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The robust stabilization problem with respect to both dynamic and parametric uncertainty for linear deterministic systems is analyzed in the present article. The robust design methods consider either the dynamic modelling in frequency domain of the uncertainty or, its parametric representation in the state space realisation. Suitable analysis approaches for parametric uncertainty modelling are provided by Kharitonov and Edge-type theorems. Under some specific assumptions, these methods allow to determine the whole admissible domain of the uncertain parameters for which a system is stable. It shall describe a method that combines the advantages of the control techniques with ones given by the polytopic representation of parametric uncertainty. A modified ∞ loop-shaping approach allowing to solve control problems in which robust stabilization, sensitivity reduction, and model following design objectives are formulated is presented and it allows to handle tracking design specifications. The modified loop-shaping procedure allows to design a controller that provides a) robust stability with respect to the normalized left coprime factorization (NLCF); b) reduced sensitivity with respect to output disturbance on a specified range of frequencies, and c) tracking of the output of a given ideal model. The article is finished with a case study in which a two degrees-of-freedom control system with respect to the pitch angle for the longitudinal dynamics of a UAV (ARCHER V1.7) is designed using the modified ∞ loop-shaping procedure.

This paper concerns a class of linear-state optimal control problems and noncooperative differential games. Deterministic and stochastic systems are considered, as well as finite- and infinite-horizon problems. We give conditions under which these systems have degenerate feedback optimal controls so that the optimal control actions a ( t , x ) ≡ a ( t ) are independent of the state variable x . As a consequence, open-loop and feedback (or Markov) optimal controls coincide, the value (or optimal objective) function is linear in the state x , and the certainty equivalence principle is satisfied.

The utilization of parafoil systems in both military and civilian domains exhibits a high degree of application potential, owing to their remarkable load-carrying capacity, consistent flight dynamics, and extended flight endurance. The performance and safety of powered parafoils during the flight are directly contingent upon the efficacy of the control system employed. For powered parafoils, the direction is controlled by steering ropes connecting to the edges of the parafoil canopy. And a propeller attached to the back of the payload controls the flight height. However, strong couplings exist between two control channels, which makes controlling powered parafoil systems challenging, especially under wind disturbances. This paper aims to address these challenges by proposing a three-dimensional (3D) path-following control method for powered parafoils. To this end, the lateral and altitude path-following controllers were designed to solve this problem based on linear active disturbance rejection control (LADRC) with disturbance rejection and decoupling features. Furthermore, the adaptive parameters of these two controllers were obtained through the implementation of deep deterministic policy gradient (DDPG). The efficacy of the proposed DDPG-LADRC approach was then evaluated through simulations of 3D path tracking, including both straight and circular paths, while also taking into account wind disturbances to assess its anti-disturbance capability. The results of these simulations indicate that the proposed method effectively realizes the 3D path following control of powered parafoils.

The transition to sustainable power infrastructure necessitates integrating various renewable energy sources efficiently. Our study introduces the deterministic balanced method (DBM) for optimizing hybrid energy systems, with a particular focus on using hydrogen for energy balance. The DBM translates the sizing optimization problem into a deterministic one, significantly reducing the number of iterations compared to state-of-the-art methods. Comparative analysis with HOMER Pro demonstrates a strong alignment of results, with deviations limited to a 5% margin, confirming the precision of our method in sizing determinations. Utilizing solar and wind data, our research includes a case study of Cairo International Airport, applying the DBM to actual energy demands.

The deterministic errors of an accelerometer comprise the prevailing i) bias, ii) scale factor, and iii) non-orthogonality. Together, these errors result in a nonlinear measurement model, which is conventionally solved via an iterative nonlinear least-squares method. In contrast to the conventional approach, we propose a novel method to transform the above nonlinear model into a system of linear equations, resulting in an exact, closed-form solution of the deterministic errors. The developed mathematical formulations are first verified in a simulation setting, followed by a real-time implementation using Robot Operating System for small micro-electromechanical inertial measurement units.

