The issue of a low minority class identification rate caused by data imbalance in anomaly detection tasks is addressed by the proposal of a GAN-SR-based intrusion detection model for industrial control systems. First, to correct the imbalance of minority classes in the dataset, a generative adversarial network (GAN) processes the dataset to reconstruct new minority class training samples accordingly. Second, high-dimensional feature extraction is completed using stacked asymmetric depth self-encoder to address the issues of low reconstruction error and lengthy training times. After that, a random forest (RF) decision tree is built, and intrusion detection is carried out using the features that SNDAE retrieved. According to experimental validation on the UNSW-NB15, SWaT and Gas Pipeline datasets, the GAN-SR model outperforms SNDAE-SVM and SNDAE-KNN in terms of detection performance and stability.

In automotive control systems, the potential risks of software defects have been increasing due to growing software complexity driven by advances in electric-electronic control. Some kind of defects such as race conditions can rarely be detected by testing or simulations because these defects manifest themselves only in some rare executions. Model checking, which employs an exhaustive state-space exploration, is effective for detecting such defects. This paper reports our approach to applying model checking techniques to real-world automotive control programs. It is impossible to directly model check such programs because of their large size and high complexity; thus, it is necessary to derive, from the program under verification, a model that is amenable to model checking. Our approach uses the SPIN model checker as well as in-house tools that facilitate this process. One of the key features implemented in these tools is boundary-adjustable program slicing, which allows the user to specify and extract part of the source code that is relevant to the verification problem of interest. The conversion from extracted code into Promela, SPIN's input language, is performed using one of the tools in a semi-automatic manner. This approach has been used for several years in practice and found to be useful even when the code size of the software exceeds 400 KLOC.

We study wireless networked control systems (WNCSs), where controllers (CLs), controlled objects (COs), and other devices are connected through wireless networks. In WNCSs, COs can become unstable due to bursty packet losses and random delays on wireless networks. To reduce these network-induced effects, we utilize the packetized predictive control (PPC) method, where future control vectors to compensate bursty packet losses are generated in the receiving horizon manner, and they are packed into packets and transferred to a CO unit. In this paper, we extend the PPC method so as to compensate random delays as well as bursty packet losses. In the extended PPC method, generating many control vectors improves the robustness against both problems while it increases traffic on wireless networks. Therefore, we consider control vector selection to improve the robustness effectively under the constraint of single packet transmission. We first reconsider the input strategy of control vectors received by COs and propose a control vector selection scheme suitable for the strategy. In our selection scheme, control vectors are selected based on the estimated average and variance of round-trip delays. Moreover, we solve the problem that the CL may misconceive the CO's state due to insufficient information for state estimation. Simulation results show that our selection scheme achieves the higher robustness against both bursty packet losses and delays in terms of the 2-norm of the CO's state.

Since cyber attacks such as cyberterrorism against Industrial Control Systems (ICSs) and cyber espionage against companies managing them have increased, the techniques to detect anomalies in early stages are required. To achieve the purpose, several studies have developed anomaly detection methods for ICSs. In particular, some techniques using packet flow regularity in industrial control networks have achieved high-accuracy detection of attacks disrupting the regularity, i.e. normal behaviour, of ICSs. However, these methods cannot identify scanning attacks employed in cyber espionage because the probing packets assimilate into a number of normal ones. For example, the malware called Havex is customised to clandestinely acquire information from targeting ICSs using general request packets. The techniques to detect such scanning attacks using widespread packets await further investigation. Therefore, the goal of this study was to examine high performance methods to identify anomalies even if elaborate packets to avoid alert systems were employed for attacks against industrial control networks. In this paper, a novel detection model for anomalous packets concealing behind normal traffic in industrial control networks was proposed. For the proposal of the sophisticated detection method, we took particular note of packet flow regularity and employed the Markov-chain model to detect anomalies. Moreover, we regarded not only original packets but similar ones to them as normal packets to reduce false alerts because it was indicated that an anomaly detection model using the Markov-chain suffers from the ample false positives affected by a number of normal, irregular packets, namely noise. To calculate the similarity between packets based on the packet flow regularity, a vector representation tool called word2vec was employed. Whilst word2vec is utilised for the culculation of word similarity in natural language processing tasks, we applied the technique to packets in ICSs to calculate packet similarity. As a result, the Markov-chain with word2vec model identified scanning packets assimulating into normal packets in higher performance than the conventional Markov-chain model. In conclusion, employing both packet flow regularity and packet similarity in industrial control networks contributes to improving the performance of anomaly detection in ICSs.

