In this paper, a distributed adaptive consensus law with fault compensation for a heterogeneous multi-robot system (MRS) is proposed. The design paradigm adopted in this work involves a leader-following cooperative algorithm featuring two distinct adaptive coupling gains to compensate for multiple additive time-varying faults. Exacerbating the situation, the follower robots commissioned in the leader-following mission are non-identical due to their dynamic characteristic as normally exist in a physical setup. The capability of the proposed scheme is investigated and compared with the other two recent works in two facets; one is to gauge how the algorithm is able to mitigate faults of varying nature in the presence of heterogeneous robot(s) while maintaining the platoon formation during the leader-following task; two is the ability to cope with subsequent topology reconfiguration. The stability and the robustness of the proposed scheme against bounded time-varying faults are proven using rigorous Lyapunov analysis. The proposed control strategy exempts the use of an observer or estimator, thereby simplifies the synthesis and implementation on mobile robots. The simulation results of the proposed adaptive consensus law demonstrate the best performance as compared to the other two recent works in the presence of multiple faulty robots.

This paper presents the design and implementation of project-based learning for the System Modeling and Analysis course using a real-world gantry crane system problem in the first control engineering course for electrical engineering students. This project-based approach covers several topics including modeling, simulation, control and analysis. Several activities were conducted as part of the implementation of this project-based learning to achieve the intended learning outcomes of the course and the students’ expectancies values such as attainment, intrinsic, and utility values. Both individual and group assessments were performed to evaluate the abilities and knowledge, as well as to observe and provide feedback on the development of the required skills. The impact of this project-based learning implementation is evaluated by analyzing students’ feedback comments and suggestions. The findings indicate that students’ can develop their ability to solve a practical problem systematically as well as enhance their understanding and motivation towards learning. Hence, this indicates that all expectancy values considered are achieved.

The 1 MWth TRIGA PUSPATI Reactor known as RTP undergoes more than 37 years of operation in Malaysia. The current core power control utilized Feedback Control Algorithm (FCA) and a conventional Control Rod Selection Algorithm (CRSA). However, the current power tracking performance suffers and increase the workload on Control Rod Drive Mechanism (CRDM) if the range between minimum and maximum rod worth value for each control rod has a significant difference. Thus, it is requiring much time to keep the core power stable at the power demand value within the acceptable error bands for the safety requirement of the RTP. In conventional CRSA, regardless of the rod worth value, the lowest position of the control rod is selected for up-movement to regulate the reactor power with 2% chattering error. To improve this method, a new CRSA is introduced named Single Control Absorbing Rod (SCAR). In SCAR, only one rod with highest reactivity worth value will be selected for coast tuning during transient and the lowest reactivity worth value will be selected for fine-tuning rod movement during steady-state. The simulation model of the reactor core is represented based on point kinetics model, thermal-hydraulic models and reactivity model. The conventional CRSA model included with control rod position dynamic model and actual reactivity worth curve data from RTP. The FCA controller is designed based on Proportional-Integral (PI) controller using MATLAB Simulink simulation. The core power control system is represented by the integration of a reactor core model, CRSA model and FCA controller. To manifest the effectiveness of the proposed SCAR algorithm, the results are compared to the conventional CRSA in both simulation and experimentation. Overall, the results shows that the SCAR algorithm offers generally better results than the conventional CRSA with the reduction in rising time up to 44%, workload up to 35%, settling time up to 26% and chattering error up to 18% of the nominal value.

In recent manufacturing industry, improving the manufacturing process is of paramount importance. One area that holds great potential for enhancement is the application and manipulation of maintenance data. By effectively leveraging this data, manufacturers can optimize maintenance schedules, leading to increased efficiency, reduced costs, and minimized downtime. However, the challenge lies in handling vast amounts of maintenance data that often come in various formats, making it difficult to extract valuable insights. Without proper analysis, this unprocessed data can result in unforeseen issues, costly disruptions, and extended downtime periods. To overcome this obstacle, modern manufacturing companies are turning to advanced technologies such as language modelling, text classification, machine translation, and Named Entity Recognition (NER). To the best of our knowledge, no investigation has been conducted to assess the impact of text preprocessing on NER performance. Improving the initial stage of NER, such as text preprocessing, can enhance NER performance which leads to the training model’s efficiency performance. In this study, Hidden Markov Model (HMM) is employed to improve NER performance by utilizing oversampling and text preprocessing techniques. The study is performed without IOB labelling and consider seven specific entities and the preprocessing text tasks include tokenization, lemmatization, erase punctuation, stop words removal, and elimination of long and short words. As a result, HMM for NER with oversampling and preprocessed text outperformed the one without any of both by 20.10% and 27.59%, respectively, due to consideration of significant classes and words among the entity classes in preprocessed factory reports. This finding highlights the importance of text preprocessing method selection in NER and its capability to optimize maintenance schedule and reduce downtime.

At present, the power tracking performance of nuclear Reaktor TRIGA PUSPATI (RTP) is considered unsatisfactory performance due to relatively long settling time during transient and a chattering noise during steady-state power output. Application of the conventional Feedback Control Algorithm (FCA) as a power control technique is proven to be inadequate to keep the core power output stable and within tight multiple parameter constraints for the safety demand of the RTP. Hence, the present study proposed a multipronged core power control strategy improvement through manipulation of the current Control Rod Selection Algorithm (CRSA), Control Rod Speed Design (CRSD), and Power Change Rate Constraint (PCRC) which are part of the core power control design. In this paper, the profiling and analysis of the multipronged core power control strategy are presented. The model for core power control consists of mathematical models of the reactor core, FCA controller, and a series of multipronged models. The mathematical models of the reactor core are based on the point kinetics model, thermal-hydraulic model, and reactivity model. The reactor model is integrated with the FCA controller and a combination of CRSA-CRSD-PCRC models. The power tracking performance of the proposed control strategy and conventional FCA is compared via computer simulation. Overall, the results show the multipronged FCA offers a wider options for optimum operation of the TRIGA reactor.

Sustaining water quality is critical in order to avoid any harmful health consequences on end-user consumers. The detection of microbial impurities at the household level is the foundation of water security. Water quality is now monitored only at water utilities of infrastructure, such as water treatment facilities or reservoirs. This research provides first-hand scientific understanding of microbial composition presence in Malaysia's household point-of-use (POUs) water supply influenced by seasonal fluctuation, standstill periods and flow dynamics by using NIR-Raman spectroscopic technique. According to the findings, 20% of water sample were contaminated by pathogenic bacteria, which are Legionella and Salmonella cells. A comparison of the spectra reveals significant signature peaks (420 cm-1 to 1800 cm-1) including species-specific bands. This demonstrates the importance of regularly monitoring POUs water quality to provide safe and clean water supply to homeowners. Conventional Raman spectroscopy, up-to-date is no longer suited for real-time monitoring. Therefore, this study introduced an alternative micro-spectrometer to give a rapid and sustainable way for monitoring POUs water quality. Assessing microbiological threats in water supply becomes more reliable and efficient by leveraging IoT protocols.