Design and Implementation of ANFIS Algorithm Using VHDL for Vechicular System

In this review paper Field Programmable Gate Array (FPGA) is proposed to build an Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm for non linear behavior of the system. In the application of designing the algorithm for controlling a full vehicle nonlinear active suspension system. The algorithm for neural was Back propagation and for fuzzy takagi- sugeno-kang active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. It is shown that the ANFIS can modelize a nonlinear system very accurately by means of data taken from mathematical model. Firstly the MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. But in this paper the implementation of the adaptive neuro fuzzy inference system algorithm using FPGA boards has been try to investigated in this work. The Xilinx ISE software is employed to synthesis the VHDL codes used to program the FPGA. DOI: 10.17762/ijritcc2321-8169.15028

Parallel Type-2 Fuzzy Logic Co-Processors for Engine Management

Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 Fuzzy Logic Controllers (FLCs) have shown that they can handle such uncertainties and give a superior performance to the existing commercial controllers. However, there are a number of computational bottlenecks that pose as significant barriers to the widespread deployment of type-2 FLCs in commercial embedded control systems. This paper explores the use of parallel hardware implementations of interval type-2 FLC as a means to eradicate these barriers thus producing bespoke co-processors for a soft core implementation of a FPGA based 32 bit RISC micro-processor. These co-processors will perform functions such as fuzzification and type reduction and are currently utilised as part of a larger embedded interval Type-2 Fuzzy Engine Management System (T2FEMS). Numerous timing comparisons were undertaken between the co-processors and their sequential counterparts where the type-2 co-processors reduced significantly the computational cycles required by the type-2 FLC. This reduction in computational cycles allowed the T2FEMS to produce faster control responses whilst offering a superior control performance to the commercial engine management systems. Thus the proposed co-processors enable us to fully explore the potential of interval and possibly general type-2 FLCs in commercial embedded applications. © 2007 IEEE

Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance

The Implementation Of Fuzzy Systems, Neural Networks and Fuzzy Neural Networks Using FPGAs.

this paper provides a report on the implementation of both architectures and also offers a comparison with the hybrid structure. 3. The application and design rationale The application selected in this work was a non-linear function approximation problem given by equation 1 : y x x x = + + - + - ( . . ) 1 1 05 2 1 3 15 2 ....(1) where x 1 , x 2 and x 3 are the three input variables confined to the range 1 to 5. This highly non-linear problem has been employed by a number of other researchers to demonstrate the ability of both fuzzy and neuro-fuzzy systems The hardware implementation was designed with the main objective of maximising the utilisation of CLBs. The design approach was modular to accommodate higher order problems with increased granularity and with increased resolution in the digital implementation. The overall design was created and simulated in schematic format using VIEWlogic, and then the Xilinx software facilitated the export of the design and the generation of the corresponding bit file used to configure the target FPGA. 4. The fuzzy, neural network and fuzzy neural network architecture