This study proposes a data-driven approach to path-following for vehicles. Data-driven control enables control parameters to be designed directly from time-series data and is effective in adapting the control law to changes in road conditions and load characteristics. However, conventional data-driven methods such as VRFT and FRIT assume linear system dynamics. Therefore, when applied to vehicle path-following problems where the reference path is defined by curvature, they typically require preprocessing steps such as linear approximation or exact linearisation. This study proposes a method of achieving data-driven path-following control without such preprocessing, utilising the velocity variation of the deviation between the reference path and the position of the vehicle as a fictitious exogenous signal within the FRIT framework. The validity of the proposed method is demonstrated through numerical simulations.

Several relative entropy-based methods have been studied in cyber-attack detection of control systems. Most existing studies set the threshold values of relative entropy by trial and error such that their error probabilities become small. Meanwhile, the relationship between threshold values and error probabilities in likelihood ratio tests is clarified by Information theory. Information theory also clarifies the relationship between relative entropy and likelihood ratio test. To theoretically set the threshold, the authors have investigated the relationship between relative entropy and the likelihood ratio test using experimental data from DoS attacks and man-in-the-middle attacks on control communication (Modbus TCP). This paper investigates the relationship between threshold values and error probabilities in actual experiments. Error probabilities are classified as false positive rates and false negative rates. Neyman-Pearson lemma shows how to construct a detector that considers the trade-off between false positive and false negative rates. Stein’s lemma shows how to give optimal threshold values. We build a detector from the two lemmas that consider the trade-off with probability models of delay time between Response and ACK of Modbus TCP. We conduct experiments and discuss optimal threshold-setting methods in the sense that the false positive rates cannot be further reduced when false positive rates are fixed.

As the risk of cyber attacks on critical infrastructure increases, railway systems, one of the significant modes of transport, also have the potential for cyber attack threats due to the increasing digitization and computerization. In the event of successful cyber attacks on railway systems, there is a high probability of sustaining significant economic losses and potential human casualties. Thus, it is imperative to consider the security of railway systems and maintain them in a secure state to mitigate the damages of cyber attacks. This paper proposes methods to maintain the safety of railway systems, assuming that the sensor incident caused the distance control to malfunction. Firstly, we enable the model train to perform distance control to reproduce the defects in distance control caused by sensor incidents. Secondly, we use Boolean networks to create a digital twin model created by integrating a model of the railway control program and a model of the rail system. We construct the state transition diagram from the entire digital twin model and identify a vulnerable state, in which the system transition is deadlocked. In the experimental system, this state appears as a train collision due to cyber attacks on the sensors at a merge point. Finally, based on the vulnerable state analysis, we discuss a robust state in which no accident occurs, even if cyber attacks on sensors occur.

Autonomous driving demands sophisticated control systems that optimize safety, performance, passenger comfort, and fuel efficiency. This study proposes a steering control system that integrates the Deep Deterministic Policy Gradient (DDPG) for speed planning with a novel feedback mechanism based on Subjective Vertical Conflict (SVC) in the reward function. Using simulations in MATLAB and Simulink, we evaluate the system's performance across various thresholds of SVC, examining its impact on ride comfort, fuel efficiency, and vehicle behavior during lane changes. Results reveal a trade-off relationship between ride comfort and fuel efficiency, with lower SVC thresholds generally improving comfort but potentially increasing steering input. Additionally, excessively low SVC thresholds degrade target-reaching performance and lengthen lane change distances, highlighting the need for careful parameter tuning. Overall, our findings demonstrate the potential of reinforcement learning-based steering control systems to optimize multiple evaluation criteria simultaneously while emphasizing the importance of balancing trade-offs in autonomous driving scenarios.

Cyber attacks on control system communication are increasing. In information systems, a lot of security counter-measure focusing on the distribution of communication packets has been studied so far. Such attack detection methods evaluate normal and abnormal packets based on the likelihood and the relative entropy. Whether the methods for information systems are also effective for control systems is another question. Then, this paper conducts attack detection experiments based on the likelihood and the relative entropy of DoS and spoofing attacks on Modbus TCP communication used in industrial control systems.

This paper proposes a position estimation method for a platoon by distributed cooperative observer set on multiple vehicles and a traffic control system installed on the road. Each observer estimates its own vehicle and the front and rear vehicles, and can exchange the estimated values between the front and rear vehicles by V2V communication or V2X communication. The observer set on the traffic control system can only observe and estimate the absolute position of some target vehicle in the sensing range. The next vehicle's absolute position can be estimated by interpolating the observed value by the target vehicle position and inter-vehicular distance. Repeating this process, all the vehicles in the platoon can estimate its own position.

