This paper presents a data-driven robust control framework for wave energy converters. Unlike traditional model-based approaches that neglect uncertainties, the proposed method uses a data-driven model with polytopic uncertainty identified from experimental data of a Wavestar-type prototype. The control problem is cast as a convex semidefinite program with linear matrix inequalities (LMIs), enabling the synthesis of a robust H2 controller that maximises average energy capture. Numerical results demonstrate that the proposed controller either outperforms or matches passive, reactive, and H-infinity strategies in energy absorption and control efficiency.

Context: Placing Artificial Intelligence (AI) in control of cybersecurity compliance and auditing shifts its role from decision-support to direct execution of regulatory operational processes, where AI outputs may constitute compliance artefacts and audit evidence. This raises the problem of Meta-Compliance , in which not only the organisation but also the AI system must satisfy enforceable requirements. Yet existing frameworks provide no operational criteria for recognising AI as authoritative in such roles. Trustworthy AI principles define high-level Second-Layer requirements but remain non-binding, whereas First-Layer organisational requirements impose explicit justificatory and evidentiary duties. Objectives: This study investigates the minimal normative conditions under which AI systems can be recognised as authoritative in compliance and auditing, capable of producing evidence valid for assurance. Methods: Doctrinal analysis is conducted on binding “shall/must” provisions across PCI DSS, DORA, UK GDPR, NIS2, ISO/IEC 27001, and NIST SP 800-53. Provisions are normalised through the compliance–audit chain ( requirement → control → rule → evidence ) and mapped against Second-Layer AI governance requirements. The result is the Compliance–Audit Authority Benchmark (CAAB) , comprising six criteria: Traceability, Explainability, Evidence Integrity, Adaptability, Action Governance, and Reasoning. Results: Applying CAAB across AI model families and architectures shows that symbolic and knowledge-representation methods satisfy most criteria intrinsically, whilst neural, deep, and generative models do not unless supported by external governance mechanisms. This exposes a structural gap between First-Layer organisational requirements and Second-Layer AI requirements, clarifying that authority rests on evidentiary guarantees rather than statistical accuracy. Conclusion: The study formalises Meta-Compliance as the recursive structure in which both organisations and AI systems become subjects of assurance. CAAB defines the minimum conditions for recognising AI as authoritative, whilst the proposed Verifiable Reasoning Architecture (VRA) may offer a pathway toward AI systems anchored in secured evidence, reproducible inference, and symbolic governance, establishing audit-ready authority in high-risk contexts.

This work investigates the application of event-triggered control (ETC) in combination with the linear quadratic regulator (LQR) for discrete-time systems. A methodology is presented that uses an iterative approach to tune the LQR controller and reduces control effort by replacing periodic control with ETC. Lyapunov’s stability theory is applied to design triggering mechanisms and analyse their effects on control design. Using the inverted pendulum on a cart as a reference system, it is demonstrated that proper tuning of the LQR controller and the implementation of an event-based control mechanism can reduce control energy and actuator effort.

The objective of this article is to validate a dynamic output-feedback (DOF) control synthesis methodology to be implemented through digital signal processors in the scope of electrical microgrid applications. The DOF controller, originally designed in continuous-time, is developed aiming to comply with strict transient behavior requirements associated with electrical systems, by using an adaptation of the$\mathcal {D}$-stability concept to handle linear systems affected by time-varying parameters (linear parameter varying model). In order to allow a digital implementation of the control action, a discrete-time equivalent representation of the continuous-time DOF controller is obtained from the Taylor series expansion of the exponential terms associated with the discretization. Additional contributions of this article include: the use of a formulation based on differential linear matrix inequalities to ensure the stability of the hybrid system (composed of the analog plant and the digital controller), and the validation of the redesigned controller performance when applied to a real-time simulation of a three-phase inverter with a grid-supporting configuration emulated via hardware-in-the-loop device. The results demonstrate that the controlled system has achieved a regular operation for all the load cases investigated after the anomalies presented, even regulating a voltage drop of 20%. The proposed method was compared with a method in the literature, showing a decrease of 17.5% in the harmonic distortion of the currents holding similar voltage distortion.

In this work, a novel methodology is introduced that employs genetic algorithms to determine the optimal weighting matrices for a linear quadratic regulator controller. A method is presented to construct a multi-objective fitness function that allows one to give prioritisation to energy consumption or other performance metrics, such as rise time, settling time, and steady-state error. To validate the effectiveness of the proposed approach, we conducted simulation studies based on a model of an inverted pendulum on a cart system. The results show a reduction of up to 30.36% in the energy of the controller and a reduction of 20.27% in its maximum value when choosing to prioritise the energy expenditure of the controller over other performance metrics, without significantly compromising the convergence of the system states. The results encompass an effective way of optimising energy expenditure in non-linear controller designs.