In the automotive industry, improving vehicle operability tailored to individual drivers is essential for achieving a long and enjoyable driving experience. This study presents a demonstration of a personal-fit control system that adaptively adjusts vehicle characteristics to match each driver’s preferred operability. The experiment is conducted using a driving simulator. In addition, an evaluation method based on a Kalman filter is examined. The results indicate that optimal vehicle characteristics vary among drivers, and that the personal-fit control system enables adaptation to these individual preferences.

A hierarchical-type control system has been proposed as the control system architecture for products developed through MBD. In this control structure, a compensator is introduced to suppress the effects of disturbances and model errors in the actual plant. This paper proposes a PID-type compensator (GMV-PID compensator), based on Generalized Minimum Variance Control (GMVC), as a method for designing compensators in the hierarchical-type control system. The proposed method is intended for application to plastic processing machinery such as injection molding machines and film production machines. As part of ongoing work, this paper presents preliminary experimental results obtained by applying the proposed method to a pilot-scale slider-crank system, which simulates the toggle-type clamping mechanism of an actual injection molding machine.

This work in progress resaerch-to-practice mainly contributes the development of a multistage classification method to distinguish between learners who are easily predictable and those who exhibit more complex patterns. This methodological innovation not only supports the efficient allocation of educational resources but also aids in customizing learning materials for learning management systems, enhancing the relevance and effectiveness of educational content. In Japan, the GIGA School Concept, which involves the use of one internet-connected electronic device per student, has been introduced in all elementary and junior high schools since 2020. The integration of IoT (Internet of Things) technology supported by such initiatives represents a significant shift towards creating more interactive and tailored learning environments. This evolution aims not only to enhance educational outcomes by providing individual electronic devices to learners but also to optimize learning results and operational efficiency. These technological tools enable learners to progress through their educational journey at their own pace, promoting a more learner-centered approach known as “Personalized Learning.” Simultaneously, educators can more effectively monitor and manage the learning process, enabling a more responsive and adaptive educational experience. At the central of this transformation is the role of machine learning models (such as neural networks and random forests) used to predict learners' performance based on past academic achievements. This predictive capability allows for the grouping of learners based on the predictability of their performance, further refining educational strategies to meet diverse learning needs. Furthermore, this study explores the practical application of these models through numerical experiments using actual learner data. The results highlight the potential of combining IoT technology and machine learning to revolutionize the educational domain by providing personalized learning experiences. Such an approach promises not only to improve learning outcomes by aligning educational content and strategies with individual learners' profiles but also to streamline the educational process, reduce the burden on educators, and optimize the overall educational environment. In conclusion, the integration of IoT and machine learning into education presents a visionary method for addressing the unique needs and potential of individual learners.

In current Japanese education, realizing individualized and optimal learning through the effective use of ICT terminals has been required. Adaptive learning support systems are expected to help solve the above issues. This study proposes an adaptive learning support system that maximizes the learner's characteristics such as the ability and motivation to learn using an extremum-seeking control method. The proposed system is validated by using a mathematical learner model that includes the forgetting factor of human short-term memory. The simulation result shows that the system maximized the learner's characteristics by tuning the assist ratio.

Model-Based Development (MBD) has been widely used as an efficient product development method in the industrial world. In MBD, actual plant characteristics should match the ideal plant model’s characteristics which are pre-designed. However, if model errors occur between the ideal plant model and the actual plant due to various factors, it may not be possible to achieve the desired performance. A control system design method that suppresses such model errors is known as a model error compensator (MEC). This paper proposes a design method of a MEC based on Generalized Minimum Variance Control (GMVC). The effectiveness of the proposed method is verified by a numerical simulation and an experiment using the slider-crank system that simulates a toggle-type clamping mechanism of an injection molding machine.

Zebrafish is a model organism widely used in behavioral neurology and drug discovery to elucidate the neurobehavioral mechanisms of emotion. Traditionally, its emotional states were assessed from motion patterns, and respiratory information was neglected despite its reported importance. This paper presents a system that enables simultaneous motion and respiration measurement using a bioelectric signal, termed ventilatory signal. The proposed system measures ventilatory signals from an electrode array and applies a spatial distribution model to locate zebrafish and the instantaneous amplitude of the signal. The experimental results confirmed that position estimation was possible with an error of approximately 1/5 of the body length. Simultaneously, the system could detect an increase in respiration frequency caused by a 100 mg/L concentration of caffeine dose.