One of the typical autonomous driving systems is a human-machine cooperative system that intervenes in the driver operation. The autonomous driving needs to make consideration of the driver individuality in addition to safety. This paper considers a human-machine cooperative system balancing safety with the driver individuality using the Human-In-The-Loop System (HITLS) for rear-wheel steering control. This paper assumes that it is safe for HITLS to follow the target side-slip angle and target angular velocity without conflicts between the controller and driver operations. We propose HITLS using the primal-dual algorithm and the internal model control (IMC) type I-PD controller. In HITLS, the signal expander delimits the human-selectable operating range and the controller cooperates stably the human operation and automated control in that range. The primal-dual algorithm realizes the driver and the signal expander. Our outcomes are the making of the rear-wheel steering system which converges to the target value while reflecting the driver individuality.

This paper considers a verification problem of the whitelist function applicable to the Programmable Logic Controller (PLC). The PLC of the industrial control system is an important controller to control sensors and actuators and requires security functions because PLCs are becoming targets for cyber-attacks such as malware and zero-day attacks. One of the PLC security functions is a whitelisting system that registers normal operations as a safety list and detects the operations not registered in the list as abnormal operations. The detection performance of the whitelist depends on how accurately the normal operation of PLC is modeled via Petri net. Therefore, it is necessary to verify the consistency of the normal operation and whitelist of the PLC. Verification of the consistency allows us to evaluate the detection range and to suppress false detection. The previous work of the current authors demonstrates that the Petri net model allows us to generate the whitelist from the control program of PLC. The whitelist generation is composed of two processes: The first is to convert a control program to a Petri net and the second is to convert a Petri net model to a whitelist. Thus, this paper proposes two whitelist verification methods. The first is a model verification method to verify the Petri net model using reachability of the Petri net. The second is an exhaustive test method to verify the whitelist operation. Furthermore, it is expected that the proposed methods are applicable for evaluation and verification of detection performance when the whitelist is compressed to reduce the load on the PLC.

Model-based development in the automotive industry supposes that multiple CAE (Computer-Aided Engineering) tools construct a physical model. Model verification is difficult in multiple CAE environment. In this research, we propose “Energy Balance Based Verification” (EBBV) to overcome this difficulty. EBBV checks whether a physical phenomenon represented by the model realizes the energy conservation law. In this paper, we create two diagrams that support EBBV. Two diagrams are hierarchical diagram and energy flow diagram. To automate the EBBV procedure with two diagrams, we develop three tools: Hierarchical tool, EF tool, Evaluation tool. Furthermore, we make an energy-based modeling guideline suitable for EBBV. Through a numerical experiment, we confirm that three tools can detect bugs in the created model according to the modeling guideline. In this numerical experiment, we use a mild hybrid electric vehicle model constructed by MATLAB / Simlink®, MapleSim®, and IPG-CarMaker®.

These days, cyber attacks against cyber-physical systems (CPS) such as power plants are constantly growing, and therefore the countermeasures are essential. Whitelisting cyber attack detection, that detects anomalous activity as deviation from normal network communication, is especially attracting attention as one of the countermeasures because the traffic patterns are predictable in CPS. However, attackers can execute illegal operations within the normal network communication defined as the whitelisting detection rules because such operations tend to cause unstable control depending on operational states. In this work, we propose a new whitelisting cyber attack detection method that estimates the operational state of CPS and monitors the CPS network with the whitelisting detection rules according to the estimated state. The proposed method can define the more limited normal network communication to generate the whitelisting detection rules in each operational state. We experimented and evaluated the method on a plant simulator, and confirmed the effectiveness of the method: state estimation with support vector machine and attack detection with divided whitelisting detection rules.

This paper considers a whitelisting system for programmable logic controllers (PLCs). In control systems, controllers are final fortresses to continues the operation of field devices (actuators/sensors), but they are fragile with respect to malware and zero-day attacks. One of the countermeasures applicable for controllers is a whitelisting system which registers normal operations of controller behavior in a “whitelist” to detect abnormal operations via a whitelist. The previous research of the current author proposed a PLC whitelisting system with a control via a ladder diagram (LD). LD representations have a wide applicability because LDs can be implemented for all PLCs and security functions without hardware/firmware updates. However, the current status requires that all instances are manually entered in the whitelist. In this talk, we show how the setting up of the can be automatized whitelist from the PLC behavior. This paper introduces an auto-generation approach for the whitelist using sequential function chart (SFC) instead of the LD. SFC and LD are compatible representations for the PLC. Using Petri Net modeling, this paper proposes how to generate the whitelist from the SFC and how to detect abnormal operations via the whitelist. We call the SFC-based approach the model-based whitelist, the Petri Net based approach the model-based detection. Further, this paper carries out an experimental validation of the algorithms using an OpenPLC based testbed system.

