Rocket reusability is a key-factor for a sustainable and cost-effective future of space missions. Despite successes in this technology, some of the open problems remain an impediment to further development. For example, the descent phase presents important challenges from a guidance and control perspective, because, due to model and environment uncertainty, the optimal re-entry trajectory is often unknown a priori. As a result, the previously designed (or offline) controller may be unable to track the reference trajectories provided by the adaptive (or online) guidance algorithm. This paper is an attempt to build up a methodology to address this issue. First, a systematic approach for H ∞ control design for a pre-defined nominal descent trajectory is described. The synthesis approach combines a disturbance rejection structured H ∞ design at fixed operating conditions with a blended gain-scheduling approach across different altitudes. Then, the problem of providing robustness over a family of reference trajectories is framed as a multi-plant control design approach. The proposed robust control synthesis approach shows a satisfactory performance with respect to system requirements in the face of a large variation in the tracked trajectories and provides a novel viewpoint on designing robust controllers to successfully deploy flexible guidance algorithms.
Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density function. In this paper, a decentralized ergodic multi-agent trajectory planning algorithm featuring limited communication constraints is proposed. The agents' trajectories are designed by optimizing a weighted cost encompassing ergodicity, control energy and close-distance operation objectives. To solve the underlying optimal control problem, a second-order descent iterative method coupled with a projection operator in the form of an optimal feedback controller is used. Exhaustive numerical analyses show that the multi-agent solution allows a much more efficient exploration in terms of completion task time and control energy distribution by leveraging collaboration among agents.
This work presents a systematic techno-economic assessment of 84 conventional and novel working fluid mixtures in two-stage, double-and triple-effect (Kangaroo) absorption refrigeration cycles. Rectifiers are modeled as staged distillation columns to capture appropriately the separation tasks. All mixtures are first evaluated based on process operating performance indicators including the coefficient of performance, the exergy efficiency, the cycle high pressure, the refrigerant and absorbent total flowrates, the total number of stripping stages and the distillate-to-feed ratios, subject to constraints ensuring feasible operation. The distillate-to-feed ratio, the number of stages in each rectifier and the individual flows of the refrigerant and the absorbent in different cycle circuits are considered as design parameters. The evaluation considers wide operating ranges, to identify the conditions that result in optimum values for the employed indicators. A multi-criteria approach is used to generate few, highly performing candidates which are further evaluated using economic criteria. A mixture of acetaldehyde/dimethylformamide is selected as the best performing working fluid which exhibits at best 39% lower cost per ton of cooling and 7% higher coefficient of performance than NH 3 /H 2 O in the tripeeffect cycle, with similar high performance observed in the double-effect cycle too. The same mixture exhibits at best 38% lower coefficient of performance and 1% lower cost per ton of cooling than H 2 O/LiBr.
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