This research proposes an adaptive smart learning support system, whose target is a typing support system, based on the Model-Based Development (MBD) approach. The proposed typing support system adaptively adjusts the level of work so that the typing skill of a learner is smoothly grown. In this paper, the goal of an adaptive smart learning support system is briefly explained, and a concrete design scheme of a part of the support system based on the MBD approach is presented. This paper also mentions the work in progress of developing an actual typing support system.

This paper to Practice Full Paper presents about a procedure to generate the learners' data using a learner model based on first-order lag system to generate learners data. By using the generated data, the learners are classified into several groups and some learners with low understanding degree can be extracted by using the neural networks. It is necessary to provide learning support corresponding to the understanding degree of each learner in a class to improve effective learning. By providing additional education for the learners who are predicted as low degree by the proposed procedure, it is expected to take countermeasures for not becoming “dropout students” in early stage. For this purpose, predicting the understanding degree is important, and this paper employs a recurrent neural network as the predictor. Hayashida et al. (2018) have constructed to classify the learners by understanding degree at the end of the class based on some observed data such as result of quizzes, or report tasks by using FNN (Feedforward Neural Network). This paper uses a RNN (Recurrent Neural Network) which consists of feedforward signal processing and structure of the signal feedbacks because of the observed data is time-series data. This paper proposes a learner model based on first-order lag system to generate learner data for training RNNs. As experimental result of the simulation, this paper succeeds in extracting the learner group with low understanding degree in future.

The aim of this study is to predict the achievement degree of each student at the end of a lecture, based on a simple questionnaire result which regularly surveys degree of the subjective understanding conducted to students in a class. In this study, the feedforward neural networks (FNNs) and decision tree are used for the prediction and the classification. A FNN which is with a multiple input and multiple output structure is well known that it has high performance for multidimensional data prediction or classification. Therefore, FNNs are considered to be suitable for the problem dealt with in this study such that student classification based on multiple questionnaire results. Additionally, it is possible to analyze students' learning process in detail by using a decision tree that can obtain student classification rules in an explicit form. This study conducts an experiment using data of a classification six times questionnaire surveys and a final examination and constructed a system for student classification based on the the answers of three questionnaire. Experiment has succeeded in roughly classifying learners into three clusters based on achievement degree. It means that the proposed method is predict the potential comprehension degree of a student. Sequentially, by providing additional education for the students who are classified as a low degree, it is expected to be able to take countermeasures for not becoming “dropout” in early stage.

This study presents the practical results concerning model-based development educational practices implemented by Hiroshima University and Mazda Motor Corporation, an automobile manufacturer, and the consequent educational effects. In recent years, diversification and complexity of product structures have become prominent. Simultaneously, there is an increasing demand for short-period development with limited resources to promptly respond to customer needs. In order to efficiently proceed with the aforementioned development, model-based development employing computer simulation is considered effective. Model-based development is performed according to a development process called the “V-type development process (V-process).” This process includes the operations of model in the loop simulation and hardware in the loop simulation. However, several professional engineers involved in automobile development have insufficient experience in conducting model-based development. In this study, the authors propose a program that allows these professional engineers to learn the V-process using a motor control system via a hands-on approach. The program is implemented to target 159 professional engineers belonging to Mazda Motor Corporation. In the learning program, exercises for motor control system using “MATLAB/Simulink” are performed. Additionally, hardware in the loop simulation exercises using a simple simulator exclusively developed for this purpose, and control experiments with an actual motor control system are conducted. From the questionnaire surveys taken by the learners, it is revealed that the V-process exercises considerably contribute to the self-efficacy of the learners for operational performance using model-based development.

This research concerned about an online learning algorithm of the group method of data handling based proportional-integral-derivative (GMDH-PID) controller that is effective for nonlinear systems. Although a lot of PID controllers have been mainly used in industrial systems, it is difficult to maintain a desired control performance only by a PID controller with fixed control parameters due to system nonlinearities. In order to deal with such systems, a GMDH-PID controller that can adjust PID parameters according to a system change was proposed, and its effectiveness was evaluated. The GMDH-PID controller can maintain good control performance by appropriately setting its weight coefficients in the GMDH network. However, these coefficients are determined in an offline manner, thus the GMDH-PID controller cannot adapt a system if unexpected system change is happened while controlling. This paper proposes an online tuning method of the GMDH-PID controller based on the adaptive moment estimation that is one of recent attracted optimization methods. Thanks to this method, the GMDH-PID controller can adapt to unknown system change, thus applicable rage of the controller is expanded. The effectiveness of the proposed method is demonstrated by simulation examples.

