CAR TRACTION CONTROL SYSTEM

This project explores the potential of implementing fuzzy logic algorithm for traction control system using VHDL. Previously, the project on car traction control was done by simulation using fuzzy logic approach. The Fuzzy Logic Toolbox in MATLAB software is used to create simulation for fuzzy logic system. The challenge of the project is to design the control system using hardware description language for future implementation on hardware using FPGA. Fuzzy logic controller provides optimum control according to the conditions specify. It is useful when the driving condition is uncontrolled. The core programming language which will be used as the hardware description language is VHSIC Hardware Description Language (VHDL). VHDL is used in FPGA - based implementation. The methodology includes designing the fuzzy logic controller, development of the algorithm and codes programming. After that, the following phase includes testing and troubleshooting. Lastly, carry out the documentation. In conclusion, it is possible to develop the algorithm for fuzzy - based car traction control system using VHDL. The implementation of the control system using VHDL is viable for future implementation onto FPGA. Thus the performance of the car traction control would be enhance

Robust Fuzzy Controllers Using FPGAs

Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms

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

Simulink modeling and design of an efficient hardware-constrained FPGA-based PMSM speed controller

The aim of this paper is to present a holistic approach to modeling and FPGA implementation of a permanent magnet synchronous motor (PMSM) speed controller. The whole system is modeled in the Matlab Simulink environment. The controller is then translated to discrete time and remodeled using System Generator blocks, directly synthesizable into FPGA hardware. The algorithm is further refined and factorized to take into account hardware constraints, so as to fit into a low cost FPGA, without significantly increasing the execution time. The resulting controller is then integrated together with sensor interfaces and analysis tools and implemented into an FPGA device. Experimental results validate the controller and verify the design

CAR TRACTION CONTROL SYSTEM

This project explores the potential of implementing fuzzy logic algorithm for traction control system using VHDL. Previously, the project on car traction control was done by simulation using fuzzy logic approach. The Fuzzy Logic Toolbox in MATLAB software is used to create simulation for fuzzy logic system. The challenge of the project is to design the control system using hardware description language for future implementation on hardware using FPGA. Fuzzy logic controller provides optimum control according to the conditions specify. It is useful when the driving condition is uncontrolled. The core programming language which will be used as the hardware description language is VHSIC Hardware Description Language (VHDL). VHDL is used in FPGA - based implementation. The methodology includes designing the fuzzy logic controller, development of the algorithm and codes programming. After that, the following phase includes testing and troubleshooting. Lastly, carry out the documentation. In conclusion, it is possible to develop the algorithm for fuzzy - based car traction control system using VHDL. The implementation of the control system using VHDL is viable for future implementation onto FPGA. Thus the performance of the car traction control would be enhance

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

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

FPGA design methodology for industrial control systems—a review

This paper reviews the state of the art of fieldprogrammable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic