Feedforward control is a term that has specific meaning within the field of CPU based Automatic Controls. The discipline of “feedforward controls” as it relates to modern, CPU based automatic controls is widely discussed, but is seldom practiced due to the difficulty and expense of developing or providing for the mathematical model required to facilitate this type of control. Open-loop control and feedback control (often based on canned PID control algorithms) are much more widely used [4, 32, 34]. See: http://www.isa.org/Content/ContentGroups/Motion_Control2/Departments1/Motion_Fundamentals/200227/20020421.pdf
The benefits of feedforward control are significant and can often justify the extra cost, time and effort required to implement the technology. Control accuracy can often be improved by as much as an order of magnitude if the mathematical model is of sufficient quality and implementation of the feedforward control law is well thought out. Energy consumption by the feedforward control system and its drives is typically substantially lower than with other controls. Stability is enhanced such that the controlled device can be built of lower cost, lighter weight, springier materials while still being highly accurate and able to operate at high speeds. Other benefits of Feedforward Control include reduced wear and tear on equipment, lower maintenance costs, higher reliability and a substantial reduction in hysteresis. Feedforward control is often combined with feedback control to optimize performance; see: http://www.bgu.ac.il/chem_eng/pages/Courses/oren%20courses/Chapter_9.pdf. [1,3, 30, 31]
While the term “feedforward control” has been adopted by other fields and even popularized in articles and textbooks about management, its meaning in modern controls is made clear in the following citation: “In feedforward control there is a coupling from the set point and/or from the disturbance directly to the control variable, that is, a coupling from an input signal to the control variable. The control variable adjustment is not error-based. In stead it is based on knowledge about the process in the form of a mathematical model of the process and knowledge about or measurements of the process disturbances.” From: Basic Dynamics and Control, Haugen, F, 2009, ISBN 978-82-91748-13-9
Feedforward control is distinctly different from open loop control and teleoperator systems. Feedforward control requires a mathematical model of the plant (process and/or machine being controlled) and the plant's relationship to any inputs or feedback the system might receive. Neither open loop control nor teleoperator systems require the sophistication of a mathematical model of the physical system or plant being controlled. Control based on operator input without integral processing and interpretation through a mathematical model of the system is a teleoperator system and is not considered feedforward control. [1, 25]
The mathematical model of the plant (machine, process or organism) used by the feedforward control system may be created and input by a controls engineer or it may be learned by the control system. See: http://www.tagonline.org/articles.php?id=42. Control systems capable of learning and/or adapting their mathematical model have become more practical as microprocessor speeds have increased. The discipline of modern Feedforward Controls was itself made possible by the invention of microprocessors. [1, 25]
Feedforward systems are also found in biological control by human and animal brains. One helpful article on this type of feedforward system can be found at: http://psychology.jrank.org/pages/1155/feedback-feedforward.html
Even in the case of biological feedforward systems, such as in the human brain, knowledge or a mental model of the plant (body) can be considered to be mathematical as the model is characterized by limits, rhythms, mechanics and patterns. [37, 38]
Historically, the use of the term “feedforward” is found in works by D. M. MacKay as early as 1956. While MacKay’s work is in the field of biological control theory, he speaks only of feedforward systems. MacKay does not mention “Feedforward Control” or allude to the discipline of “Feedforward Controls.” MacKay and other early writers who use the term “feedforward” are generally writing about theories of how human or animal brains work.[37] See article on motor skills.
Feedforward control requires integration of the mathematical model into the control algorithm such that it is used to determine the control actions based on what is known about the state of the system being controlled. In the case of control for a lightweight, flexible robotic arm, this could be as simple as compensating between when the robot arm is carrying a payload and when it is not. The target joint angles are adjusted to place the payload in the desired position based on knowing the deflections in the arm from the mathematical model’s interpretation of the disturbance caused by the payload. Systems that plan actions and then pass the plan to a different system for execution do not satisfy the above definition of feedforward control. Unless the system includes a means to detect a disturbance or receive an input and process that input through the mathematical model to determine the required modification to the control action, it is not true feedforward control. [38, 13,14]
The discipline of “feedforward controls” was largely developed by professors and graduate students at Georgia Tech, MIT, Stanford and Carnegie Mellon. Feedforward is not typically hyphenated in scholarly publications. Meckl and Seering of MIT and Book and Dickerson of Georgia Tech began the development of the concepts of Feedforward Control in the mid 1970s. The discipline of Feedforward Controls was well defined in many scholarly papers, articles and books by the late 1980s. [1, 2, 31, 33, all]
Feedforward control is also discussed in the field of artificial intelligence. See Feedforward neural network.
1. Oosting, K.W., Simulation of Control Strategies for a Two Degree-of-Freedom Lightweight Flexible Robotic Arm, Thesis, Georgia Institute of Technology, Dept. of Mechanical Engineering, 1987,
2. Alberts, T.E., Augmenting the Control of A Flexible Manipulator with Passive Mechanical Damping, PhD. Thesis, Georgia Institute of Technology, Dept. of Mechanical Engineering, August 1986.
3. Hastings, G.G., Controlling Flexible Manipulators, An Experimental Investigation, Ph.D. Dissertation, Dept. of Mech. Eng., Georgia Institute of Technology, August, 1986.
