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| High performance nonlinear controller
for MEMS optical switch |
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| Neural network force controller for
robotic grinding and milling |
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Overview:
Develop
improved control systems for modern high performance
systems in aerospace, robotics, unmanned vehicles, automotive
systems, microdevices, compute hard disk drives, and
elsewhere, which are becoming more complex. Performance
requirements for these systems are becoming more rigorous
in terms of speed of response and precision of motion.
Approach:
- Use biologically inspired control system structures
based on neural networks, decision-making, and linguistic
fuzzy logic to avoid the need for exact system descriptions,
to avoid full internal state measurements, and to deal
with incomplete knowledge of disturbances.
- Formal derivation of digital control system structures
with learning features for implementation on computer
microcontroller systems.
- Feedback Linearization that allows the control of
systems with high-frequency flexible modes such as
vehicle suspension vibration, fuel slosh, tank gun
barrel vibration, aircraft flutter, computer disk drive
oscillations and windage. Neural
networks allow learning of unknown effects.
- Dynamic Inversion with a neural network learning
feature for effective compensation of actuator deadzones,
uncertainties, and disturbances.
- Nonlinear Lyapunov proof extension methods to determine
control system structure and neural network learning
schemes that guarantee closed-loop performance of intelligent
controllers in the presence of uncertainties and disturbances.
- High-level performance critic methods for updating
control system parameters through real-time learning
techniques.
- Backstepping methods using Neural Networks to learn
unknown dynamics. This allows effective control
of coupled systems with more degrees of freedom than
control inputs by using the system structure properties.
- Adaptive Fuzzy Logic methods to allow dynamic focusing
of controller awareness on regions where more performance
activity is needed.
- Actuator feedforward compensation controllers that
incorporate neural network or fuzzy logic components
to offset the effects of unknown backlash, deadzone,
and friction.
- Nearly optimal control design using solution of
nonlinear controller design equations using neural
network approximators.
- Development of robust output-feedback learning controllers
that operate effectively with reduced measurement information
about the system state.
Accomplishments:
- New learning control systems
have been developed, simulated on computers, and
implemented on DoD, vehicle, and industry platforms.
These systems significantly improve the performance
and control of modern high-performance systems.
- Development
of high performance nonlinear control system for
MEMS optical switch.
Publications:
[1] |
S. Jagannathan and F.L. Lewis, "Discrete-time tuning of neural network controllers for nonlinear dynamical systems," U.S. Patent 6,064,997, awarded 16 May 2000. |
[2] |
R. Selmic, F.L. Lewis, A.J. Calise, and M.B. McFarland, "Backlash Compensation Using Neural Network," U.S. Patent 6,611,823, Awarded 26 Aug. 2003. |
[3] |
J. Campos and F.L. Lewis, "Method for Backlash Compensation Using Discrete-Time Neural Networks," SN 60/237,580, The Univ. Texas at Arlington, awarded March 2006. |
[4] |
F.L. Lewis, S. Jagannathan, and A. Yesildirek, Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor and Francis, London, 1999. |
[5] |
F.L. Lewis, J. Campos, and R. Selmic, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities, Society of Industrial and Applied Mathematics Press, Philadelphia, 2002. |
[6] |
M. Abu-Khalaf, J. Huang, and F.L. Lewis, Nonlinear H2/H-Infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks, Springer-Verlag, Berlin, 2006. |
[7] |
M. Abu-Khalaf and F.L. Lewis, “A Neural Network Approach for Nearly Optimal Control of Constrained Nonlinear Systems,” in Intelligent Systems Using Soft Computing Methodologies, ed. A. Ruano, IEE Press, Stevenage, UK, 2005. |
[8] |
F. Hong, S.S. Ge, F.L. Lewis, and T.H. Lee, “Adaptive neural-fuzzy control of nonholonomic mobile robots, in Autonomous Mobile Robots: Sensing, Control, Decision-Making, and Applications, ed. S.S. Ge and F.L. Lewis, CRC Press, 2005. |
[9] |
A. Yesildirek and F.L. Lewis, “Adaptive feedback linearization using efficient neural networks,” Journal of Intelligent & Robotic Systems, vol. 31, pp. 253-281, 2001. |
[10] |
J.-Q. Huang and F.L. Lewis, “Neural-network predictive control for nonlinear dynamic systems with time delay,” IEEE Trans. Neural Networks, vol. 14, no. 2, pp. 377-389, Mar. 2003. |
[11] |
O. Kuljaca and F.L. Lewis, “Adaptive Critic design using nonlinear network structures,” Int. J. Adaptive Control and Signal Proc., vol. 17, no. 6, pp. 431-445, June 2003. |
[12] |
J. Campos and F.L. Lewis, “Backlash compensation with filtered prediction in discrete time nonlinear systems by dynamic inversion using neural networks,” Asian J. Control, vol. 6, no. 3, Sept. 2004. |
[13] |
M. Abu-Khalaf and F.L. Lewis, “Nearly optimal state feedback control of constrained nonlinear systems using a neural networks HJB approach,” IFAC Annual Reviews in Control, vol. 28, pp. 239-251, 2004. |
[14] |
M. Abu-Khalaf and F.L. Lewis, “Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach,” Automatica, vol. 41, pp. 779-791, 2005. |
[15] |
S. Jagannathan and F.L. Lewis, “Robust backstepping control of a class of nonlinear systems using fuzzy logic,” Int. J. Information Sciences, vol. 12, no. 3, pp. 223-240, 2000. |
[16] |
D. O. Popa, J.T. Wen, “Feedback Stabilization and Trajectory Tracking of Nonlinear Affine Systems”, Proceedings of Asian Control Journal, May, 2000, pp. 57-75. |
[17] |
S. K. Singh, D. O. Popa, “An Analysis of Some Fundamental Problems in Adaptive Control of Force and Impedance Behaviour: Theory and Experiments” IEEE Journal of Robotics and Automation, Dec. 1995, pp. 912-921. |
[18] |
Wen, J.T.; Popa, D.O.; Montemayor, G.; Liu, P.L.; “Human assisted impedance control of overhead cranes,“ Control Applications, 2001. (CCA '01). Proceedings of the 2001 IEEE International Conference on 5-7 Sept. 2001 Page(s):383 - 387 “Human Assisted Impedance Control of Overhead Cranes”, in Proc. of Int’l Conference on Control Applications, Mexico City, September 2001. |
[19] |
Popa, D.O.; Wen, J.T.;” Feedback stabilization of nonlinear affine systems,” Decision and Control, 1999. Proceedings of the 38th IEEE Conference on Volume 2, 7-10 Dec. 1999 Page(s):1290 - 1295 vol.2. |
[20] |
D.O. Popa, J.T. Wen, “Characterization of Singular Controls for nonholonomic motion planning", in Proc. of IFAC World Congress, San Francisco, June 1996. |
[21] |
Popa, D.O.; Wen, J.T.;” Nonholonomic path-planning with obstacle avoidance: a path-space approach,” Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on Volume 3, 22-28 April 1996 Page(s):2662 - 2667 vol.3. |
Related Topics
:
Control :
Intelligent and Nonlinear
Control
Discrete Event Control
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