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,

x ˙ = g ( x , t ) {\displaystyle {\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,

x ˙ = f ( x , u ) {\displaystyle {\dot {x}}=f(x,u)}

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

u = k ( x , t ) {\displaystyle 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,

x ˙ = g ( x , t ) {\displaystyle {\dot {x}}=g(x,t)}

is equivalent to a controlled differential equation,

x ˙ = u {\displaystyle {\dot {x}}=u}

with feedback control, u = g ( x , t ) {\displaystyle u=g(x,t)} . Then, given an initial value problem, Ross partitions the time interval [ 0 , ) {\displaystyle [0,\infty )} to a grid, π = { t i } i 0 {\displaystyle \pi =\{t_{i}\}_{i\geq 0}} with t i  as  i {\displaystyle t_{i}\to \infty {\text{ as }}i\to \infty } . From t 0 {\displaystyle t_{0}} to t 1 {\displaystyle t_{1}} , generate a control trajectory,

u ( t ) = g ( x 0 , t ) , x ( t 0 ) = x 0 , t 0 t t 1 {\displaystyle u(t)=g(x_{0},t),\quad x(t_{0})=x_{0},\quad t_{0}\leq t\leq t_{1}}

to the controlled differential equation,

x ˙ = u ( t ) , x ( t 0 ) = x 0 {\displaystyle {\dot {x}}=u(t),\quad x(t_{0})=x_{0}}

A Carathéodory solution exists for the above equation because t u {\displaystyle t\mapsto u} has discontinuities at most in t, the independent variable. At t = t 1 {\displaystyle t=t_{1}} , set x 1 = x ( t 1 ) {\displaystyle x_{1}=x(t_{1})} and restart the system with u ( t ) = g ( x 1 , t ) {\displaystyle u(t)=g(x_{1},t)} ,

x ˙ ( t ) = u ( t ) , x ( t 1 ) = x 1 , t 1 t t 2 {\displaystyle {\dot {x}}(t)=u(t),\quad x(t_{1})=x_{1},\quad t_{1}\leq t\leq 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

  • Ross' π lemma

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. ^ a b 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. ^ a b 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. ^ a b 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. ^ a b 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