4. Book, W.J. and Cetinkunt, S., "Optimum Control of Flexible Robot Arms OR Fixed Paths", IEEE Conference on Decision and Control. December 1985.
5. Meckl, P.H. and Seering, W.P. /'Feedforward Control Techniques Achieve Fast Settling Time in Robots" Automatic Control Conference Proceedings. 1986, pp 58–64.
6. Sakawa, Y., Matsuno, F. and Fukushima, S., "Modeling and Feedback Control of a Flexible Arm", Journal of Robotic Systems. August 1985, pp 453–472.
7. Truckenbrodt, A., "Modeling and Control of Flexible Manipulator Structures", 4th CISM-IFToMM Symp., Warszawa, 1981.
8. Leu, M.C., Dukovski, V. and Wang, K.K., "An Analytical and Experimental Study of the Stiffness of Robot Manipulators with Parallel Mechanisms", 1985 ASME Winter Annual Meeting PRD-Vol. 15 Robotics and Manufacturing Automation, pp. 137–144
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11. Balas, M.J., "Feedback Control of Flexible Systems", IEEE Trans. on Automatic Control, Vol.AC-23, No.4, Aug. 1978, pp. 673–679.
12. Balas, M.J., "Active Control of Flexible Systems", J. of Optim. Th. and App., Vol.25, No.3, July 1978,
13. Book, W.J., Modeling, Design and Control of Flexible Manipulator Arms, PhD. Thesis, MIT, Dept. of Mech. Eng., April 1974.
14. Maizza-Neto, 0., Modal Analysis and Control of Flexible Manipulator Arms, PhD. Thesis-, MIT, Dept. of Mech. Eng., September 1974.
15. Book, W.J., Maizzo Neto, 0. and Whitney, D.E., "Feedback Control of Two Beam, Two Joint Systems With Distributed Flexibility", Journal of Dynamic Systems, Measurement and Control, Vol.97, No.4, December 1975, pp. 424–430.
16. Book, W.J., "Analysis of Massless Elastic Chains With Servo Controlled Joints", Journal of Dynamic Systems, Measurement and Control, Vol.101, September 1979, pp. 187–192.
17. Book, W.J., "Recursive Lagrangian Dynamics of Flexible Manipulator Arms Via Transformation Matrices", Carnegie-Mellon University Robotics Institute Technical Report, CMU-RI-TR-8323, Dec. 1983.
18. Hughes, P.C., "Dynamics of a Flexible Manipulator Arm for the Space Shuttle", AAS/AIAA Astrodynamics Conference, September 1977, Jackson Lake Lodge, Wyoming.
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22. Martin, G.D., On the Control of Flexible Mechanical Systems, Ph.D. Dissertation, Stanford Univ., Dept. of E.E., May 1978.
23. Zalucky, A. and Hardt, D.E., "Active Control of Robot Structure Deflections", J. of Dynamic Systems, Measurement and Control, Vol. 106, March 1984, pp. 63–69.
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25. Alberts, T.E., Sangveraphunsiri, V. and Book, Wayne J., Optimal Control of a Flexible Manipulator Arm: Volume I, Dynamic Modeling, MHRC Technical Report, MHRC-TR-85-06, Georgia Inst, of Technology, 1985.
26. Nemir, D. C, Koivo, A. J., and Kashyap, R. L., "Pseudolinks and the Self-Tuning Control of a Nonrigid Link Mechanism", Purdue University, Advance copy submitted for publication, 1987.
27. Widmann, G. R. and Ahmad, S., "Control of Industrial Robots with Flexible Joints", Purdue University, Advance copy submitted for publication, 1987.
28. Hollars, M. G., Uhlik, C. R., and Cannon, R. H., "Comparison of Decoupled and Exact Computed Torque Control for Robots with Elastic Joints", Advance copy submitted for publication, 1987.
29. Cannon, R. H. and Schmitz, E., "Initial Experiments on the End- Point Control of a Flexible One Link Robot", International Journal of Robotics Research, November 1983.
30. Oosting, K.W. and Dickerson, S.L., “Low-Cost, High Speed Automated Inspection”, 1991, Industry Report
31. Oosting, K.W. and Dickerson, S.L., “Feed Forward Control for Stabilization”, 1987, ASME
32. Oosting, K.W. and Dickerson, S.L., “Control of a Lightweight Robot Arm”, 1986, IEEE International Conference on Industrial Automation
33. Khatib and Oussama, SPRINGER HANDBOOK OF ROBOTICS, Springer Press, 2008.
34. Oosting, K.W., “Actuated Feedforward Controlled Solar Tracking System,” 2009, Patent Pending
35. Oosting, K.W., “Feedforward Control System for a Solar Tracker,” 2009, Patent Pending
36. Oosting, K.W., “Smart Solar Tracking,” July, 2010, InterSolar NA Presentation
37. MacKay, D. M. (1966): "Cerebral organization and the conscious control of action". In: J. C. Eccles (Ed.), Brain and conscious experience, Springer, pp. 422–440
38. Greene, P. H. (1969): "Seeking mathematical models of skilled actions". In: H. C. Muffley/D. Bootzin (Eds.), Biomechanics, Plenum, pp. 149–180>.