In this paper, we present an advanced control framework for power management applications, named Neural Adaptive Model Predictive Control (NA-MPC), designed to provide an optimal power allocation among multiple energy sources, perform a multi-objective online adaptation of the optimal control policy, and ensure a fast real-time execution with low computational demand. NA-MPC augments general MPC problems with three key features: 1) an online metaheuristic tuning strategy adapts the MPC cost function weights, to attain multiple concurrent control objectives at once; 2) through neural emulation, the MPC control policy is replaced by an equivalent neural MPC controller, exhibiting universal approximation guarantees and ensuring real-time feasibility; 3) a neural black-box MPC prediction model is employed, identified only via noise-corrupted input-output measurements from the plant, which is assumed to be unknown. The general formulation and versatility of NA-MPC make it potentially applicable to several power management scenarios; in this work, we apply NA-MPC to the case study of power management in fuel cell hybrid electric vehicles (FCHEVs), a topic of growing interest within the frame of sustainable transportation, for which novel and efficient strategies are still lacking. The effectiveness of NA-MPC is thoroughly assessed via numerical simulations, demonstrating its capability to optimally attain multiple control objectives concurrently in real time; moreover, NA-MPC consistently outperforms the most prominent state-of-the-art HEV power management strategies.

This article presents an approach to drag-free and attitude control for the laser interferometer space antenna (LISA) space mission, based on a constrained decoupling $H_{\infty }$ approach. LISA will be a space-based gravitational wave observatory, which is expected to be launched by the European Space Agency (ESA) in 2034. The LISA concept consists of a constellation of three satellites that exchange a bidirectional laser link to perform interferometry. The gravitational waves can be detected by measuring the relative distance variations, by means of laser interferometers, between two free-falling bodies located at a far distance, called the test masses (TMs). In this framework, the spacecraft (SC) drag-free attitude control plays a key role since it allows the TMs to move in free-fall conditions, rejecting external disturbances and noises, at the nanoscopic level, that can compromise the quality of scientific measurements. To this end, we propose an $H_{\infty } $ drag-free attitude controller, based on a constrained decoupling of the SC linearized dynamics, where the pseudoinverse of the control matrix is obtained by minimizing the inversion error. Moreover, we provide sufficient conditions for stability of the closed-loop, in order to ensure that the decoupling inversion error does not affect the closed-loop stability. The effectiveness of the proposed approach is confirmed by means of an extensive Monte Carlo campaign, carried out employing a high-fidelity simulator.

Missions finalized at measuring the space-time variations of the Earth gravity field have become of high relevance in recent years. These missions are indeed of interest for scientific purposes and applications in several fields. Precise observations of the Earth gravity field can be accomplished by measuring the distance between two satellites flying in suitable orbits. Several mission concepts foresee an active formation control to maintain the distance variations between the two satellites within given bounds. In this paper, we first present an original constellation model, called the Triangle Dynamics (TD) model, which is particularly suitable to describe the orbital dynamics of satellite pairs. Open-loop simulations are performed, where the TD model is compared with a standard model, derived from the well-known Hill–Clohessy–Wiltshire (HCW) equations. The simulation results show that the TD model provides more accurate predictions than the HCW model. Then, we propose a formation control approach based on a new Model Predictive Control (MPC) algorithm. The core of this algorithm is the TD model, which is used in real-time to predict the behavior of the satellite pair, allowing the computation of an optimal formation control command. A case study concerned with the Next Generation Gravity Mission (NGGM) is presented to demonstrate the effectiveness of the proposed MPC-TD algorithm.