The authors have proposed the Fallback Control System (FCS) as a countermeasure after cyber-attacks happen in Industrial Control Systems (ICSs). For increased robustness against cyber-attacks, introducing multiple countermeasures is desirable. Then, an appropriate collaboration is essential. This paper introduces two FCSs in ICS: field network signal is driven FCS and analog signal driven FCS. This paper also implements a collaborative FCS by a collaboration function of the two FCSs. The collaboration function is that the analog signal driven FCS estimates the state of the other FCS. The collaborative FCS decides the countermeasure based on the result of the estimation after cyber-attacks happen. Finally, we show practical experiment results to analyze the effectiveness of the proposed method.

This paper considers driving state management with a tampered control device. We propose the broadcasting algorithm of the control terminal and the collecting algorithm of the state monitoring device in which a tampered control device exists in the networked control system using error correction code. In addition, we propose the dummy agent method and the no-dummy agent method by sharing the parity check bits between the control terminal and the state monitoring device. By numerical experiments, we verify that it is effective for tamper detection and correction.

This paper aims to propose a fallback system isolated from man-in-the-middle attack (MITM) for networked control systems. In this paper, fallback is an incident response of the control system in which facilities are preferentially protected and a minimal system operates during MITM. Since the communication contents of the networked control systems can be tampered with by MITM, the proposed system named a "fallback system" is installed in a control target side, and the fallback system detects MITM without the communication contents. This system detects MITM based on the bilinear observer and switched Lyapunov function. When MITM is detected, normal operation is switched to fallback operation automatically. As a first step of this study, a selector and a fallback unit of the fallback system are developed. A practical experiment simulating a simple defective discriminator is shown.

This paper proposes a smartphone centered system where home appliances cooperate with each other. This system realizes convenience of a consumer as low cost and high secure environment by Near Field Communication (NFC) and a Cloud Server consists of Social Networking Service (SNS) and a Virtual Server (VS). Moreover, Verbal Communication (VC) through the smartphone is added for usability. A cooking application is presented in order to show a detail and validity of the proposed system.

This paper describes a method of generating low-distortion sinusoidal waves using an arbitrary waveform generator (AWG), and experimental results of using such a generator for ADC dynamic performance testing. With this proposed method, 3rdorder harmonics of the generated signal are suppressed simply by changing the AWG program (or waveform memory contents)-AWG nonlinearity identification is not required-and spurious components, generated far from the signal band, are relatively easy to remove using an analog filter; our theoretical analyses, simulations, and experiments showed that a simple passive LC analog filter (with relaxed requirements compared to the one for direct HD3 removal) is sufficient. Our ADC testing results-using signals generated by an AWG with the proposed algorithm, and a simple passive LC LPF-show measurement errors (caused by 3rdorder harmonics generated by AWG DAC nonlinearity) half that of previous algorithms.

This paper proposes a new static simultaneous optimization of dispatching and conflict-free routing for unidirectional Overhead Hoist Transport (OHT) systems. Our objective is to minimize the entire transportation time where the given transportation tasks are finished. A hybrid system modeling is applied for OHT system that is constituted by the OHT vehicles at the lower level and governed by the upper level scheduler. In the proposed modeling, it is clarified that the simultaneous optimization problems are recast as a binary integer linear problem by using model predictive control. This paper presents a numerical example and discusses limitation of proposed modeling.

This paper considers a task scheduling problem for multicore CPUs and proposes a multistep scheduling algorithm. The existing scheduling algorithms formulated as 0-1 integer linear programming can consider optimality of a task scheduling. However, the existing scheduling algorithms cannot address complicated relations among tasks or cannot consider communication costs among processors. Then, first purpose is to propose a new scheduling algorithm with communication costs formulated as 0-1 integer linear programming. On the other hand, 0-1 integer linear programming is NP-complete and it takes long time to calculate scheduling result. Then, the second purpose is to decrease scheduling time. A solution decreasing scheduling time is a graph clustering which decomposes a large task graph into smaller sub-task graph (cluster). Also, it is important for parallel and distributed processing to find task parallelism in a task graph. Then, this paper proposes a clustering algorithm based on SCAN which is an algorithm for finding clusters in a network. The proposed algorithm can find task parallelism in a task graph. In numerical examples, the multistep scheduling algorithm is superior to the existing scheduling algorithm in terms of calculation time.