A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues

Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing

A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research

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

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Adaptive hysteresis based fuzzy controlled shunt active power filter for mitigation of harmonics

Active filters are widely employed in distribution system to reduce the harmonics produced by non-linear loads result in voltage distortion and leads to various power quality problems. In this work the simulation study of a Adaptive hysteresis based fuzzy logic controlled shunt active power filter capable of reducing the total harmonic distortion is presented. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system and it can adapt its gain according to the changes in load. The instantaneous p-q theory is used for calculating the compensating current. Fuzzy-adaptive hysteresis band technique is adopted for the current control to derive the switching signals for the voltage source inverter. The fuzzy-adaptive hysteresis band current controller changes the hysteresis bandwidth according to the supply voltage and slope of the reference compensator current wave. A fuzzy logic-based controller is developed to control the voltage of the DC Capacitor. This work presents and compares the performance of the fuzzy-adaptive controller with a conventional fuzzy and PI controller under constant load. The total Harmonic Distortion, Individual harmonic content with respect to % of fundamental in Supply current, source voltage have been analyzed. Various simulation results are presented. And also the performance of two current control techniques namely adaptive hysteresis current control and fixed hysteresis control techniques are compared with respect to average switching frequency. A neural network control method for regulating the DC Voltage across the capacitor connected to the inverter for harmonic suppression is proposed. The THD of the source current after compensation is well below 5%, the harmonic limit imposed by the IEEE-519 standard

Position control of a shape memory alloy actuator using a four-term bilinear PID controller

Shape memory alloy (SMA) actuators have a number of appealing features, such as their low weight or their high force-to-weight ratio, that make them a potential alternative to traditional actuation technologies in fields such as space applications, surgical devices or wearable robotics. In this paper, a type of bilinear controller consisting of a conventional PID controller cascaded with a bilinear compensator, known as BPID, is proposed. Bilinear controllers are a subset of nonlinear controllers, which is why the BPID may be a promising alternative to control the position of a SMA actuator. Nonlinear control techniques are commonly applied to control SMA actuators, because of their nonlinear behavior caused by thermal hysteresis. The BPID controller is simpler and easier to implement than other nonlinear control strategies, which makes it a very appealing candidate to control SMA actuators. The performance of the BPID controller has been compared with other two controllers, a conventional PID and a commuted feed-forward PIPD, controlling a real SMA actuator. To this end, a set of five tests has been defined, in which the controlled actuator must follow a series of position references. From these tests, the position and error of the actuator have been plotted, and a series of metrics has been computed to have quantitative measurements of the performance of the three controllers. It is shown that, in most of the experiments, the BPID has a better performance than the other two tested controllers, especially tracking step references. However, the power consumption is slightly higher when the actuator is controlled with this strategy, although-the difference is minimal. Also, the BPID imposes greater energy variations to the SMA actuator, which might affect its service life. Overall, the BPID controller has proved to be a viable alternative to control SMA actuators.The research leading to these results has received funding from the STAMAS (Smart technology for artificial muscle applications in space) project, funded by the European Union’s Seventh Framework Programme for Research (FP7) (grant number 312815), and from the RoboHealth (DPI2013-47944-C4-3-R) Spanish research project

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic