Self Tuning PID Control of Antilock Braking System using Electronic Wedge Brake

This paper describes the design of an antilock braking system (ABS) control for a passenger vehicle that employs an electronic wedge brake (EWB). The system is based on a two-degree-of-freedom (2-DOF) vehicle dynamic traction model, with the EWB acting as the brake actuator. The developed control structure, known as the Self-Tuning PID controller, is made up of a proportional-integral-derivative (PID) controller that serves as the main feedback loop control and a fuzzy supervisory system that serves as a tuner for the PID controller gains. This control structure is generated through two structures, namely FPID and SFPID, where the difference between these two structures is based on the fuzzy input used. An ABS-based PID controller and a fuzzy fractional PID controller developed in previous works were used as the benchmark, as well as the testing method, to evaluate the effectiveness of the controller structure. According to the results of the tests, the performance of the SFPID controller is better than that of other PID and FPID controllers, being 10% and 1% faster in terms of stopping time, 8% and 1% shorter in terms of stopping distance, 9% and 1% faster in terms of settling time, and 40% and 5% more efficient in reaching the target slip, respectively.

Methodical Approach of Detecting and Calculating Key Indicators of the Competitive Potential of a Trade-Logistics Company in the Conditions of Uncertainty

The significantly changed conditions of the activity of trade-logistics enterprises place heavy demands on the accuracy of calculation and planning key indicators of competitive potential. The article formulates and solves a scientific problem, which consists in the absence of a unified approach, within which not only the strategic potential of an enterprise is assessed and key indicators of competitive potential are determined, but also a toolkit for calculating values of these indicators in the conditions of fuzzy input data is proposed. It is suggested to determine the key indicators of competitive potential based on the characteristics of the corporate profile that have the potential for temporary competitive advantages with the possibility of increasing their organization, as well as the key weaknesses of the organization. To calculate the values of the key indicators of competitive potential and their planning in the conditions of uncertainty, it is proposed to use fuzzy inference systems. The article presents the results of approbation of the developed methodological approach in a particular trade and logistics company.

A model intended to choose optimal wooden construction software according to many criteria

Environmental friendliness gains increasing importance for people in constructing modern residential buildings. Wood is a natural polymer with unique properties that make it an excellent choice for the construction of environmentally-friendly new housing. New building technologies have significantly improved the performance of wood as a material. Modern software allows not only to get a 3D-visualization of the project, but to make the house strong and safe, save costs on construction, and reduce the build time of wooden houses. Currently, there are a large number of various software packages for wooden construction, so it may be a problem to choose the appropriate one based on the criteria of the required functionality and cost-effectiveness. Aim. This work aims at the development of a model intended to choose optimal wooden construction software according to many criteria, which will reduce the time of the scientifically grounded decision, and in such a way, increase the efficiency of a wooden house designing process. To achieve this goal, it is necessary to analyze the existing wooden construction software, to consider the criteria for selecting designing programs, to develop a model for scientifically grounded choice of software on many functional and cost criteria in the conditions of fuzzy information; and to provide an example of using this model has been developed. Methodology. A fuzzy multicriteria analysis of variants according to the Bellman-Zade scheme is used. Results. Modern building information technologies have expanded the possibilities of spatial planning of residential, working, shopping, entertainment, sports, and other areas of design. The model for the choice of wooden construction software that allows choosing software according to many functional and cost criteria in the conditions of fuzzy input information is presented. An example of using the developed model for the choice of wooden construction software is given. Practical value. The use of the developed model for wooden construction choice in the conditions of fuzzy information based on many functional and cost criteria will make the choice scientifically grounded.

