Development of FPGA based Standalone Tunable Fuzzy Logic Controllers

Soft computing techniques differ from conventional (hard) computing, in that unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind and its ability to address day-to-day problems. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Artificial Neural Networks (ANNs). This thesis presents a generic hardware architecture for type-I and type-II standalone tunable Fuzzy Logic Controllers (FLCs) in Field Programmable Gate Array (FPGA). The designed FLC system can be remotely configured or tuned according to expert operated knowledge and deployed in different applications to replace traditional Proportional Integral Derivative (PID) controllers. This re-configurability is added as a feature to existing FLCs in literature. The FLC parameters which are needed for tuning purpose are mainly input range, output range, number of inputs, number of outputs, the parameters of the membership functions like slope and center points, and an If-Else rule base for the fuzzy inference process. Online tuning enables users to change these FLC parameters in real-time and eliminate repeated hardware programming whenever there is a need to change. Realization of these systems in real-time is difficult as the computational complexity increases exponentially with an increase in the number of inputs. Hence, the challenge lies in reducing the rule base significantly such that the inference time and the throughput time is perceivable for real-time applications. To achieve these objectives, Modified Rule Active 2 Overlap Membership Function (MRA2-OMF), Modified Rule Active 3 Overlap Membership Function (MRA3-OMF), Modified Rule Active 4 Overlap Membership Function (MRA4-OMF), and Genetic Algorithm (GA) base rule optimization methods are proposed and implemented. These methods reduce the effective rules without compromising system accuracy and improve the cycle time in terms of Fuzzy Logic Inferences Per Second (FLIPS). In the proposed system architecture, the FLC is segmented into three independent modules, fuzzifier, inference engine with rule base, and defuzzifier. Fuzzy systems employ fuzzifier to convert the real world crisp input into the fuzzy output. In type 2 fuzzy systems there are two fuzzifications happen simultaneously from upper and lower membership functions (UMF and LMF) with subtractions and divisions. Non-restoring, very high radix, and newton raphson approximation are most widely used division algorithms in hardware implementations. However, these prevalent methods have a cost of more latency. In order to overcome this problem, a successive approximation division algorithm based type 2 fuzzifier is introduced. It has been observed that successive approximation based fuzzifier computation is faster than the other type 2 fuzzifier. A hardware-software co-design is established on Virtex 5 LX110T FPGA board. The MATLAB Graphical User Interface (GUI) acquires the fuzzy (type 1 or type 2) parameters from users and a Universal Asynchronous Receiver/Transmitter (UART) is dedicated to data communication between the hardware and the fuzzy toolbox. This GUI is provided to initiate control, input, rule transfer, and then to observe the crisp output on the computer. A proposed method which can support canonical fuzzy IF-THEN rules, which includes special cases of the fuzzy rule base is included in Digital Fuzzy Logic Controller (DFLC) architecture. For this purpose, a mealy state machine is incorporated into the design. The proposed FLCs are implemented on Xilinx Virtex-5 LX110T. DFLC peripheral integration with Micro-Blaze (MB) processor through Processor Logic Bus (PLB) is established for Intellectual Property (IP) core validation. The performance of the proposed systems are compared to Fuzzy Toolbox of MATLAB. Analysis of these designs is carried out by using Hardware-In-Loop (HIL) test to control various plant models in MATLAB/Simulink environments

A Novel ANFIS Algorithm Architecture for FPGA Implementation

This paper presents a new architecture for the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm targeting FPGA implementation. This new architecture offers higher efficiency and scalability in comparison to the existing methods. The proposed architecture is modeled and simulated using VHDL and is targeted at a Xilinx FPGA. Existing implementation architectures are also modeled and comparisons are drawn between them in terms of both performance and logic utilization. The results show that the new architecture offers a reduction in calculation cycles of around 50% in comparison to the architecture from which it’s derived. This increase in calculation speed comes with only a modest increase in logic utilization, specifically a 2.5% increase in look-up table (LUT) usage and a 1.5% increase in flip-flop usage. The new architecture also eliminates scalability issues which can arise in the existing architectures when extra input members are required

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

Hardware Realization of Interval Type 2 Fuzzy Logic Controller

Form the publication of Prof. Loftis’ fuzzy set theory and linguistic approach, fuzzy logic has become one of the major research topic for researchers. The type 1 fuzzy logic controller has already been used in many areas such as consumer electronics, defense, aerospace engineering etc. This type 1 fuzzy system does not model the fuzziness of the membership function or in other terms it does not deal with uncertainty very well. The control problems where more accuracy is needed, type 1 fuzzy system unable to satisfy their requirements. If a type 2 fuzzy controller is designed, it will have great application in control industry. Type 2 fuzzy system can deal with fuzziness in the decisions or in the membership functions. Membership functions are the decisions of an expert. This inclusion of uncertainty in the design will give us much better control behavior. The design and hardware realization of interval type 2 fuzzy logic controller is aim of this work. The way of design or methods of design much more important as it will give a clear understanding to the reader

