Hasitha Ruwan Jayetileke

This paper presents a wireless sensing platform for Industrial applications such as Industrial 5.0. It uses Xbee S2C and Arduino Due/ESP32 for wireless communication and offers customizable data resolution, sensor array size and synchronization at the same sampling iteration. The system effectively monitors reference trajectories up to 20 Hz and eliminates costly wired connections, improving data acquisition efficiency. This study contributes to the development of wireless telemetry systems for even Industrial 5.0 implementation.

This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.

Significant effort has to be devoted to surviving the businesses relying on fleet vehicles in the year 2020 and ahead as the novel coronavirus (COVID-19) epidemic became pandemic. Executing profitable business while keeping the staff safe and productive is a critical challenge to deal with. To find a solution, we focus on driver management out of major functions in fleet management such as vehicle, driver, and operation management. We were unable to identify a study conducted to capture real-time data on a ride in a fleet. Therefore, to fill that gap we implemented a cost-effective real-time Fleet Management System (FMS) using data analytics with the use of ESP32 SIM800L with reprogrammable capabilities. Fleet can use this system to monitor real-time data on vehicle location, remaining time to the destination, vehicle speed, and distance traveled. Moreover, the system can be personalized as it has reprogrammable features to be enabled or disabled based on the customer's preference. Once the data is captured through the Global Positioning System (GPS) receiver, data will be transmitted via General Packet Radio Service (GPRS) to two remote servers. One server is hosted locally with SQL and where the other is hosted in a cloud environment with a Firebase realtime database. The vehicle location is tracked using GPS. For fast data transfer, 3G Global System for Mobile communications (GSM) with ESP32 800L microprocessor was used. A web-based graphical user interface is developed to analyse and present the transmitted data. Vehicle information can be viewed and located on the web application in form of google maps. Real-time data analytics is used with Firebase's real-time database. Furthermore, Short Message Service (SMS) facility is made available for the driver to communicate with configured mobile numbers

In this paper, a dynamic optimization self-adaptive controller for a four-wheel independent drive electric rover has been investigated to enhance the dynamic stability. The proposed self-adaptive AI controller is based on dynamic Fuzzy Logic (FL) control mechanism. The dynamic self-adaptive properties have been integrated into the proposed FL controller through a dynamically tuned Particle Swarm Optimization (PSO) mechanism. Nevertheless, the dynamic FL controller and the dynamic PSO mechanism has been synchronized together for every sampling instance k to obtain the optimum performance of the electric rover. In this electric rover, all the four wheels have a fixed orientation and each wheel powered by a 250-Watt Brushless Direct Current (BLDC) motor through separate gear ratio mechanisms to obtain the desired torque and angular velocity. Therefore, the steering mechanism was achieved in this rover through the proposed AI controller, which was based on the differential speed mechanism. However, this paper presents the control methodology and obtained test results related to straight road tests under different slippery road conditions. The rover test results show that on different slippery road conditions the proposed PSO based FL controller has maintained the wheel slip ratio of all the four wheels which was less than 0.35 approximately. Here, the translational speed has been limited to 40 km/hr approximately within its recorded top speed of 90 km/hr while maintaining the desired fix orientation.

This paper presents the speed regulation of a Sensored BLDC (Brushless Direct Current) Motor through a Genetic-Fuzzy controller, where the Sensored BLDC motor was modeled in MATLAB Simulink environment according to State-Space analysis approach. When designing Fuzzy Logic controllers (FLCs) there is no generalized defined approach and these controllers are mainly based on linguistically defined variables which are non-linear elements, that are impossible to model accurately. Our test results shows that the fuzzy controller's output highly depends on the fuzzy rules. In some situations, very experienced and a skillful expert's solutions (fuzzy rules) even may not satisfy the desired output. In many cases FLCs rule bases have been designed according to trial-and-error method which makes the optimization of the solution very difficult. As a solution, FLC of the Sensored BLDC motor was tuned through a stochastic search optimization technique which is based on GA (Genetic Algorithm) and the GA parameters (Crossover, Mutation rates etc.) adapted through another TSK-FLC (Takagi-Sugeno-Kang type FLC) in real-time. The optimization stochastic search process was implemented using a fitness function index (i.e. a predefined threshold level) which is calculated from the population (randomly generated solutions by the GA) based on the E (Error), MAE (Mean Absolute Error) and the RMSE (Root Mean Square Error). The simulated test results shows the proposed control technique has effectively reduced the maximum overshoot, settling time, steady state error and the rise time by 12%, 15%, 11% and 1% respectively. But further research is needed to optimize the search algorithm to increase the Genetic-Fuzzy controller's efficiency and the stability to withstand external disturbances while increasing the frequency for various desired input signal wave pattern trajectories.

Designing and developing AI controllers on separately dedicated chips have many advantages. This paper reviews the development of a real-time fuzzy logic controller for speed control of a dc motor using Arduino Due board. The proposed fuzzy logic controller is based on Mamdani approach and has been tested on the aforementioned high performance microcontroller board and using MATLAB. During the real-time operation the dc motor behavior and the fuzzy controller's response were plotted and the data were stored in MATLAB without interrupting the fuzzy logic controller. Based on these observed information, the system settling time and the rise time reduction were calculated for each input wave patent trajectories while increasing the wave frequency. It was noted that the system overshoot is negligible. Utilizing the aforementioned parameters the Arduino Due board performance was analyzed with the fuzzy logic speed control approaches of dc motors made by past researchers as mentioned above. The system response shows a satisfactory performance for this particular dc motor application when the input signal (desired output signal) frequency is less than 2 Hz, but further research is needed when identifying the optimum performance of the Arduino Due board for different fuzzy logic algorithms while increasing the desired input signal frequency.