This study presents the design and evaluation of robust controllers, based on linear programming, Kharitonov's theorem and Chebyshev's theorem, in order to enhance the performance of a typical structure of multi-stage converters present in direct current (dc) systems. In such electric power distribution systems, point-of-load converters act as a constant power load (CPL), which introduces a destabilizing nonlinear effects to their supply bus voltage. The multi-stage converter system uses a simplified scheme based on cascaded converter, which is comprised of two dc-dc buck converter in series connection. The robust controllers evaluated overcome the negative incremental impedance instability problem due to CPL, which causes a high risk of instability in interconnected converters. Thereby, the robust controllers evaluated ensure robust control performance and stability with a minor performance degradation compared to a conventional controller when the cascaded converter system is subjected to parametric uncertainties. The control methodologies evaluated are applied in both dc-dc buck converters. Assessments on the performance of the control methodologies evaluated are conducted. Experimental validations on a 150-W dc-dc cascaded converter system test board are carried out to verify the theoretical claims.

This study presents the design and evaluation of a robust controller, based on frequency-domain, in order to enhance the performance of dc-dc power converters under parametric uncertainties. This robust control approach takes into account all possible uncertainties in the system such as load and input voltage variations. Thereby, the robust controller design is based on the constraints of the gain and/or phase margins delimited by these uncertainties in order to ensure robust performance and stability of the system. Assessments on the performance of the proposed robust control are conducted. Comparisons with other control methods are also provided. Experimental validations on a dc-dc buck converter system test board are carried out to verify the theoretical claims.

This article portrays the relationship between fractional order differential calculus and the computational intelligence method, applying it to the improvement of intelligent systems. The Kirchhoff Laws, represented by second order differential equations, were solved via non-integer order differential calculus. The results obtained were used in the implementation of decision trees, which allowed the decision rules to be incorporated into the controllers. The results obtained by mathematical modeling did magnify the information extracted from Kirchhoff's Laws. Due to the gain magnitude of this information, the decision trees were obtained with greater precision and accuracy. In this way, it was achieved to build a hybrid system capable of being used in the development of controllers automata that has the lower response time and highest efficiency.