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This paper investigates and benchmarks quadrotor navigation and hold autopilots’ global control performance using heuristic optimization algorithms. The compared methods offer advantages in terms of computational effectiveness and efficiency to tune the optimum controller gains for highly nonlinear systems. A nonlinear dynamical model of the quadrotor using the Newton–Euler equations is modeled and validated. Using a modified particle swarm optimization (MPSO) and genetic algorithm (GA) from the heuristic paradigm, an offline optimization problem is formulated and solved for three different controllers: a proportional–derivative (PD) controller, a nonlinear sliding-mode controller (SMC), and a nonlinear backstepping controller (BSC). It is evident through the simulation case studies that the utilization of heuristic optimization techniques for nonlinear controllers considerably enhances the quadrotor system response. The performance of the conventional PD controller, SMC, and BSC is compared with heuristic approaches in terms of stability and influence of internal and external disturbance, and system response using the MATLAB/SIMULINK environment. The simulation results confirm the reliability of the proposed tuned GA and MPSO controllers. The PD controller gives the best performance when the quadrotor system operates at the equilibrium point, while SMC and BSC approaches give the best performance when the system does an aggressive maneuver outside the hovering condition. The overall final results show that the GA-tuned controllers can serve as a benchmark for comparing the global performance of aerial robotic control loops.

The use of electric power by wind generation in actual grids is hampered by its inherent stochastic nature and the penalty deviations adopted in several electricity regulation markets with respect to power quality requirements. Coupling wind farms with advanced Energy Storage Systems (ESS) can help their integration within grids. In this direction, several studies have been conducted, but the problem is still open due to the constraints and limitations regarding the ESSs time autonomy, time response, degradation issues and overall costs. In order to take into account these relevant aspects, advanced control algorithms are needed. In this paper, a Model-Based Predictive Controller (MPC) is presented. Such a controller minimizes the degradation of the ESS and the load tracking error while fulfilling the operational constraints and dynamics. The ESS considered is hydrogen-based and the study has been developed within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS aiming at building and integrating advanced control strategies for a hydrogen-based ESS within a wind farm fence. Numerical simulations show the feasibility and the effectiveness of the proposed approach.

A hydrogen-based ESS model includes several degradation costs of the electrolyzer and fuel cell.Degradation costs concern working cycles and life spans of the electrolyzer and fuel cell.Features, like warm and cold start, are considered through continuous and logical states combined.An MPC controller minimizes the logical state switching between different operating modes.The controller meets local load demand and maximizes revenue in the electricity market.

The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.

Hydrogen is a promising energy vector for achieving renewable integration into the grid, thus fostering the decarbonization of the energy sector. This paper presents the control platform architecture of a real hydrogen-based energy production, storage, and re-electrification system (HESS) paired to a wind farm located in north Norway and connected to the main grid. The HESS consists of an electrolyser, a hydrogen tank, and a fuel cell. The control platform includes the management software, the control algorithms, and the automation technologies operating the HESS in order to address the three use cases (electricity storage, mini-grid, and fuel production) identified in the IEA-HIA Task24 final report, that promote the integration of wind energy into the main grid. The control algorithms have been already developed by the same authors in other papers using mixed-logical dynamical modeling, and implemented via a two-layer model predictive control scheme for each use case, and are quickly introduced in order to make evident their integration into the presented architecture. Simulation test runs with real equipment data, wind generation, load profiles, and market prices are also reported so as to highlight the control platform performances.Note to Practitioners-The paper develops the integration between the management platform of a HESS, paired to a real wind farm in northern Norway, and the control algorithms aimed at scheduling hydrogen production and re-electrification on the basis of several forecast streams about exogenous conditions and different possible operating modes of the wind-hydrogen system.