED]
The mobile robot is provided with a multiple use of the sensing
elements (video, proximity, position). The interface RS232 aids the
implementation of a control strategy compatible with the robot
environment or the upload of the control code. The dSPACE 1005 board
interfaces the computer and the robot.
4. CONCLUSION
The created platforms offer a concrete answer to a critical
requirement in robotics control: rapid control prototyping.
The modular structures of the platforms make the developed
applications to be supple and friendly and guarantee excellent results
in rapid control prototyping for various mechatronic systems. An
integrated real-time hardware-in-the-loop simulation system becomes
available and executable code for microcontroller can be automatically
generated.
To date only Matlab/ Simulink tools were used on the platforms;
other software environments for dynamic simulations are also available
(Dymola, 20SIM) and will be considered to increase the platforms
applicability.
The intention is of further developing applications that suppose
implementations of appropriate controllers. Analyses for levitation mechatronic systems and for shape memory alloys actuators are planned to
be approached.
The described platforms for rapid control prototyping offer high
flexibility; next research will make possible to apply concepts of
advanced control like "full path" and "bypass".
5. REFERENCES
Bona, B.; Indri, M. & Smaldone, N. (2003). Architectures for
rapid prototyping of model-based robot controllers, In: Advances in
control of articulated and mobile robots, ISBN 978-3-540-20783-2,
Springer Berlin, Heidelberg
Dolga, L.; Dolga, V. & Filipescu, H. (2007). Rapid prototyping
within the simulation and control platform for mechatronics, Proceedings
of the 18th International DAAAM Symposium "Intelligent
Manufacturing & Automation: Focus on Creativity, Responsibility and
Ethics of Engineers", pp. 243-244, Zadar, Oct. 2007
Gattringer, H. (2006). The Bipedal Robot. dSPACE News, no.1, 2006,
pp.12-13, Paderborn, Germany
Ionescu, F. (2008). Vlad, C. & Arotaritei, D.: Advanced Control
of an Electrohydraulic Axis, In: Mechatronic System Control, Logic and
Data Acquisition, edited by R.H. Bishop, CRC Press, ISBN
9260-0-8493-9260-8, 2008
Low, C. B.; Nah, K. H. & Er, M.J. (2004). Real -Time
implementation of a dynamic fuzzy neural networks controller for a
SCARA. Journal of the Institution of Engineers, vol.44 issue 3 pp.
43-56, Singapore
Ridley, P. (2004). The World's Largest Industrial Robot.
dSPACE News, no. 2, 2004, pp.4 -5, Paderborn, Germany
