This article deals with periodic event-triggered control (PETC) of nonlinear systems, considering an equivalent quasi-linear parameter-varying (quasi-LPV) polytopic representation of the nonlinear plant and a gain-scheduled controller for stabilization. Although gain-scheduling approaches allow one to improve the results and extend the set of feasible solutions to the co-design problem, the event-based sampling induces the so-called asynchronous scheduling functions, which void the gain-scheduling advantages, leading to conservative results, especially in the PETC framework. The dominant approaches for dealing with this issue consider a bounding assumption on the mismatched scheduling functions, but do not guarantee that those bounds cannot be violated during the closed-loop operation. To properly manage the asynchronous phenomenon, we propose a novel PETC scheme. Based on the looped-functional approach and a nonquadratic Lyapunov function, we derive linear matrix inequality (LMI)-based conditions to co-design the event-triggering mechanism and the gain-scheduled controller. These conditions are incorporated into a multiobjective optimization problem to maximize the estimate of the region of attraction of the origin and minimize the number of transmissions of the PETC scheme. We prove that the closed-loop trajectories initiated in the estimated region of attraction converge toward the origin without violating the boundedness of the mismatched scheduling functions during operation. Two numerical examples are provided to illustrate the methodology.

Predictive maintenance is an approach based on technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and robust system architectures. It is widely used in industry and is increasingly adopted in the automotive sector, where its application is often limited to newer vehicles and trucks, excluding most older models. To fill this gap, we propose a solution for cars manufactured from 2010 onward with On-Board Diagnostics II (OBD-II). Our system has a low-cost onboard device, the Legacy Internet of Vehicles (IoV), which enables these vehicles to connect to the cloud server. The system’s initial goal is to send telemetry data to the cloud for analysis, identify anomalies, and predict potential future failures. The system alerts users through internet-connected devices when problems are detected, ensuring that notifications are sent directly to the user. By directing processing to the cloud, the system minimizes the complexity required onboard the vehicle, making it an accessible and scalable approach for a wider audience. To validate our solution, we conducted one of the application scenarios for this system. In this first stage, we performed tests with the system, identifying engine temperature alert problems that would immediately notify the user on the registered device, which in this case was the smartphone.

The use of Internet of Things (IoT) technologies has become more readily available with the advent of cyber-physical systems. This context motivates concerns about cybersecurity and the occurrence of malicious attacks in IoT networks. This paper investigates the problem of the automatic detection of cyberattacks in MQTT-based IoT networks by deploying artificial intelligence algorithms for processing traffic data and indicating whether an attack is occurring or not. Thus, this paper trains different artificial intelligence binary classifiers based on machine learning and compares their performance for malicious attack detection in cyber-physical systems with MQTT-based IoT networks. For training and testing the classifiers, we employ the MQTTset dataset which contains many labelled samples with both legitimate traffic and observations under attack occurrence. By analyzing data features and preprocessing, the algorithms achieved good performance in classifying network traffic, contributing to the security of cyber-physical systems.

Deploying Neural networks (NNs) in low-resource domains is challenging because of their high computing, memory, and power requirements. For this reason, NNs are often quantized before deployment, but such an approach degrades their accuracy. Thus, we propose the counterexample guided neural network quantization refinement (CEG4N) framework, which combines search-based quantization and equivalence checking. The former minimizes computational requirements, while the latter guarantees that the behavior of an NN does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small NNs. Our technique successfully quantizes the networks in the chosen evaluation set, while producing models with up to 163% better accuracy than state-of-the-art techniques.

Neural networks are a powerful class of non-linear functions. However, their black-box nature makes it difficult to explain their behaviour and certify their safety. Abstraction techniques address this challenge by transforming the neural network into a simpler, over-approximated function. Unfortunately, existing abstraction techniques are slack, which limits their applicability to small local regions of the input domain. In this paper, we propose Global Interval Neural Network Abstractions with Center-Exact Reconstruction (GINNACER). Our novel abstraction technique produces sound over-approximation bounds over the whole input domain while guaranteeing exact reconstructions for any given local input. Our experiments show that GINNACER is several orders of magnitude tighter than state-of-the-art global abstraction techniques, while being competitive with local ones.

This work aims at developing a fault detection system for photovoltaic systems (PVSs) based on the Fuzzy C-Means (FCM) clustering technique. A simulator is developed based on a mathematical model of the PVS by considering three groups of faults: shading, short circuit, and junction box to investigate the fault effects in photovoltaic systems. Several tests are carried out and indicate that the FCM is able to detect these faults and separate the data of a faulty PVS from that of a healthy one based on uncertain data obtained from different weather conditions.

This article presents a novel secure-control framework against sensor deception attacks. The vulnerability of cyber-physical systems with respect to sensor deceptive attacks makes that all sensor measurements are not reliable until the system security is assured by an attack detection module. Most of the active attack detection strategies require some time to assess the system security, while injecting a watermark signal to ease the detection. However, the injection of watermark signals deteriorates the performance and stability of the plant. The proposed control framework consists of a dual-rate control (DRC) that is able to stabilize the plant using: 1) a model predictive controller that operates at a slower sampling time; 2) a state-feedback predictor-based controller that operates in the nominal sampling time disregarding the use of the untrustworthy measurements until the attack detector is able to certify the security; and 3) a reconfiguration block (RB) for palliating the effect of the watermarking. Simulation results indicate the efficacy of the proposed DRC framework to defend the system from cyber-attacks and the ability of the RB to improve the closed-loop performance during the watermark injection.