Development of controllers using FPGA for fuel cells in standalone and utility applications

In the recent years, increase in consumption of energy, instability of crude oil price and global climate change has forced researchers to focus more on renewable energy sources.Though there are different renewable energy sources available (such as photovoltaic and wind energy), they have some major limitations. The potential techniques which can provide renewable energy are fuel cell technology which is better than other renewable sources of energy. Solid oxide fuel cell (SOFC) is more efficient, environmental friendly renewable energy source. This dissertation focuses on load/grid connected fuel cell power system (FCPS) which can be used as a backup power source for household and commercial units. This backup power source will be efficient and will provide energy at an affordable per unit cost. Load/grid connected fuel cell power system mainly comprises of a fuel cell module, DCDC converter and DC-AC inverter. This thesis primarily focuses on solid oxide fuel cell (SOFC) modelling, digital control of DC-DC converter and DC-AC inverter. Extensive simulation results are validated by experimental results. Dynamic mathematical model of SOFC is developed to find out output voltage, efficiency, over potential loss and power density of fuel cell stack. The output voltage of fuel cell is fed to a DC-DC converter to step up the output voltage. Conventional Proportional-Integral (PI) controller and FPGA based PI controller is implemented and experimentally validated. The output voltage of DC-DC converter is fed to DC-AC inverter. Different pulse width modulation-voltage source inverter (PWM-VSI) control strategy (such as Hysteresis Current Controller (HCC), Adaptive-HCC, Fuzzy-HCC, Adaptive Fuzzy-HCC, Triangular Carrier Current Controller (TCCC) and Triangular Periodical Current Controller (TPCC)) for DC-AC inverter are investigated and validated through extensive simulations using MATLAB/SIMULINK. This work also focuses on number of fuel cells required for application in real time and remedy strategies when one or few fuel cells are malfunctioning. When the required numbers of fuel cells are not available, DC-DC converter is used to step up the output voltage of fuel cell. When there is a malfunction in fuel cell or shortage of hydrogen then a battery is used to provide backup power

A Novel Programmable CMOS Fuzzifiers Using Voltage-to-Current Converter Circuit

This paper presents a new voltage-input, current-output programmable membership function generator circuit (MFC) using CMOS technology. It employs a voltage-to-current converter to provide the required current bias for the membership function circuit. The proposed MFC has several advantageous features. This MFC can be reconfigured to perform triangular, trapezoidal, S-shape, Z-Shape, and Gaussian membership forms. This membership function can be programmed in terms of its width, slope, and its center locations in its universe of discourses. The easily adjustable characteristics of the proposed circuit and its accuracy make it suitable for embedded system and industrial control applications. The proposed MFC is designed using the spice software, and simulation results are obtained

Advanced control system for stand-alone diesel engine driven-permanent magnetic generator sets

The main focus is on the development of an advanced control system for variable speed standalone diesel engine driven generator systems. An extensive literature survey reviews the historical development and previous relevant research work in the fields of diesel engines, electrical machines, power electronic converters, power and electronic systems. Models are developed for each subsystem from mathematical derivations with necessary simplifications made to reduce complexity while retaining the required accuracy. Initially system performance is investigated using simulation models in Matlab/Simulink. The AC/DC/AC power electronic conversion system used employs a voltage controlled dc link. The ac voltage is maintained at constant magnitude and frequency by using a dc-dc converter and a fixed modulation ratio VSI PWM inverter. The DC chopper provides fast control of the output voltage by dealing efficiently with transient conditions. A Variable Speed Fuzzy Logic Core (VSFLC) controller is combined with a classical control method to produce a novel hybrid controller. This provides an innovative variable speed control that responds to both load and speed changes. A new power balance based control strategy is proposed and implemented in the speed controller. Subsequently a novel overall control strategy is proposed to co-ordinate the hybrid variable speed controller and chopper controller to provide overall control for both fast and slow variations of system operating conditions. The control system is developed and implemented in hardware using Xilinx Foundation Express. The VHDL code for the complete control system design is developed and the designs are synthesised and analysed within the Xilinx environment. The controllers are implemented with XC95108-PC84 and XC4010-PC84 to provide a compact and cheap control system. A prototype experimental system is described and test results are obtained that show the combined control strategy to be very effective. The research work makes contributions in the areas of automatic control systems for diesel engine generator sets and CPLD/FPGA application that will benefit manufacturers and consumers.EPSR

An FPGA Implementation of Kak's Instantaneously-Trained, Fast-Classification Neural Networks

Motivated by a biologically plausible short-memory sketchpad, Kak's Fast Classification (FC) neural networks are instantaneously trained by using a prescriptive training scheme. Both weights and the topology for an FC network are specified with only two presentations of the training samples. Compared with iterative learning algorithms such as Backpropagation (which may require many thousands of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks are suitable for applications where real-time classification and adaptive filtering are needed. In this paper we show that FC networks are "hardware friendly" for implementation on FPGAs. Their unique prescriptive learning scheme can be integrated with the hardware design of the FC network through parameterization and compile-time constant folding