Implementation of genetic algorithm based fuzzy logic controller with automatic rule extraction in FPGA

A number of fuzzy logic controllers are being designed till now to replace complex, non-linear and huge controlling equipment in numerous industrial sectors. But the designing of these controllers requires thorough knowledge about the controlled process. For this purpose a highly experienced experts are required, which is not feasible all the time. Most of these processes are non-linear and depend on large number of parameters. Thus mathematical representation of these systems is an arduous line of work. This project addresses these problems by proposing using of genetic algorithm based Fuzzy Logic systems as controllers. The system includes algorithms which are run on a capable computing platform, to read an experimental data sheet obtained from experimental observations of the system and generate a fine tuned rule base that is to be used in the fuzzy logic controller hardware. The hardware is implemented in an FPGA. Transfer of synthesized rule base from the computer to the FPGA implementation and crisp output value back to the computer is done by UART. A graphical user interface is provided that runs on the computer

Hybrid Optimized Fuzzy Pitch Controller of a Floating Wind Turbine with Fatigue Analysis

Floating offshore wind turbines (FOWTs) are systems with complex and highly nonlinear dynamics; they are subjected to heavy loads, making control with classical strategies a challenge. In addition, they experience vibrations due to wind and waves. Furthermore, the control of the blade angle itself may generate vibrations. To address this issue, in this work we propose the design of an intelligent control system based on fuzzy logic to maintain the rated power of an FOWT while reducing the vibrations. A gain scheduling incremental proportional–derivative fuzzy controller is tuned by genetic algorithms (GAs) and combined with a fuzzy-lookup table to generate the pitch reference. The control gains optimized by the GA are stored in a database to ensure a proper operation for different wind and wave conditions. The software Matlab/Simulink and the simulation tool FAST are used. The latter simulates the nonlinear dynamics of a real 5 MW barge-type FOWT with irregular waves. The hybrid control strategy has been evaluated against the reference baseline controller embedded in FAST in different environmental scenarios. The comparison is assessed in terms of output power and structure stability, with up to 23% and 33% vibration suppression rate for tower top displacement and platform pitch, respectively, with the new control scheme. Fatigue damage equivalent load (DEL) of the blades has been also estimated with satisfactory results.This work has been partially supported by the Spanish Ministry of Science and Innovation under the project MCI/AEI/FEDER number RTI2018-094902-B-C21 and PDI2021-123543OB-C21

Evolution of Voronoi based Fuzzy Recurrent Controllers

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. Among the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi (RFV) model, a representation for recurrent fuzzy systems. It is an extension of the FV model proposed by Kavka and Schoenauer that extends the application domain to include temporal problems. The FV model is a representation for fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, fulfilling the $\epsilon$-completeness property and providing a simple way to introduce a priory knowledge. In the proposed representation, the temporal relations are embedded by including internal units that provide feedback by connecting outputs to inputs. These internal units act as memory elements. In the RFV model, the semantic of the internal units can be specified together with the a priori rules. The geometric interpretation of the rules allows the use of geometric variational operators during the evolution. The representation and the algorithms are validated in two problems in the area of system identification and evolutionary robotics

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

Intelligent STATCOM Voltage Regulation using Fuzzy Logic Control

Reactive power compensation is a very important and challenging task in electrical power systems today. Future trends foreseen in power systems such as high interconnectivity and the integration of renewable energy resources produce even more issues related to power system control and stability. Flexible AC transmission systems are vastly used in power systems in order to mitigate several performance aspects found in typical power systems. One shunt connected device in particular, STATCOM, is very powerful and commonly used in voltage regulation at the power transmission level. STATCOM uses voltage sourced converters to inject or absorb reactive power from the power grid as commanded to stabilize the transmission line voltage at the point of connection. The control of STATCOM has relied historically on using traditional PI controllers, however, since the dynamic response of STATCOM highly affects its ability to perform its task, improving the capabilities of STATCOM using more advanced control approaches has become vital for both manufacturers and power systems operators. Fuzzy logic control, as one area of artificial intelligence techniques, has been emerging in recent years as a complement to the conventional methods in various areas of power systems control. The most significant advantage of fuzzy controller as an intelligent controller is that it doesn’t require mathematical modelling. It is robust and nonlinear in its nature, and expert’s knowledge can be utilized in generating control rules. The main contribution is to use fuzzy logic control theory to design a pure fuzzy logic control and another fuzzy adaptive PI control strategies for STATCOM that are superior in performance to traditional PI control approach. This will increase STATCOM’s ability to seamlessly perform their task in voltage regulation. This work investigates the performance of classical PI controlled STATCOM then compares it with fuzzy logic based STATCOM and fuzzy adaptive PI controlled STATCOM. Simulations done using MATLAB on a three generator test system show that adaptive fuzzy PI control technique is faster in responding to voltage variations and better in tracking the reactive current reference. Results also show that a direct control using fuzzy logic provides even faster voltage regulation and acts almost as a perfect tracker for reference reactive current