Event-triggered control is a method that the control input is updated only when a certain triggering condition is satisfied. In networked control systems, quantization errors via A/D conversion should be considered. In this paper, a new method for quantized event-triggered control with switching triggering conditions is proposed. For a discrete-time linear system, we consider the problem of finding a state-feedback controller such that the closed-loop system is uniformly ultimately bounded in a certain ellipsoid. This problem is reduced to an LMI (Linear Matrix Inequality) optimization problem. The volume of the ellipsoid may be adjusted. The effectiveness of the proposed method is presented by a numerical example.

As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

In this paper, we consider a networked control system where bounded network delays and packet dropouts exist in the network. The physical plant is abstracted by a transition system whose states are quantized states of the plant measured by a sensor, and a control specification for the abstracted plant is given by a transition system when no network disturbance occurs. Then, we design a prediction-based controller that determines a control input by predicting a set of all feasible abstracted states at time when the actuator receives the delayed input. It is proved that the prediction-based controller suppresses effects of network delays and packet dropouts and that the controlled plant still achieves the specification in spite of the existence of network delays and packet dropouts.

Event-triggered and self-triggered control methods are an important control strategy in networked control systems. Event-triggered control is a method that the measured signal is sent to the controller (i.e., the control input is recomputed) only when a certain condition is satisfied. Self-triggered control is a method that the control input and the (non-uniform) sampling interval are computed simultaneously. In this paper, we propose new methods of event-triggered control and self-triggered control from the viewpoint of online optimization (i.e., model predictive control). In self-triggered control, the control input and the sampling interval are obtained by solving a pair of a quadratic programming (QP) problem and a mixed integer linear programming (MILP) problem. In event-triggered control, whether the control input is updated or not is determined by solving two QP problems. The effectiveness of the proposed methods is presented by numerical examples.

Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems, and is studied in the field of networked control systems. In this paper, a new approach for self-triggered control is proposed based on the model predictive control (MPC) method. First, self-triggered MPC with delay compensation in which the delay-compensation input is introduced is newly formulated. Next, in order to efficiently solve this MPC problem, the optimal control problem with horizon one is formulated, and an approximate solution method is derived. Finally, the effectiveness of the proposed approach is shown by a numerical example.

This paper provides an overview on the recent research on networked control with an emphasis on the tight relation between the two fields of control and communication. In particular, we present several results focusing on data rate constraints in networked control systems, which can be modeled as quantization of control-related signals. The motivation is to reduce the amount of data rate as much as possible in obtaining control objectives such as stabilization and control performance under certain measures. We also discuss some approaches towards control problems based on techniques from signal processing and information theory.

In this paper, we study the decentralized coverage control problem for an environment using a group of autonomous mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated into the controller design. Furthermore, a collision avoidance component is added in the kinematic controller in order to guarantee a collision free coverage of the area. The convergence of the network to the optimal sensing configuration is proven with a Lyapunov-type analysis. Numerical simulations are provided approving the effectiveness of the proposed method through several experimental scenarios.

We review practical case studies of a developing method of highly reliable real-time embedded control systems using a CPU model-based hardware/software co-simulation. We take an approach that enables us to fully simulate a virtual mechanical control system including a mechatronics plant, microcontroller hardware, and object code level software. This full virtual system approach simulates control system behavior, especially that of the microcontroller hardware and software. It enables design space exploration of microarchitecture, control design validation, robustness evaluation of the system, software optimization before components design. It also avoids potential problems. The advantage of this work is that it comprises all the components in a typical control system, enabling the designers to analyze effects from different domains, for example mechanical analysis of behavior due to differences in controller microarchitecture. To further improve system design, evaluation and analysis, we implemented an integrated behavior analyzer in the development environment. This analyzer can graphically display the processor behavior during the simulation without affecting simulation results such as task level CPU load, interrupt statistics, and the software variable transition chart. It also provides useful information on the system behavior. This virtual system analysis does not require software modification, does not change the control timing, and does not require any processing power from the target microcontroller. Therefore this method is suitable for real-time embedded control system design, in particular automotive control system design that requires a high level of reliability, robustness, quality, and safety. In this study, a Renesas SH-2A microcontroller model was developed on a CoMETTMplatform from VaST Systems Technology. An electronic throttle control (ETC) system and an engine control system were chosen to prove this concept. The electronic throttle body (ETB) model on the Saber® simulator from Synopsys® and the engine model on MATLAB®/Simulink® simulator from MathWorks can be simulated with the SH-2A model using a newly developed co-simulation interface between MATLAB®/Simulink® and CoMETTM. Though the SH-2A chip was being developed as the project was being executed, we were able to complete the OSEK OS development, control software design, and verification of the entire system using the virtual environment. After releasing a working sample chip in a later stage of the project, we found that such software could run on both actual ETC system and engine control system without critical problem. This demonstrates that our models and simulation environment are sufficiently credible and trustworthy.

