Television – Basic receiver with additional function – For display of additional information
Patent
1994-06-09
1997-01-28
Masih, Karen
Television
Basic receiver with additional function
For display of additional information
31856813, 31856815, 31856823, 31856821, 395900, 395 21, 395 20, 36447412, 36447415, 364151, G05B 19408
Patent
active
055980767
DESCRIPTION:
BRIEF SUMMARY
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to a process for optimizing the control parameters of a system by measuring differences between an actual behavior and a desired behavior.
2. Description of the Related Art
In modern manufacturing equipment, for reasons of cost, time and staff savings, manufacturing systems are frequently employed which are very complex and the correct functioning of which is dependent on a multiplicity of control parameters. It is particularly important here that such control parameters that bring about a correct actual behavior in relation to the reference behavior of such a system are available as a function of time. This means that the parameters must be such that the actual behavior of the system corresponds as closely as possible to the reference behavior.
Some examples of such systems are: example, which is to be guided along a particular contour line of a workpiece. profile to a workpiece.
In order to be able to control such systems, it is necessary to know and describe their response characteristics very precisely. For control one can attempt to record the response characteristics of the systems in higher-order differential equations. In the case of mechanical systems, such as a robot arm for example, the differential equation would be influenced by the weight of the tool, the weight of the individual arms, the moments of inertia that occur during movements, the torques of the motors and the manner in which the individual joints and the sections of the robot arm connected thereto are positioned. It is already evident from the above that a very complex differential equation would result with the known variables. A further complicating factor is the fact that the systems, such as a robot arm for example, exhibit nonlinearities. The nonlinearities consist, for example, of the play in the joints, of the play in the speed-transforming transmissions and of the positioning imprecision of the servos. These variables are not predictable and therefore cannot be described either.
Analogously, other nonlinearities are conceivable in the case of heating controllers, such as, for example, the thermal conductivity coefficient of the insulation, the different reflection behavior of the heated material, convection influences, different ambient temperatures, etc.
In order to be able to employ such expensive investment goods, such as robots for example, in the production process for as long as possible, there are methods for determining the parameters of these systems without using a robot. In the case of robot arms which guide tools, for example, it is necessary to specify coordinates along which the robot is to guide the tool. A travel trajectory is then produced by the chronological sequence of the coordinates. One method for determining such xy coordinates is, for example, that of simulating a robot arm. In this case the model of a robot arm containing all the known variables of the robot arm is described in a computer. This description includes, for example, the geometry, the kinematic and the dynamic behavior of the robot, of the workpieces and of the machines, and also the behavior of the sensors where they are relevant to the simulation.
It is also particularly important in this connection that the control behavior of the robot is also taken into account in such simulation models.
Control parameters are then supplied to the model, xy coordinates in the case of a robot arm and possibly also z coordinates of a travel trajectory. The actual behavior of the robot arm then becomes apparent from the simulation, which can then be compared with the known reference behavior, namely the coordinates of the trajectory. The control parameters, that is to say the coordinates for the robot model, can be optimized on the basis of this comparison.
It is then possible to drive the real robot with the control parameters optimized on the model in this way. As a result of the aforesaid nonlinearities it will not have the reference behavior, that is to
REFERENCES:
patent: 5016188 (1991-05-01), Lan
patent: 5162997 (1992-11-01), Takahashi
patent: 5177625 (1993-01-01), Nakashima et al.
patent: 5268834 (1993-12-01), Sanner et al.
F. Garnich et al., "Laserrobotic for 3D cutting and welding" Proc. of the European Conference on Optics (ECO III), The Hague (NL), Mar. 1990.
E. Freund, "Fast Nonlinear Control With Arbitrary Pole-Placement For Industrial Robots And Manipulators", Int. J. of Robotics Research, vol. 1, No. 1, pp. 65-78.
C. G. Atkeson et al., "Robot Trajectory Learning Through Practice", Proc of 1986 IEEE Conf. on Robotics and Automation, pp. 1737-1742 (1986).
A. Balesdtrino et al., "An Adaptive Model Following Control For Robotic Manipulators", Trans. ASME J. of DSMC, vol. 105, pp. 143-151 (1983).
S. Dubowsky et al., "The Application of Model-Referenced Adaptive Control To Robotic Manipulators", Trans. ASME J. of DSMC, vol. 101, pp. 193-200 (1979).
J. O. Berg: Robot Calibration for Off-Line Programming. Industrial Robot, vol. 18, No. 2, 1991, pp. 29-31.
S. Arimoto: in M. Brady (ed.); Robotics Science, pp. 349-377, MIT Press (1989).
M. W. Spong: Modeling and control of Elastic Joint Robots, in F. W. Paul and K. Youcef-Toumi (eds.): Robotics: Theory and Applications, DSC-vol. 3 (presented at the winter Annual Meeting of ASME), pp. 57-65 (1986).
V. D. Sanchez, A. and G. Hirzinger: State-of-the-art Robot Learning Control based on Artificial Neural Networks--An overview. To appear in: O. Khatib et al. (ed.): The Robotics Review 2, MIT Press (1991).
J. Y. S. Luh, M. W. Walker and P. C. Paul: Online Computation Scheme for Mechanical Manipulators. ASME Trans. J. Dynam. Syst. Measure. Control, vol. 102, pp. 69-76 (1980).
N. N.: Consistent Welds Automatically: Description of Meta Torch Sensor Family. Meta Machines Ltd., Oxford (UK).
G. Leininger: Adaptive Control of Manipulators Using Self-Tuning Methods, in M. Brady and R. P. Paul (eds.): Robotics Research: First International Symposium, MIT Press (1984).
A. J. Koivo and T. H. Guo: Adaptive Linear Controller for Robotic Manipulators. IEEE Trans. on Automatic Control, Vol. AC-28, pp. 162-171(1983).
J. M. Hollerbach: A Recursive Lagrangian Formulation of Manipulator Dynamics and a Comparative Study of Dynamic FormulationComplexity. IEEE Trans. Syst. Man. Cybernet, vol. SMC-10, pp. 730-736 (1980).
E. G. Gilbert and I. J. Ha: An approach to Nonlinear Feedback Control with Application to Robotics, Proc. 22nd IEEE CDC, San Antonio (1983).
G. Casareo and R. Marino: On the Controllability Properties of Elastic Robots. 6 Int. Conf. on Analysis and Optimization of Systems, INRiA, Nice (1984).
S. Arimoto from Brady (ed): Robotics Science, MIT Press (1989), pp. 349-377.
V. D. Sanchez A. and G. Hirzinger, "State-of-the-art Robot Learning Control based on Artificial Neural Networks--An Overview--", from Okhatib et al. (ed) The Robotics Review 2 MIT Press (1991), pp. 1-15-15-15.
Bocionek Siegfried
Joppich Martin
Moller Marcus
Neubauer Werner
Masih Karen
Siemens Aktiengesellschaft
LandOfFree
Process for optimizing control parameters for a system having an does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Process for optimizing control parameters for a system having an, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Process for optimizing control parameters for a system having an will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-943357