Optimizing the allocation of technological resources of an air transport system in the presence of a schedule and fuzzy input data

The problem of optimal allocation of technological resources (operators) of a technical or organizational-technical system, designed to serve certain objects (operands) according to a given schedule, is considered. We take into account the necessity of incorporating, together with the main, preparatory and final operations, the possibility to select one or several operators for operand service into the service process, as well as the dependence of the operations duration on the factors characterized by uncertainty. Due to the supposed absence of statistics, expert-assigned indefinite values in the form of triangular fuzzy numbers are used. The optimization problem is formulated as a mathematical programming problem with a fuzzy criterion and clear-cut constraints, consisting in finding such a distribution of a given number of operators to serve each operand from a given set which minimizes the target function that takes into account deviations from the schedule (delay) with the service termination. Typical examples of systems for which the problem is relevant are the production complexes of air transport enterprises operating in conditions of uncertainty when it is necessary to ensure the regularity and safety of air transportation. A model example of solving the problem of allocating mobile refueling facilities at a hub airport, taking into account the peculiarities of its schedule, is presented. It is shown that the capabilities of standard personal computer software are sufficient for the solution.

A Dynamic K-means Based Clustering Algorithm Using Fuzzy Logic for CH Selection and Machine Learning Based Data Transmission

Clustering is effective method to increase network lifetime, energy efficiency, and connectivity of Sensor nodes in wireless sensor network. An energy efficient clustering algorithm has been proposed in this paper. Sensor nodes are clustered using K-means algorithm which dynamically forms number of clusters in accordance with number of alive nodes. Selection of suitable CH is done by fuzzy inference system by choosing three fuzzy input variable such as residual energy of Sensor node, its distance from cluster center and base station. Amount of data transmitted by member nodes to CH is reduced by machine learning that classify similar data at regular interval. The simulation results show that proposed algorithm outperforms other cluster based algorithms in terms of data received by base station, number of alive node per round, time of first node, middle node and last node to die for various density of sensor nodes and scalable conditions.

Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller’s output, which further inputs the rotor side converter’s speed control loop to control the rotor’s actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller’s performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller’s wide range of input wind speed.

Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment

In rehabilitation, the Fugl–Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.

A fuzzy computational approach for selecting interdependent projects using prioritized criteria

This paper presents a fuzzy computational approach for selecting project portfolio by combining fuzzy logic, Quality Function Deployment (QFD) and Genetic algorithm (GA) approaches with the consideration of prioritized selection criteria as per objectives of the organization to make decisions effectively with incomplete and ambiguous information to help in portfolio selection. This approach addresses the issues of the uncertainty of experts in selecting projects, prioritizing criteria before initiating project selection process and evaluating the number of interdependent projects for their maximal values. It completes the task in three stages. Firstly, it involves interaction with experts to extract fuzzy input about the benefits of organization and selection criteria for selecting a project portfolio. The second stage requires the application of fuzzy QFD to prioritize criteria before deciding the project portfolio. In this stage, the paper contributes a method for using fuzzy values in a distinct way for obtaining priorities of selection criteria. The final stage evaluates the candidate projects concurrently based on top priority selection criteria by considering interrelation among projects by proposing a distinct fitness function of GA. The validity of the proposed approach is demonstrated by an example that considers three experts, three objectives of the organization and four selection criteria.

Advanced detection Denial of Service attack in the Internet of Things network based on MQTT protocol using fuzzy logic

Message Queuing Telemetry Transport (MQTT) is one of the popular protocols used on the Internet of Things (IoT) networks because of its lightweight nature. With the increasing number of devices connected to the internet, the number of cybercrimes on IoT networks will increase. One of the most popular attacks is the Denial of Service (DoS) attack. Standard security on MQTT uses SSL/TLS, but SSL/TLS is computationally wasteful for low-powered devices. The use of fuzzy logic algorithms with the Intrusion Detection System (IDS) scheme is suitable for detecting DoS because of its simple nature. This paper uses a fuzzy logic algorithm embedded in a node to detect DoS in the MQTT protocol with feature selection nodes. This paper's contribution is that the nodes feature selection used will monitor SUBSCRIBE and SUBACK traffic and provide this information to fuzzy input nodes to detect DoS attacks. Fuzzy performance evaluation is measured against changes in the number of nodes and attack intervals. The results obtained are that the more the number of nodes and the higher the traffic intensity, the fuzzy performance will decrease, and vice versa. However, the number of nodes and traffic intensity will affect fuzzy performance.