  We address the problem of stability of motor actions implemented by the
central nervous system based on simple algorithms potentially reflecting
physical (including physiological) processes within the body. A number of
conceptually simple algorithms that solve motor tasks with a high probability
of success may be based on feedback schemes that ensure stability of subspaces
of neural variables associated with accomplishing those tasks.
  The task is formulated in terms of linear constrains imposed either on the
human body mechanical variables or on neural variables; we discuss three
reference frames relevant to these processes. We discuss underlying basic
principles of such algorithms, their architecture, and efficiency, and compare
the outcomes of implementation of such algorithms with the results of
experiments performed on the human hand.