In this study, a learning algorithm for a data-driven proportional-integral-derivative (DD-PID) controller that uses a database for tuning control parameters is considered. PID controllers are still used in many process systems. However, if systems exhibit nonlinearity, PID controllers with fixed PID parameters cannot achieve a desired control performance when a system's equilibrium points are changed by set point changes. To solve this problem, the DD-PID controller was proposed. The DD-PID controller can maintain good control performance for nonlinear systems because it learns the PID parameters in its database so that the control performance around each equilibrium point has a desired characteristic. In this research, a fictitious reference iterative tuning (FRIT) method is applied as the learning method of the DD-PID controller. This method can perform offline learning and obtain a desired tracking property of a closed-loop system by storing one-shot operating data given by a PID controller with fixed PID parameters into a database using the concept of the FRIT. This paper also shows that the DD-PID controller can achieve good control performance for unlearned system changes and disturbances by performing online learning with the same criterion used in offline learning. The effectiveness of the proposed method is evaluated using simulation results.

PID control schemes have been widely used in most process industries. Since the control performance strongly depends on PID parameters, it is important to suitably choose a set of these parameters. Especially, it is difficult to determine these parameters in the case where the time-delay is unknown and/or large. On the other hand, it is well-known that the generalized predictive control(GPC) design method effectively works for such systems. Then, a new PID tuning scheme(GPC-PID) which is derived from the relationship between the GPC and the PID control laws is explained in this paper. The GPC-PID controller has a user-specified parameter, and it is adjusted from the viewpoint of the robust stability. The effectiveness of the GPC-PID controller is verified by application to a temperature control system whose time-delay can be changed to test the control scheme.

Proportional-integral-derivative (PID) control schemes have been widely used in most industrial control systems. However, it is difficult to determine a suitable set of PID gains because most industrial systems have nonlinearity. On the other hands, the cerebellar model articulation controller (CMAC) classified as neural networks has been proposed, and design scheme of an intelligent PID controller has been proposed. However, the CMAC-PID controller has a problem that CMACs used as PID tuners must be trained in an online manner to get their optimal weights. In order to train CMACs in an offline manner, a combination of CMAC learning and a fictitious reference iterative tuning (FRIT) scheme, which is called CMAC-FRIT scheme, has been proposed in our previous research, and an effectiveness of the method has been evaluated only by simulations. FRIT is a scheme to determine control parameters of linear controllers by using a set of experimental data. According to the CMAC-FRIT scheme, a CMAC-PID tuner can be trained in an offline manner by using a set of operating data. In this research, the proposed CMAC-PID controller is implemented and applied to a magnetic levitation device.

PID control schemes have been widely used in most industrial control systems, but it has been difficult to determine a suitable set of PID gains as most industrial systems are nonlinear. Although there have been proposals for Cerebellar Model Articulation Controller (CMAC) classified as neural networks, and a design scheme for an intelligent PID controller that uses a CMAC-PID tuner, CMAC-PID tuners have two problems. One issue is that a CMAC must be trained on-line in order to obtain their optimum weights. Another issue is that the CMAC has high computational costs and memory reqirements for some micro controllers. In order to train a CMAC off-line, a CMAC-FRIT (a combination of CMAC and Fictious Reference Iterative Tuning) scheme has been proposed in previous research. FRIT is a scheme to determine control parameters by using a set of experimental data. According to the CMAC-FRIT scheme, CMAC-PID tuners can be trained offline by using a set of operating data. This paper proposes to address the problems of memory requirements and computational costs with a method that expresses a CMAC-PID tuner as a simple nonlinear function by using a Group Method of Data Handling (GMDH). According to the proposed method, a network of N-Adaline (units expressed by a simple nonlinear function) replaces a CMAC-PID tuner (which is trained in advance with a set of operating data), enabling the proposed algorithm to be easily programmed on a micro controller, even if it is a commodity micro controller. The effectiveness of the proposed method is validated by an experiment in order to demonstrate the proposed method, the algorithm is programmed on a general purpose micro controller, which is applied to a magnetic levitation device.