VHDL-AMS based genetic optimisation of fuzzy logic controllers

Purpose – This paper presents a VHDL-AMS based genetic optimisation methodology for fuzzy logic controllers (FLCs) used in complex automotive systems and modelled in mixed physical domains. A case study applying this novel method to an active suspension system has been investigated to obtain a new type of fuzzy logic membership function with irregular shapes optimised for best performance. Design/methodology/approach – The geometrical shapes of the fuzzy logic membership functions are irregular and optimised using a genetic algorithm (GA). In this optimisation technique, VHDL-AMS is used not only for the modelling and simulation of the FLC and its underlying active suspension system but also for the implementation of a parallel GA directly in the system testbench. Findings – Simulation results show that the proposed FLC has superior performance in all test cases to that of existing FLCs that use regular-shape, triangular or trapezoidal membership functions. Research limitations – The test of the FLC has only been done in the simulation stage, no physical prototype has been made. Originality/value – This paper proposes a novel way of improving the FLC’s performance and a new application area for VHDL-AMS

A model-based fuzzy control of an induction motor

The paper presents a method of obtaining a simplified fuzzy model of an induction motor from measured data, without the necessity of preliminary knowledge of its internal structure and parameters. With the aim of avoiding a heuristic search for linguistic control rules, the paper presents one of the possibilities of the application of this method for an inverse fuzzy model based control. The proposed simplified fuzzy model of an induction motor was applied in the control of the desired torque of the drive with induction motor. Obtained results were first verified by simulation in programme Matlab and finally experimentally validated by measurements on an IC inverter–induction motor system. Simulation results and experimental measurements confirmed the correctness of the proposed fuzzy modelling and control method and its applicability also to other nonlinear dynamic systems