Experimental assessment of fuzzy-tree adaptive synergetic control law for DC/DC buck converter

The step-down DC/DC converter is characterized by the parametric uncertainty of its model and its high sensitivity to external disturbances identified by variations in the input voltage, load and output reference voltage. To overcome these limitations, this paper proposes the experimental evaluation of a new adaptive synergetic control law based on the M5P model tree for efficient output voltage control of the step-down type DC/DC converter. A combination of the synergetic technique, the fuzzy logic theory and the machine learning has been used for the first time to develop this advanced control law. The synergetic strategy is exploited to overcome the chattering problem arising from the use of the sliding mode approach which has been applied in previous research. Furthermore, the novel machine learning M5P tree-based approximator was designed and applied to approximate the unknown and imprecise dynamics of the studied converter. The learning data comes from the smart approximator, which is based on fuzzy logic, with the aim of extracting its own advantages and offering them to the new tree-based approximator M5P. This action will make it possible to improve the operation of the fuzzy approximator, which has already been studied in some research. Finally, the developed control strategy was successfully evaluated in the laboratory using the dSPACE 1104 board and compared with the fuzzy adaptive synergetic approach under different conditions of load variations and imposed references. The practical results show that the proposed tree adaptive synergetic approach is competitive with the fuzzy adaptive strategy in terms of ease of practical implementation, fast control execution and better output voltage regulation quality.