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.
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