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

Modeling and Design of Digital Electronic Systems

The paper is concerned with the modern methodologies for holistic modeling of electronic systems enabling system-on-chip design. The method deals with the functional modeling of complete electronic systems using the behavioral features of Hardware Description Languages or high level languages then targeting programmable devices - mainly Field Programmable Gate Arrays (FPGAs) - for the rapid prototyping of digital electronic controllers. This approach offers major advantages such as: a unique modeling and evaluation environment for complete power systems, the same environment is used for the rapid prototyping of the digital controller, fast design development, short time to market, a CAD platform independent model, reusability of the model/design, generation of valuable IP, high level hardware/software partitioning of the design is enabled, Concurrent Engineering basic rules (unique EDA environment and common design database) are fulfilled. The recent evolution of such design methodologies is marked through references to case studies of electronic system modeling,simulation, controller design and implementation. Pointers for future trends / evolution of electronic design strategies and tools are given

A Review on ANFIS based Linearization of Non Linear Sensors

Low cost sensors having high sensitivity, better resolution and linear characteristics are required for industrial applications based on instrumentation and control. Unfortunately, the natural non linear characteristic of sensor itself and also the dynamic nature of the environment, aging effect, inherent sensor’s noise and data loss due to transients or intermittent faults affects the sensor characteristics non linearly. As the transfer characteristic of most sensors is nonlinear in nature, obtaining data from such a nonlinear sensor, by using an optimized device, has always been a design challenge. Linearization of nonlinear sensor characteristic in digital environment, is a vital step in the instrument signal conditioning process. This paper gives a brief review about how to overcome this nonlinear characteristic of the sensor using artificial intelligence such as  Hybrid Neuro Fuzzy Logic (HNFL) based on digital linearization technique using VLSI technology such as Field Programmable Gate Array (FPGA)

Hardware/software codesign of configurable fuzzy control systems

Fuzzy inference techniques are an attractive and well-established approach for solving control problems. This is mainly due to their inherent ability to obtain robust, low-cost controllers from the intuitive (and usually ambiguous or incomplete) linguistic rules used by human operators when describing the control process. This paper focuses on the hardware/software codesign of configurable fuzzy control systems. Two prototype systems implemented on general-purpose development boards are presented. In both of them, hardware components are based on specific and configurable fuzzy inference architecture whereas software tasks are supported by a microcontroller. The first prototype uses an off-the-shelf microcontroller and a low-complexity Xilinx XC4005XL field programmable gate array (FPGA). The second one is implemented as a system on programmable chip (SoPC), integrating the microcontroller together with the fuzzy hardware architecture and its interface circuits into a Xilinx Spartan2E200 FPGA.Comisión Interministerial de Ciencia y Tecnología TIC2001-1726-C02-0

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs