Data-driven control has recently attracted attention because it enables the calculation of control parameters without requiring a model. However, in some cases, the evaluation criteria used for parameter optimization in data-driven control schemes differ from those in model-based control schemes. This mismatch can degrade control performance. This study proposes a prefilter design to reduce this mismatch in two-degree-of-freedom control systems. By using a desired model with adjustable parameters, the evaluation criteria can be aligned and the effectiveness of the proposed scheme is confirmed through simulations on higher-order delay systems. Additionally, if model matching cannot be achieved at that point, the initial operational data will also be affected, and the degree of impact will be considered using multiple initial data sets.

If the stress applied to a machine during operation can be estimated, the degree of fatigue and the machine’s lifespan can be calculated from the stress state. The calculated fatigue state can also be used to detect abnormal stresses that may accelerate failure. In this study, stress estimation for excavator attachments is performed using database-driven modeling to detect abnormal stresses. To reduce false detections, a reliability evaluation is conducted on the estimated stresses, and abnormal stresses are identified only when the estimates are deemed reliable.

The Performance-driven PID (Pd-PID) control method, which utilizes the minimum variance control index (MV-Index), has garnered significant attention. In Pd-PID, a deadbeat control system is always considered optimal for evaluation, regardless of the system’s response speed. As a result, systems with slower responses receive lower evaluations, even when good control performance can be obtained. Furthermore, the Pd-PID method continuously updates PID parameters to improve control performance evaluation, which can ultimately lead to system instability. This paper addresses this issue and proposes a new control performance evaluation index to replace the MV-Index.

The Sustainable Development Goals (SDGs) have recently garnered increased attention. Additionally, Japan has been advocating for the achievement of "Society 5.0". In particular, the construction industry is promoting "i-Construction" which involved the automation and semi-automation of hydraulic excavators. However, some situations require human judgment, particularly during unforeseen circumstances, underscoring the need for cooperative systems between humans and machines at construction sites. To realize such systems, evaluating human operability is essential. Therefore, the proposed method conducted system identification using closed-loop operating data comprising humans and machines. Furthermore, changes in human operability were captured by evaluating the poles calculated during the process. The effectiveness of this method was experimentally verified using a hydraulic excavator. The findings demonstrated that closed-loop system identification can be performed using operational data. Additionally, the results confirmed the potential for improving the development pf hydraulic excavator operators through shared control experience.

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.

The increasing adoption of technologies, such as building information modeling underscores the significance of leveraging data and digital technologies on construction sites. Research and development of data-driven automated excavators are underway. Cooperative construction using multiple automated excavators is required to improve productivity. For realizing the construction method, utilizing operation and construction data collected in the construction sites is effective. This study presents cooperative motion planning based on a data-driven numerical optimization method. By leveraging a database of historical construction data, the search range for optimization calculations is limited, enabling the efficient calculation of motion plans for automated excavators that avoid collision with nearby machinery. The effectiveness of the proposed method was validated through simulations and experiments conducted with radio-controlled (RC) excavators.

Failure can be predicted based on the load conditions by estimating the load applied to a machine during operation. However, the load applied to construction machinery, such as hydraulic excavators, varies depending on the work environment and nature of the work; therefore, it is necessary to construct a load estimation model that adapts to these conditions. This study proposes a load-estimation modeling method that builds a database for each operation, selects a database based on the operation judgment by long short-term memory, and performs database-driven modeling. Applying the proposed method enables the selection of an appropriate database according to the work content and perform a highly accurate load estimation. The effectiveness of the proposed method was verified experimentally using a radio-controlled excavator.

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.

Since the proportional-integral-derivative (PID) controller has been proposed and developed for the feedback control, the implementations of this approach have variable employed in the industrial process control, such as the chemical and velocity measurement. However, due to the fact that many plants are of nonlinear system, the modeling of such systems is difficult, which requires the adjustment of the control parameters (PID gains) in response to the transitions of the system characteristics. In this paper, one of the adaptive learning control methods, database-driven PID (DD-PID) control approach is discussed firstly, then a newly proposed method is designed via a cerebellar model articulation controller (CMAC) to reduce the computational burden and memory consumption for the applications. Finally, an implementation of a hydraulic motor system using these two methods are presented and verified according to the quantitative evaluations. In addition, since the proposed method is realized by the dynamic data and memory maps, it is considered that the proposed method is one of a practical dynamic method for the industrial application.

Sustainable development goals (SDGs) have attracted considerable attention in recent years. In Japan, “Society 5.0” has been proposed and promoted by various organizations as a vision for a future society linked to achieving these SDGs. Particularly, “i-Construction” has been promoted in the construction industry. For example, hydraulic excavators are automated (or semi-automated). Nevertheless, having a sense of accomplishment and motivation for the work through active operations is important for operators. In this study, a control system that automatically adjusts the operational assistance rate according to the user is proposed. Modeling a person is difficult because they exhibit time-varying and nonlinear characteristics. Therefore, the control system uses work data to evaluate the differences in the desired characteristics and adaptively adjusts the operational assist rate. The proposed method was implemented on a hydraulic excavator and its effectiveness was verified.

This paper addresses an optimization issue that the Nelder-Mead (NM) method is implemented to optimize initial database which is used to design Database-Driven (DD) proportional-integral-derivative (PID) controller. As it is well recognized that PID controller, which is considerable applied in industrial processes, is adopted and the parameters tuning are based on DD approach without requirement of model information. The existing DD approach needs to collect a batch of closed-loop operation data, which is used to generate initial database. It is still an open problem that the way of processing data involved in initial database. The proposed scheme considers the NM method, as a free derivative method, to optimize initial database. However, in the evaluation stage, the NM method requires the model information to calculate the output aiming at comparing objective function, therefore, a better vertex can be computed. The fictitious reference iterative tuning (FRIT) is introduced in the proposed scheme to generate fictitious output based on the fictitious reference which is obtained based on input and output data stored in initial database. As a result, the optimized initial database is accomplished, based on which the PID controller then can be designed in Just-in-Time manner. In addition, the efficiency of the proposed scheme is illustrated with a numerical example.

As a nonlinear control algorithm, database-driven PID control (DD-PID) approach has been proposed to learn PID parameters based on a database. This method is based on a strategy in which PID parameters are determined based on neighboring data extracted based on the similarity between the query (current input/output data) and the information vector contained in the database. Since sorting operation is required in extracting the neighbor data, it is impossible to finish the calculation within a certain sampling interval for systems with fast response time, which is one of the hindrances in industrial applications. In addition, the DD-PID requires a large amount of storage memory in the database in order to obtain the desired control performance. On the other hand, one of the neural networks is the cerebellar model articulation controller (CMAC). It is a table-referenced adaptive learning controller. The major advantage of this method lies in the reduction of memory and computational load. This paper discusses a realization of the DD-PID by effectively utilizing the advantage of the CMAC.

This paper addresses a regulation problem of non-linear systems via database-driven nonlinear generalized predictive controller without model information. In industrial processes, lots of controlled systems with unknown time-delay and strong nonlinearity, are difficult to be handled in terms of control performance. Advanced controllers are considered to be established to deal with those nonlinear systems. In several design methods, advanced controllers are designed based on model information. However, it is time- and cost-consuming to identify the model of controlled systems, and requires regular maintenance to maintain acceptable performance. The database-driven approach has been attracted attentions to tackle those issues without model information. The controller can be designed and tuned only based on data, which is the main feature of this approach. Besides, the database-driven approach can deal with strong nonlinear systems. Additionally, the Generalized Predictive Control (GPC) is one of predictive controllers and widely applied in industrial processes. The GPC controller is developed based on multi-step prediction, therefore, it is effective to those systems subject to unknown or time-delay. As a result, a nonlinear GPC controller in the proposed scheme inherits the advantage of GPC, and is also tuned by the database-driven approach. The effectiveness and benefits of the proposed scheme are demonstrated through a numerical simulation and a comparative study.

The work object characteristics of a hydraulic excavator differ depending on the construction site, furthermore, they change during the work. Therefore, it is difficult to derive the desired control performance in the control system of a hydraulic excavator without considering the characteristics of the work object. This paper presents a database-driven model predictive control system that considers the characteristics of the work object. The characteristics of the excavator-environment interaction are modeled as a spring-mass-damper system with two degrees of freedom, and used in the proposed control system. The effectiveness of the proposed method is verified by a simulation using the model. The control performance is improved by applying the proposed method to the controlled object whose characteristics are subject to change.

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 addresses a design problem of a Proportional-Integral-Derivative (PID) controller with new adaptive updating rule based on Reinforcement Learning (RL) approach for nonlinear systems. A new design scheme that RL can be used to complement the conventional control technology PID is presented. In this study, a single Radial Basis Function (RBF) network is introduced to calculate the control policy function of Actor and the value function of Critic simultaneously. Regarding to the PID controller structure, the inputs of RBF network are system error, the difference of output as well as the second order difference of output, and they are defined as system states. The Temporal Difference (TD) error in this study is newly defined and involves the error criterion which is defined by the difference between one-step ahead prediction and the reference value. The gradient descent method is adopted based on TD error performance index, then the updating rules can be obtained. Therefore, the network weights and the kernel function can be calculated in an adaptive manner. Finally, the numerical simulations are conducted in nonlinear systems to illustrate the efficiency and robustness of the proposed scheme.

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.

Superior operation skills for hydraulic excavators are learned over time. Therefore, it is difficult to improve the skills of non-expert operators quickly, and their work needs to be supported. This paper presents a control system, which realizes smooth digging (similar to that performed by expert operators), based on the Center of Mass velocity of the attachment as an index. In addition, corresponding to the system switching caused by the digging reaction force, a controller gain tuning method that uses the Fictitious Exogenous Signal is proposed. The proposed method is applied to a hydraulic excavator, and its effect is verified.

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.

The data-driven control scheme has been proposed to control nonlinear systems by adaptively tuning controller parameters based on the database. In the data-driven control scheme, the fixed number of neighbors' data are selected to calculate controller parameters. Therefore, the control performance cannot be improved when the inappropriate neighbors' data are chosen. In other words, the inappropriate controller parameters are calculated when query data is not included in the database. In this study, the data-driven control scheme using the kernel density estimation is proposed. The kernel density estimation can calculate the similarity between query data and database. According to the proposed scheme, controller parameters are calculated based on the abnormality which is obtained by the similarity mentioned above. The effectiveness of the proposed scheme is evaluated by a numerical example.

Virtual reference feedback tuning (VRFT) and fictitious reference iterative tuning (FRIT) are the data-driven tuning schemes for directly designing feedback controllers. These schemes have been extended to two-degree-of-freedom (2DOF) control systems in recent years. The conventional design schemes of 2DOF controllers need a complicated two-stage tuning process. This paper describes a one-stage tuning scheme for the 2DOF control system using a fictitious exogenous signal based on a data-driven method. In the proposed scheme, 2DOF controllers are designed with one criterion, and the least squares method is applied to find the optimal set of 2DOF controller parameters. According to the proposed scheme, the reference models are determined based on the stability margin that is quantified by the sensitivity function. It is also possible to design a control system taking robustness into account. Finally, the effectiveness of the proposed scheme is numerically verified using a simulation example.

In process industries, it is necessary to maintain the user-specified control performance in order to achieve desired productivity. This paper describes a design scheme for control performance evaluation based PID controllers in which the “controller design” is driven by a “control performance assessment” scheme. The proposed scheme is a type of data-oriented adaptive controller whose controller parameters are tuned directly without requiring a model of the process. Unlike previous performance-based adaptive schemes, the control objective function to be minimized takes into account controller error variance as well as the manipulating variable or the control effort variance. The effectiveness of the proposed scheme is verified by using a simulation example and the benchmark stirred tank-heater system.

Lots of systems in the industries have nonlinearity and those structures are generally complicated. Therefore, it is difficult to express the system as mathematical model. As one approach for this problem, database-driven modeling (DDM) method which is a kind of Just-In-Time(JIT) modeling has been proposed as a method to construct a non-linear model for controlling. However, DDM method has a problem that modeling accuracy deteriorates in a complicated system including many variables not related to output. This study introduces the variable evaluation/selection method based on the Random Forest(RF) to improve the accuracy of DDM method. RF can quantify the degree of contribution to variable prediction as importance. The effectiveness of the proposed scheme is numerically verified by a simulation example.

In this paper, a new PID controller design scheme is proposed for non-linear systems. According to this scheme, the system model is first generated by using local linear models. Then, it is described a design of internal model controllers(IMC) based on the local linear models. The internal model control has a simple structure and has a high robustness for system uncertainties. However, there are few studies of IMC schemes for non-linear systems. On the other hand, lots of controlled systems have non-linearities. Finally the effectiveness of the newly proposed control scheme is experiment examples in comparison with the conventional control methods for non-linear systems.

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.

Proportional-integral-derivative (PID) control algorithm is playing an important role in industrial control process. However, for nonlinear control objects, it is difficult to obtain the desired control performance by using a typical PID controller. Therefore, some intelligent PID controllers are proposed and one of them is a PID controller with some cerebellar model articulation controllers (CMACs). In this controller, CMACs are utilized as a control parameter tuner and PID control parameters are calculated as the output of CMAC. The CMAC obtains higher accuracy by increasing the number of label for each weight table while larger memory is needed and generalization ability decreases. On the other hand, the CMAC costs less memory while obtains higher generalization ability and the accuracy decreases. Hence, a novel CMAC that number of label for each weight table can be decided respectively is proposed so that the accuracy is compatible with the generalization ability. Moreover, the efficiency of the memory allocation is improved. The proposed method employs hierarchical clustering to perform specified numbers of label for each weight table. At last, the effectiveness of proposed method is verified by applying to a nonlinear system numerically.

Industrial equipment, such as bulldozers, excavators, and cranes, requires human operation. Construction machines with better operability are necessary in order to improve productivity. The evaluation of such operational equipment is performed in order to obtain better operability, at the design stage, by sensory evaluation from a specific evaluator. Therefore, the design and evaluation of operational equipment depend on subjective evaluations. However, subjective evaluation requires a skilled evaluator or plenty of sample data. In this study, a quantitative evaluation is considered with regard to the responsiveness of excavator operation. The evaluated system is approximated by a low-order system, such as a first-order lag system with dead time, based on the control engineering approach. The parameters of the estimated system, i.e., dead time and time constant, are considered in the numerical evaluation of excavator operation responsiveness. The system is operated and evaluated by operators; then, the score by sensory evaluation is obtained. The estimated parameters and evaluated score are investigated.

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.

Some design schemes of data-driven control methods which called model free methods have been proposed in nearly a decade. Fictitious Reference Iterative Tuning (FRIT) method which is one of the data-driven control has good advantages. This method can calculate the control parameters by the operation data which is under closed loop control. This paper provides the PID parameter calculation based on FRIT with the control performance assessment index. The effectiveness and usefulness of the proposed approach is evaluated 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.

In this study, a nonlinear system is controlled using a linear adaptive method. A nonlinear system is approximated a linear model at each operating point, and a control law is designed based on the approximated linear model. To obtain a suitable linear model at each operating point, many linear models are simultaneously identified. However, the computation load for identifying many models is considerably heavy. Hence, many linear models are identified using General-Purpose computing on Graphics Processing Units (GPGPU). In this study, the assessment of modeling performance is newly introduced. As a result, the control system is updated only when modeling performance is degraded, and frequent update of a control law can be avoided. Finally, numerical results are shown to demonstrate the effectiveness of the proposed method.

The Advanced Land Observing Satellite-2 (ALOS-2) carries the state-of-the-art L-band Synthetic Aperture Radar (SAR) called PALSAR-2 which succeed to the ALOS/PALSAR. ALOS-2 was launched on 24th May 2014, and is performing the initial functional verifications of onboard components and systems. This paper describes the initial launch operation results, and the plan of the performance evaluation regarding to the orbit control and determination.

