Caratheodory-π solution

A Carathéodory-π solution is a generalized solution to an ordinary differential equation. The concept is due to I. Michael Ross and named in honor of Constantin Carathéodory.[1] Its practicality was demonstrated in 2008 by Ross et al.[2] in a laboratory implementation of the concept. The concept is most useful for implementing feedback controls, particularly those generated by an application of Ross' pseudospectral optimal control theory.[3]

Mathematical background

A Carathéodory-π solution addresses the fundamental problem of defining a solution to a differential equation,

\dot x = g(x,t)

when g(x,t) is not differentiable with respect to x. Such problems arise quite naturally [4] in defining the meaning of a solution to a controlled differential equation,

\dot x = f(x,u)

when the control, u, is given by a feedback law,

u = k(x,t)

where the function k(x,t) may be non-smooth with respect to x. Non-smooth feedback controls arise quite often in the study of optimal feedback controls and have been the subject of extensive study going back to the 1960s.[5]

Ross' concept

An ordinary differential equation,

\dot x = g(x,t)

is equivalent to a controlled differential equation,

\dot x = u

with feedback control, u = g(x,t) . Then, given an initial value problem, Ross partitions the time interval [0, \infty) to a grid, \pi = \{t_i\}_{i\ge 0} with t_i \to \infty \text{ as } i \to \infty . From t_0 to t_1, generate a control trajectory,

u(t) = g(x_0, t), \quad x(t_0) = x_0, \quad t_0 \le t \le t_1

to the controlled differential equation,

\dot x = u(t), \quad x(t_0) = x_0

A Carathéodory solution exists for the above equation because t \mapsto u has discontinuities at most in t, the independent variable. At t = t_1, set x_1 = x(t_1) and restart the system with u(t) = g(x_1, t),

\dot x(t) = u(t), \quad x(t_1) = x_1, \quad t_1 \le t \le t_2

Continuing in this manner, the Carathéodory segments are stitched together to form a Carathéodory-π solution.

Engineering applications

A Carathéodory-π solution can be applied towards the practical stabilization of a control system.[6] [7] It has been used to stabilize an inverted pendulum,[6] control and optimize the motion of robots,[7] [8] slew and control the NPSAT1 spacecraft[3] and produce guidance commands for low-thrust space missions.[2]

See also

References

  1. ↑ Biles, D. C., and Binding, P. A., “On Carathéodory’s Conditions for the Initial Value Problem," Proceedings of the American Mathematical Society, Vol. 125, No. 5, May 1997, pp. 1371–1376.
  2. 1 2 Ross, I. M., Sekhavat, P., Fleming, A. and Gong, Q., "Optimal Feedback Control: Foundations, Examples and Experimental Results for a New Approach," Journal of Guidance, Control and Dynamics, Vol. 31, No. 2, pp. 307–321, 2008.
  3. 1 2 Ross, I. M. and Karpenko, M. "A Review of Pseudospectral Optimal Control: From Theory to Flight," Annual Reviews in Control, Vol.36, No.2, pp. 182–197, 2012.
  4. ↑ Clarke, F. H., Ledyaev, Y. S., Stern, R. J., and Wolenski, P. R., Nonsmooth Analysis and Control Theory, Springer–Verlag, New York, 1998.
  5. ↑ Pontryagin, L. S., Boltyanskii, V. G., Gramkrelidze, R. V., and Mishchenko, E. F., The Mathematical Theory of Optimal Processes, Wiley, New York, 1962.
  6. 1 2 Ross, I. M., Gong, Q., Fahroo, F. and Kang, W., "Practical Stabilization Through Real-Time Optimal Control," 2006 American Control Conference, Minneapolis, MN, June 14-16 2006.
  7. 1 2 Martin, S. C., Hillier, N. and Corke, P., "Practical Application of Pseudospectral Optimization to Robot Path Planning," Proceedings of the 2010 Australasian Conference on Robotics and Automation, Brisbane, Australia, December 1-3, 2010.
  8. ↑ Björkenstam, S., Gleeson, D., Bohlin, R. "Energy Efficient and Collision Free Motion of Industrial Robots using Optimal Control," Proceedings of the 9th IEEE International Conference on Automation Science and Engineering (CASE 2013), Madison, Wisconsin, August, 2013
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