Toward Global Complex Systems Control The Autonomous Intelligence Challenge

Abstract

<p align="justify"><strong><span>Complex systems are the emerging new scientific frontier with modern technology advance and new parametric domains study in natural systems. An important challenge is, contrary to classical systems studied so far, the great difficulty in predicting their future behaviour from initial time because, by their very structure, interactions strength between system components is shielding completely their specific individual features. Independent of clear existence of strict laws complex systems are obeying like classical systems, it is however possible today to develop methods allowing to handle dynamical properties of such systems and to master their evolution.</span></strong><span>�</span><strong><span>So the methods should be imperatively adapted to representing system self organization when becoming complex. This rests upon the new paradigm of passing from classical trajectory space to more abstract trajectory manifolds associated to natural system invariants characterizing complex system dynamics. The methods are basically of qualitative nature, independent of system state space dimension and, because of its generic impreciseness, privileging robustness to compensate for not well known system parameters and functional variations. This points toward the importance of </span></strong><strong><em><span>control approach</span></em></strong><strong><span>�for complex system study in adequate function spaces, the more as for industrial applications there is now evidence that transforming a complicated man made system into a complex one is extremely beneficial for overall performance improvement</span></strong><span>. </span><strong><span>But this last step requires larger intelligence delegation to the system </span></strong><strong><span>requiring more autonomy for exploiting its full potential.</span></strong><strong><span>�A well defined, meaningful and explicit control law should be set by using equivalence classes within which system dynamics are forced to stay, so that a complex system described in very general terms can behave in a prescribed way for fixed system parameters value</span></strong><span>. </span><strong><span>Along</span></strong><strong><span>�the line traced by Nature for living creatures, the delegation is expressed at lower level by a change from regular trajectory space control to task space control following system reassessment into its complex stage imposed by the high level of interactions between system constitutive components. Aspects of this situation with coordinated action on both power </span></strong><strong><em><span>and</span></em></strong><strong><span>�information fluxes are handled in a new and explicit control structure derived from application of Fixed Point Theorem which turns out to better perform than (also explicit) extension of Popov criterion to more general nonlinear monotonically upper bounded potentials bounding system dynamics discussed here.</span></strong><span>�</span><strong><span>An interesting observation is that</span></strong><span>�</span><strong><span>when correctly amended as proposed here, complex systems are </span></strong><strong><em><span>not</span></em></strong><strong><span>�as commonly believed a counterexample to reductionism so strongly influential in Science with Cartesian method supposedly only valid for complicated systems.</span></strong><strong></strong></p>