  We study \emph{optimal insider control problems}, i.e. optimal control
problems of stochastic systems where the controller at any time $t$ in addition
to knowledge about the history of the system up to this time, also has
additional information related to a \emph{future} value of the system. Since
this puts the associated controlled systems outside the context of
semimartingales, we apply anticipative white noise analysis, including forward
integration and Hida-Malliavin calculus to study the problem. Combining this
with Donsker delta functionals we transform the insider control problem into a
classical (but parametrised) adapted control system, albeit with a
non-classical performance functional. We establish a sufficient and a necessary
maximum principle for such systems. Then we apply the results to obtain
explicit solutions for some optimal insider portfolio problems in financial
markets described by It\^ o-L\' evy processes. Finally, in the Appendix we give
a brief survey of the concepts and results we need from the theory of white
noise, forward integrals and Hida-Malliavin calculus.