We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential equations with inputs, evolving over a continuous state space. The synthesis precisely accounts for the effects of finite-precision arithmetic introduced by the controller. The approach uses counterexample-guided inductive synthesis: an inductive generalization phase produces a controller that is known to stabilize the model but that may not be safe for all initial conditions of the model. Safety is then verified via bounded model checking: if the verification step fails, a counterexample is provided to the inductive generalization, and the process further iterates until a safe controller is obtained. We demonstrate the practical value of this approach by automatically synthesizing safe controllers for physical plant models from the digital control literature.

Dealing with uncertain data requires effective methods to properly describe their real meaning in terms of a tradeoff between interpretability and generality on the process of knowledge formation based on data abstraction. This article proposes an online granulation process based on evolving ellipsoidal fuzzy information granules (EEFIG) and the principle of justifiable granularity (PJG) for data streams parameterization. The granulation process consists in the information granule development taking into consideration the data stream with a simplified optimal granularity allocation. In the sequel, an evolving Takagi-Sugeno fuzzy model based on the ellipsoidal granules is proposed for data reconstruction and one-step ahead prediction from past data numerical evidence. Experimental studies concerning clustering, data granulation, and time-series forecasting are performed to illustrate the effectiveness of the proposed method.

We present an automated counterexample reproducibility tool based on MATLAB, called DSValidator, with the goal of reproducing counterexamples that refute specific properties related to digital systems. We exploit counterexamples generated by the Digital System Verifier (DSVerifier), which is a model checking tool based on satisfiability modulo theories for digital systems. DSValidator reproduces the execution of a digital system, relating its input with the counterexample, in order to establish trust in a verification result. We show that DSValidator can validate a set of intricate counterexamples for digital controllers used in a real quadrotor attitude system within seconds and also expose incorrect verification results in DSVerifier. The resulting toolbox leverages the potential of combining different verification tools for validating digital systems via an exchangeable counterexample format.

During the last decades, model checking techniques have been applied to improve overall system reliability, in unmanned aerial vehicle (UAV) approaches. Nonetheless, there is little effort focused on applying those methods to the control-system domain, especially when it comes to the investigation of low-level implementation errors, which are related to digital controllers and hardware compatibility. The present study addresses the mentioned problems and proposes the application of a bounded model checking tool, named as Digital System Verifier (DSVerifier), to the verification of digital-system implementation issues, in order to investigate problems that emerge in digital controllers designed for UAV attitude systems. A verification methodology to search for implementation errors related to finite word-length effects (e.g., arithmetic overflows and limit cycles), in UAV attitude controllers, is presented, along with its evaluation, which aims to ensure correct-by-design systems. Experimental results show that low-level failures in UAV attitude control software used in aerial surveillance are identified by DSVerifier, which can also be used for developing sound and correct implementations, through its integration into development processes. Finally, given that the proposed approach handles C code and takes into account hardware specifications, it is suitable for verifying final controller implementations, which is a more practical scenario.

Modern control is implemented with digital microcontrollers, embedded within a dynamical plant that represents physical components. We present a new algorithm based on counterexample guided inductive synthesis that automates the design of digital controllers that are correct by construction. The synthesis result is sound with respect to the complete range of approximations, including time discretization, quantization effects, and finite-precision arithmetic and its rounding errors. We have implemented our new algorithm in a tool called DSSynth, and are able to automatically generate stable controllers for a set of intricate plant models taken from the literature within minutes.

A MATLAB toolbox is presented, with the goal of checking occurrences of design errors typically found in fixed-point digital systems, considering finite word-length effects. In particular, the present toolbox works as a front-end to a recently introduced verification tool, known as Digital-System Verifier (DSVerifier), and checks overflow, limit cycle, quantization, stability, and minimum phase errors in digital systems represented by transfer-function and state-space equations. It provides a command-line version with simplified access to specific functionality and a graphical-user interface, which was developed as a MATLAB application. The resulting toolbox enables application of verification to real-world systems by control engineers.

We present an automated MATLAB Toolbox, named DSSynth (Digital-System Synthesizer), to synthesize sound digital controllers for physical plants that are represented as linear timeinvariant systems with single input and output. In particular, DSSynth synthesizes digital controllers that are sound w.r.t. stability and safety specifications. DSSynth considers the complete range of approximations, including time discretization, quantization effects and finite-precision arithmetic (and its rounding errors). We demonstrate the practical value of this toolbox by automatically synthesizing stable and safe controllers for intricate physical plant models from the digital control literature. The resulting toolbox enables the application of program synthesis to real-world control engineering problems. A demonstration can be found at https://youtu.be_hLQslRcee8.

A verification methodology is described and evaluated to formally determine uncertain linear systems stability in digital controllers with considerations to the implementation aspects. In particular, this methodology is combined with the digital-system verifier (DSVerifier), which is a verification tool that employs Bounded Model Checking based on Satisfiability Modulo Theories to check the stability of digital control systems with uncertainty. DSVerifier determines the control system stability, considering all the plant interval variation set, together with the Finite Word-length (FWL) effects in the digital controller implementation; DSVerifier checks the robust non-fragile stability of a given closed-loop system. The proposed methodology and respective tool are evaluated considering non-fragile control examples from literature. Experimental results show that the approach used in this study is able to foresee fragility problems in robust controllers, which could be overlooked by other existing approaches due to underestimating of FWL effects.

Digital controllers have several advantages with respect to their flexibility and design's simplicity. However, they are subject to problems that are not faced by analog controllers. In particular, these problems are related to the finite word-length implementation that might lead to overflows, limit cycles, and time constraints in fixed-point or floating-point processors. This paper proposes a new method to detect design's errors in fixed-point digital controllers using a state-of-the art bounded model checker based on satisfiability modulo theories. The experiments with a commercial plant demonstrate that the proposed method can be effective in finding errors in digital controllers than other existing approaches, which are based on traditional simulations tools. The verification results are conclusive in 93.5% of the benchmarks, determining the absence or occurrence of errors.