In general, the problem of infinite impulse response (IIR) filters design has a non-linearity due to the presence of denominator polynomial; hence, an iterative optimization is usually needed to obtain the solution. In this paper, we independently generate several time series signals in stop-band, transition zone, and pass-band based on an algorithm which generates a Gaussian stochastic process with a prescribed frequency characteristic. After that, we synthesis the input signal and its ideal output signal, and can obtain the IIR filter by using system identification method from these signals. The advantage of the proposed method is that we can compute the IIR stable digital filters as a closed-form solution. We can approximate the given frequency response and the constant group delay without using iterative optimization. Finally, a design example is presented to illustrate the effectiveness of the proposed method by designing a high-pass IIR digital filter. As a result, we confirm the filter designed has a good magnitude response and an almost flat group delay.

ALOS-2, the next-generation Japanese SAR satellite, is designed to perform autonomous precise orbit control of Earth-referenced repeating orbits for effective repeat-pass SAR interferometry. The orbit control accuracy requirement of ALOS-2 is 500m (95%) with respect to the reference Earth-fixed flight path. This accuracy is guaranteed for all latitudes by not only drag-makeup maneuvers but also frequent inclination maneuvers. The on-board software of ALOS-2 can handle operations of orbit determination, maneuver prediction and planning, and maneuver executions for both drag-makeup maneuvers and inclination maneuvers. This feature of autonomy is expected to be great help for efficient ground operations of ALOS-2.

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.

PID Control schemes have been widely used in most process control systems. However, most process systems are nonlinear, it is difficult to obtain good control performances using simple controllers with the fixed PID parameters. This paper describes a design scheme of nonlinear controllers with a local linear models structure. The linear model is designed using the data-driven(DD) approach. The DD approach is effective in describing the nonlinear components. In addition, it is easy to construct the local linear models, if the system model can be suitably designed. According to the proposed control system, the desired control performance can be obtained for nonlinear systems.

With the advance of an aging society, people who are physically handicapped have specific needs concerning mobility assistance in relation to their respective living conditions. Moreover, operating an electric wheelchair indoors in confined spaces requires considerable skill. This paper presents an obstacle avoidance support system for an electric wheelchair, using reinforcement learning. The obstacle avoidance is semi-automatically supported by the Minimum Vector Field Histogram (MVFH) method. The MVFH modifies the user manipulation and assists the obstacle avoidance. In the proposed scheme, the modification rate is adjusted by reinforcement learning according to the environment and the user condition. The newly proposed scheme is numerically evaluated on a simulation example. Furthermore, the proposed scheme was applied to an experimental electric wheelchair, and the effectiveness of the proposed technique was verified in a real operating environment.

PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed. Although it is important to choose the suitable nominal model in designing the robust controller, it is not usually easy. In this paper, a new robust PD controller design scheme is proposed, in which the suitable nominal model is designed using a real-coded genetic algorithm.

This paper proposes training equipment using a balloon actuator-sensor system (BASS) for persons unable to move their hands because of injury or disease. BASS is able to control the stiffness adaptively using an adaptive learning impedance controller. The pneumatic actuator has excellent compliance and flexibility, which is good for a humanmechanical system. However, it is also nonlinear, hence high precision control is difficult. Therefore, a CMAC-PID control scheme is installed. Finally, the BASS control performance is evaluated in a control experiment.

In this paper, the soft constrained Nash games for multiparameter singularly perturbed systems (MSPS) are discussed. After establishing the asymptotic structure and local uniqueness of the solution for the cross-coupled sign-indefinite multiparameter algebraic Riccati equations (CSMARE), a new algorithm that is based on Newton’s method for solving the CSMARE is established. As a result, it is shown that the proposed algorithm attains quadratic convergence. Moreover, in order to solve the cross-coupled multiparameter algebraic Lyapunov equation (CMALE) that appears in Newton’s method, a new gradient-based iterative algorithm is proposed.