Prior knowledge in evolutionary fuzzy recurrent controllers design

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Control of a benchmark structure using GA-optimized fuzzy logic control

Mitigation of displacement and acceleration responses of a three story benchmark structure excited by seismic motions is pursued in this study. Multiple 20-kN magnetorheological (MR) dampers are installed in the three-story benchmark structure and managed by a global fuzzy logic controller to provide smart damping forces to the benchmark structure. Two configurations of MR damper locations are considered to display multiple-input, single-output and multiple-input, multiple-output control capabilities. Characterization tests of each MR damper are performed in a laboratory to enable the formulation of fuzzy inference models. Prediction of MR damper forces by the fuzzy models shows sufficient agreement with experimental results. A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation, and then further improve their performance by intelligently modulating the command voltage for concurrent reductions of displacement and acceleration responses. An optimal controller is identified and validated through numerical simulation and fullscale experimentation. Numerical and experimental results show that performance of the controller algorithm is superior to optimal passive cases in 43% of investigated studies. Furthermore, the state-space model of the benchmark structure that is used in numerical simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction of response of the benchmark structure to base excitation

Can trained monkeys design flight controllers for hypersonic vehicles?

The supersonic combustion ramjet is an as yet unproven propulsion system for hypersonic flight. Provided it can be developed into a practical vehicle, the ultimate success of sustained hypersonic flight will depend on configuring a robust and stable airframe-propulsion-control combination. To design the longitudinal flight controller for this inherently unstable vehicle we have applied a genetic algorithm, hence the trained monkeys metaphor in the title. Being a nondeterministic search method, there is no guarantee of generating a useful solution, yet given a little direction and enough time it is able to solve hard problems. The controller is built using fuzzy logic rules, directed at manipulating the vehicle's angle of attack through the actuation of symmetric elevators. A preset structure for the rules is used whereby the design task is to configure the control surface through selection of the rule consequents. To direct the search for a controller design, the genetic algorithm uses simulated flight responses to a range of initial conditions, without linearization of the vehicle model and dynamics. Results for the genetic algorithm designed controller show longitudinal stability and disturbance rejection

Hardware Implementation of Soft Computing Approaches for an Intelligent Wall-following Vehicle

Soft computing techniques are generally well-suited for vehicular control systems that are usually modeled by highly nonlinear differential equations and working in unstructured environment. To demonstrate their applicability, two intelligent controllers based upon fuzzy logic theories and neural network paradigms are designed for performing a wall-following task and an autonomous parking task. Based on performance and flexibility considerations, the two controllers are implemented onto a reconfigurable hardware platform, namely a Field Programmable Gate Array (FPGA). As the number of comparative studies of these two embedded controllers designed for the same application is limited in the literature, one of the main goals of this research work has been to evaluate and compare the two controllers in terms of hardware resource requirements, operational speeds and trajectory tracking errors in following different pre-defined trajectories. The main advantages and disadvantages of each of the controllers are presented and discussed in details. Challenging issues for implementation of the controllers on the FPGA platform are also highlighted. As the two controllers exhibit benefits and drawbacks under different circumstances, this research suggests as well a hybrid controller scheme as an attempt to integrate the benefits of both control units. To evaluate its performance, the hybrid controller is tested on the same pre-defined trajectories and the corresponding results are compared to that of the fuzzy logic and the neural network based controllers. For further demonstration of the capabilities of the wall-following controllers in other applications, the fuzzy logic and the neural network controllers are used in a parallel parking system. We see this work to be a stepping stone for further research work aiming at real world implementation of the controllers on Application Specified Integrated Circuit (ASIC) type of environment