Increasing energy demand and the detrimental environmental impacts of fossil fuels have led to the development of renewable energy sources. Rapid demand growth for wood pellets over the last decade has established wood pellets as a potential renewable energy source in a globally competitive energy market. Integrated decision making including all stakeholders in the wood pellet supply chain (WPSC) is essential for a smooth transition to commercially viable wood pellet production. In this aspect, this study aims to suggest a decision support system for optimizing biomass-based wood pellet production supply chain network design (WPP-SCND). The WPP-SCND decision system minimizes the total supply chain (SC) cost of the system while also reducing carbon emissions associated with wood pellet SC activities. All objective parameters, including biomass availability at the supply terminals, market demand, and biomass production, are considered fuzzy to account for epistemic uncertainty. A fuzzy flexible robust possibilistic programming (fuzzy-FRPP) technique is developed for solving the suggested uncertain WPP-SCND model. The case findings show that the imprecise nature of the parameters has a significant impact on the strategic and tactical decisions in the wood pellet SC. By investing almost 10% of the total cost, robust decisions within the wood pellet SC can be obtained. It is established that the fuzzy-FRPP technique successfully provides robust decisions and achieves a balance between transportation costs, emissions costs, and economies of scale when making capacity decisions. Although the suggested decision support system is used to manage the production and distribution of wood pellets, the insights and solution methodology may be extended to the production of other biofuels. The proposed research may be valuable to authorities involved in planning large-scale wood pellet-related production-distribution projects.

A deterministic real-time communication is required by the effective management of physical processes in Cyber Physical Systems including industrial automation, in-vehicle and avionic communication platforms. IEEE 802.1 Time Sensitive Networking (TSN) task group is the leading organization that aims to standardize Ethernet-based deterministic communication technologies. In this paper, a Multi-Topology Routing (MTR)-based traffic optimization approach is developed for the route planning of AVB streams in a TSN network with mixed-criticality support using the GRASP meta-heuristic. MTR was standardized by IETF as extensions to OSPF and IS-IS, and supports virtual topologies which have the same network graph as the physical topology, but with different link weights. Thanks to the diverse forwarding capabilities provided by the MTR concept, experimental results show that our approach significantly improves the schedulability of AVB streams in the majority of the scenarios compared to the other approaches in the literature.

Controller synthesis for general linear temporal logic (LTL) objectives is a challenging task. The standard approach involves translating the LTL objective into a deterministic parity automaton (DPA) by means of the Safra-Piterman construction. One of the challenges is the size of the DPA, which often grows very fast in practice, and can reach double exponential size in the length of the LTL formula. In this paper, we describe a single exponential translation from limit-deterministic Büchi automata (LDBA) to DPA and show that it can be concatenated with a recent efficient translations from LTL to LDBA to yield a double exponential, ‘Safraless’ LTL-to-DPA construction. We also report on an implementation and a comparison with other LTL-to-DPA translations on several sets of formulas from the literature.

This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with attention to forecast electricity prices, but they differ in the way the resulting forecasts are converted into operational decisions. The first method uses 24- to 48 h-ahead price forecasts within a mixed-integer linear programming rolling-horizon optimizer to compute the revenue-maximizing schedule for the following day. The second method uses an online twin delayed deep deterministic policy gradient controller that outputs a complete 24 h charge–discharge schedule once per day, using state information that includes battery state, recent price history, forecast prices, and forecast photovoltaic production. The control models are trained using historical data from 2019 to 2022, validated chronologically on 2023 data, and tested on the 2024 annual horizon, while the price forecaster is trained and validated on non-2024 data and evaluated on the held-out 2024 test period. In the realistic execution setting, schedules are planned using forecast photovoltaic production and implemented against actual photovoltaic production, while the day-ahead omniscience benchmark uses actual next-day prices and actual PV production as ideal scheduling inputs. The BiLSTM-MILP framework achieves EUR 10,928.7 over the 2024 test horizon, corresponding to 82.67% of the day-ahead omniscience benchmark. The online BiLSTM-TD3 controller achieves EUR 10,884.9, corresponding to 82.34% of the same benchmark and 99.60% of the BiLSTM-MILP revenue, while outperforming a rule-based baseline by 34.9%. These results show that online deep reinforcement learning can approach the performance of explicit mathematical optimization in day-ahead PV-battery trading while substantially improving over simple rule-based operation. Overall, the results indicate that BiLSTM-based forecasts can support both optimization-based and reinforcement-learning-based day-ahead control for the examined PV-battery system.

Modeling a system in engineering applications is a time-consuming and labor-intensive task, as system parameters may change with temperature, component aging, etc. In this paper, a novel data-driven model-free optimal controller based on deep deterministic policy gradient (DDPG) is proposed to address the problem of continuous-time leader-following multi-agent consensus. To deal with the problem of the dimensional explosion of state space and action space, two different types of neural nets are utilized to fit them instead of the time-consuming state iteration process. With minimal energy consumption, the proposed controller achieves consensus only based on the consensus error and does not require any initial admissible policies. Besides, the controller is self-learning, which means it can achieve optimal control by learning in real time as the system parameters change. Finally, the proofs of convergence and stability, as well as some simulation experiments, are provided to verify the algorithm’s effectiveness.