This paper investigates an application of fuzzy control to the guaranteed cost control problem of decentralized robust control for a class of discrete-time uncertain large-scale systems. Based on linear matrix inequality (LMI) design approach, a class of decentralized local fixed state feedback controllers with additive gain perturbations is established. The novel contribution of this paper is that in order to reduce the large cost caused by the LMI conditions, the fuzzy controllers are substituted for the additive gain perturbations. Although the fuzzy controllers are included in the uncertain large-scale systems, the closed-loop system is asymptotically stable. As another important feature, the control input matrices allow uncertainty and the conservative assumption is not needed compared with the existing result that is based on the neural networks. In order to demonstrate the efficiency of our proposed controller, the simple numerical example is given.

PID control schemes have been widely employed for most process systems represented by chemical processes. However, it is a very important problem how to tune PID parameters, because these parameters have a great influence on the stability and the performance of the control system. On the other hand, lots of works for the robust control have been carried out to cope with system uncertainties. Then, some PID parameter tuning methods have been proposed based on the robust stability. However, if the range of uncertainties is very wide, the control performance becomes quite conservative. By the way, the support vector machine (SVM) has been proposed as one of the pattern recognition methods and gets lots of attention in last decades. The main motivation in this paper is to present a design scheme of controllers with the switching structure, in which some robust PID controllers are suitably switched using the SVM. Finally, the proposed control scheme is numerically evaluated on a simulation example.

This paper deals with a design scheme of the speed control for a 3-mass system, which has a hybrid structure of a sliding mode controller and a cerebellar model articulation controller (CMAC). The nonlinear control part in the sliding mode controller is firstly designed, and then a real-coded genetic algorithm is utilized to optimize the output from the sliding mode controller. The CMAC compensates the sliding mode controller so that the control performance is improved well. In this proposed scheme, the divergent property of the CMAC caused by a disturbance is fairly suppressed by effects of the nonlinear control part. The behavior of the newly proposed control scheme is experimentally examined in comparison with using only the sliding mode controller.

With advance of an aging society, the persons who are physically handicapped have their respective needs about mobility assist with their living conditions. Moreover, operating an electric wheelchair indoors in confined spaces requires considerable skill. This paper presents an obstacle avoiding support system for an electric wheelchair, using reinforcement learning. The obstacle avoidance is semi-automatically supported by the minimum vector field histogram (MVFH) method. The MVFH modifies the user manipulation and assists the obstacle avoidance. In the proposed scheme, the modification rate is adjusted by the reinforcement learning according to the environment and the user condition. The newly proposed scheme is numerically evaluated on a simulation example.

Since most process systems have nonlinearities, it is necessary to consider controller design schemes to deal with such systems. A new method of the generalized minimum variance control (GMVC) for nonlinear systems is proposed. In designing the GMVC, the predictive outputs must be estimated exactly by using the nonlinear model. However, because most nonlinear systems have a complex structure, it is difficult to make a suitable model for such systems. Then, the new method of modeling for nonlinear systems is also proposed by using GA. According to the newly proposed scheme, the structure and parameters of the nonlinear model are automatically generated. Finally, the effectiveness of the proposed scheme is numerically evaluated on a simulation example.

Since most process systems have nonlinearities, it is necessary to consider the design of schemes to deal with such systems. In this paper, a new design scheme of PID controllers is proposed. This scheme is designed based on the IMC, which is a kind of the model driven controllers. The internal model consists of the design-oriented model and the full model. The full model is designed by using the neural network. The inner PID control system is first constructed for the augmented system, which is composed of the controlled subject and the internal model, and this control system is designed by the pole-assignment method. Furthermore, the outer PID controller is designed in order to remove the steady state error. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

When designing control systems, it is necessary to make mathematical models which describe the controlled objects in detail. However, since the controlled objects generally have nonlinearities and uncertainties, it is difficult to obtain exact models. As an artificial network model, the group method of data handling (GMDH) scheme has been proposed. The GMDH network has a feature that the nonlinear dynamics are clearly expressed as a mathematical model. Therefore, it is relatively easy to obtain the system properties in detail. A new design scheme is proposed, which adjusts all weighting coefficients based on backpropagation. The newly proposed scheme enables us to obtain a mathematical model with superior approximation ability for nonlinear systems. Furthermore, the generalized minimum variance control (GMVC) system is constructed by using the proposed GMDH network. A numerical example for a process system is demonstrated to illustrate the effectiveness of the proposed scheme.