The maritime industry plays a key role in the global supply chain. Advanced digital technologies bring significant economic benefits to ports and shipowners, but at the same time increase the risks of cyber threats and attacks. This article aims to provide guidelines and examples of good practice that will help in the effective implementation of cyber risk assessment, cyber resilience and cyber sustainability, which are the products of increasingly pronounced challenges. The interconnection of ports requires operators to achieve and maintain a baseline level of cybersecurity to ensure security across the entire port ecosystem. The development of new technologies in areas such as the Internet of Things, cloud computing, artificial intelligence, etc., contributes to the fact that monitoring and control systems in the maritime industry are becoming increasingly exposed to cyber threats and various forms of cyberattacks. The connection of vessels with systems on land in real time presents a necessary element in meeting the intended goals in the digital transformation of the maritime sector. This results in increasingly frequent work on specific software solutions within the maritime sector. With the adoption of new operational technologies (OT) and information technologies (IT), the desire for more efficient supply chains and operations of shipping in general has been realized, but at the same time the level of cybersecurity has decreased. The research results aim to encourage port operators and shippers to develop a series of good practices in order to develop an appropriate level of cybersecurity, resilience, and sustainability.

We present a novel method for the fuzzy control of a DC-DC boost converter based on a new approach to modelling the converter using Takagi–Sugeno (T-S) fuzzy identification. Two grades of identification result in a global model of a non-linear dynamical system and its finite impulse response model (FIRM) expression, which is therefore applicable in various model predictive control (MPC) standard methods with constraints. The successful simulation and experimental results shown in this study indicate the robustness and demonstrate stable operation of the DC-DC converter, even in the dynamic exchange of the discontinuous conduction mode (DCM) and the continuous conduction mode (CCM) with the preservation of a similar transient time. Although the study was primarily conducted on a hybrid simulation model of the DC-DC boost converter, the presented method is insensitive to the complexity of the physical process, as it suggests identified model-based control and emphasizes a new, general approach to pulse energy converter (PEC) controls. The statement is pursued with the subsequent application to the physical system of the converter. Furthermore, it underlines the method’s consideration of real-time processing and its final online simplicity.

This paper presents a new approach to compensate for the current imbalance of an interleaved DC–DC buck converter (IBC), in which the current sensors are not involved in the operation of the converter when it is connected to an invariable load. The current sensors are only used during the offline identification process that builds the universal fuzzy model of the converter’s steady states. Model building involves an upstream identification phase, followed by further dimensionality reduction of the model and error minimization. The method presented here discusses the mathematical complexity of the analytical modelling of hybrid systems and opposes it with a complexity-reduced identification by learning from data. An offline rendered model of the stable and steady states of the IBC is used as a mapping of the required inverter output current to n-fold asymmetric duty cycles, which are distributed among the IBC phases to allow arbitrarily accurate load sharing. The mapping is carried out in the mathematically normalized space of variables or in the physical sense RMS values, achieving the desired robustness in a noisy environment and stability. The final and canonical feedback control is built from the standard and optimized PI controller, which is compensated by the identified IBC model correction. The only measured feedback of the whole controller is the output voltage. Even when applied to the simulation model (physical MATLAB platform) of a two-phase IBC with the built-in system asymmetry, the presented methodology is also applicable to the n-phase IBC without loss of generality.

This paper introduces novel approaches to the predictive control of a DC-DC boost converter and a comparison of the controllers built that consider all of the current objectives and minimize the complexity of the online processing. The primary concern is given to the applicability of the inclined methods for systems that are physically small but considered physically fast processes. Although the performed methodologies are simulated and applied to a DC-DC boost converter, they can have broader applicability for different switched affine systems as a subgroup of the hybrid systems. The introduced methods present an alternative way of building the process model based on the fuzzy identification that contributes to the final objective: the applicability of the predictive methods for fast processes.