This paper deals with new fuzzy controller for handling systems having large dead time and nonlinearities, via approximations of large rule fuzzy logic controller by simplest fuzzy controller (4 rules). The error between large rule fuzzy controller and simplest fuzzy controller are compensated by proposed compensating factors. These compensating factors are modified to handle large dead time and nonlinear systems. Features of proposed approximations are discussed. The concept of variation of nonlinearity factor of fuzzy controller is also discussed. Various processes from different literatures are utilized to demonstrate the proposed methodology. After doing many simulations it has been found that with proper tuning, overall system handles large dead time and nonlinearity which may be difficult by conventional methods. The processes are also simulated for load disturbances and change of operating point (set point) and it has been found that proposed scheme is robust for long dead times.

Computers are increasingly used for implementing control algorithms in safety-critical embedded applications, such as engine control, braking control and flight surface control. Consequently, computer errors can have severe impact on the safety of such systems. Addressing the coupling of control performance with computer related errors, this paper develops a methodology for analyzing the impacts data errors have on control system dependability. The impact of a data error is measured as the resulting control error. We use maximum bounds on this measure as the criterion for control system failure (i.e., if the control error exceeds a certain threshold, the system has failed). In this paper we a) develop suitable models of computer faults for analysis of control level effects and related analysis methods, and b) apply traditional control theory analysis methods for understanding the impacts of data errors on system dependability. An automobile slip-control brake-system is used as an example showing the viability of our approach.

We propose middleware which works on widely-used commercial off-the-shelf platforms (UDP/IP, FastEthernet, and Windows NT or commercial real-time kernels) to realize real-time distributed services for plant monitoring and control systems. It is not suitable to use TCP/IP for the systems because of its unpredictable re-transmission, while, as well known, UDP/IP does not guarantee certain arrivals of packets and it is also not acceptable for the systems. With UDP/IP, packets are lost mainly because of collisions in a network and buffer overflows. To avoid these packet losses, the middleware controls scheduling of all the packets transmitted between the nodes in a distributed system and prevents excessive collisions and buffer overflows. The middleware provides a necessary set of functions for plant monitoring and control applications. The middleware on each node in a distributed system consists of library functions and run-time modules. An application program on the node is required to use these library functions according to the rules the middleware provides. In this way the middleware can manage all the traffic among the nodes in the system. Receiving requests from the application via library functions, the run-time module of the middleware schedules transmission of messages to other nodes, avoiding unexpected delivery delays or buffer overflows. The module also guarantees application-to-application quality of service (QoS), such as transmission period and delivery deadline, required by the applications. This is achieved by assigning the resources not shared by other services to each distributed service and scheduling these resources so as not to violate the assignment. Here, resources include maximum numbers of packets which a node can receive or send in a specific period (20 msec, for example). We show implementation of the middleware to make it clear how to guarantee application-to-application QoS with some application examples.

With the evolution of semiconductor technology, automotive electronics has made tremendous progress. The aim of automotive electronics is to improve the basic automotive functions of vehicles (running, turning, and stopping) from the standpoint of environmental protection, energy conservation, and transportation efficiency. This paper introduces the process of automotive electronics with an emphasis on major control systems such as engines and brakes. The role of ASICs in automotive control systems is also presented with actual examples of ASICs that are used in these systems.

It has been recognized that there exist some disparities between properties of continuous control systems and those of discrete ones which are obtained from their continuous counterparts by use of a sampler and zero order hold. This still remains true even if the sampling rate becomes fast enough and sometimes causes unfavorable effects in control systems design. To reconcile with this conflict, use of delta operator has been proposed in place of z-operator recently. This note formulates a delta domain Lyapunov matrix equation and shows that the equation actually mediates the discrete Lyapunov equation and its continuous counterpart.