Integration of Soft Computing and Fractional Derivatives in Adaptive Control

Realizing that generality and uniformity of the usual Soft Computing (SC) structures exclude the application of plausible simplifications relevant in the case of whole problem classes resulted in the idea that a novel branch of soft computing could be developed by the use of which far simpler and more lucid uniform structures and procedures could be applied than in the traditional ones. Such a novel approach to computational cybernetics akin to SC was developed at Budapest Tech to control inaccurately and incompletely modelled dynamic systems under external disturbances. Hydraulic servo valve controlled differential cylinders as non-linear, strongly coupled multivariable electromechanical tools serve as excellent paradigms of such difficulties. Their control has to cope with the problem of instabilities due to the friction forces between the piston and the cylinder, as well as with uncertainties and variation of the hydrodynamic parameters that makes it unrealistic to develop an accurate static model for them. In this paper a combination of this novel method with the use of fractional derivatives is applied for the control of a hydraulic differential cylinder. Simulation results well exemplifying the conclusions are also presented

Robust Fixed Point Transformations-based Control of Chaotic Systems

Nowadays, nonlinear control is a very important task because machines are playing more and more role in life. Lyapunov's 2nd method is a popular tool by the use of which various controllers can be designed like the adaptive Neural Networks, Fuzzy Controllers, and Neuro-Fuzzy solutions, or the Sliding Mode Controllers and the well-known PID feedback controllers. Robust Fixed Point Transformation is a procedure which can be built for almost any type of controller in case an approximate model is used to estimate the controlled system's behavior. In this paper a new approach to Robust Fixed Point Transformations (RFPT) is introduced by integrating a second controller in the system. Authors show that this additional, "recalculated" controller not just improves the original controller's results, but halves the tracking errors achieved by the previous RFPT methods