A hybrid hyper-heuristic whale optimization algorithm for reusable launch vehicle reentry trajectory optimization

A novel predefined performance barrier function based adaptive sliding mode control method for RLV under mismatched disturbance

“…With the improvement of technology and the rapid growth of space transportation demand, reusable launch vehicle (RLV), as a cheap and convenient delivery vehicle travelling between outer space and earth, has always been the unremitting pursuit of aerospace technology development for various countries (Gu et al, 2022;Li and Hu, 2018;Su et al, 2021). Compared with expendable launch vehicles, RLV has a shorter prefiring cycle and requires more simple maintenance, which greatly reduces the launch cost (Li et al, 2020;Tian et al, 2015).…”

“…With the improvement of technology and the rapid growth of space transportation demand, reusable launch vehicle (RLV), as a cheap and convenient delivery vehicle travelling between outer space and earth, has always been the unremitting pursuit of aerospace technology development for various countries (Gu et al. , 2022; Li and Hu, 2018; Su et al. , 2021).…”

A Four-Dimensional Space-Time Automatic Obstacle Avoidance Trajectory Planning Method for Multi-UAV Cooperative Formation Flight

“…At present, the commonly-used UAV trajectory planning algorithms can be divided into two categories: one is traditional algorithms for classical optimization [6,7], mainly consisting of dynamic planning, rapidly exploring random tree (RRT), Voronoi diagram, A * algorithm [8][9][10][11][12][13] and Dijkstra algorithm; and the other is modern intelligent optimization algorithms [14], including differential evolution (DE) [15], ant colony optimization (ACO), particle swarm optimization, and whale optimization algorithm (WOA) [16][17][18]. As plenty of researchers have studied and applied these two types of algorithms, they have suggested a cooperative coevolutionary genetic algorithm in the literature [19], which solves the problem of trajectory planning of multiple UAVs in two-dimensional space. In the literature [20], the artificial bee colony (ABC) algorithm and simulated annealing (SA) algorithm have been combined to solve issues of cooperative trajectory planning in two-dimensional space.…”

“…To this end, this paper proposes a trajectory planning approach with automatic obstacle avoidance for multiple UAVs to achieve formation flight, which is a way of prior trajectory planning according to flight tasks. The main innovations of this paper are listed as follows: (1) compared with the literature [19,20], the algorithm in this paper could be applied to trajectory planning in three-dimensional space; (2) this method combined the Hungarian algorithm with the hierarchical decomposition strategy, which is of significantly better efficiency and can achieve planning the optimal trajectory in only a few hundred milliseconds; (3) under the condition of no communication between UAVs in formation, the method proposed in this paper could realize automatic obstacle avoidance and be able to automatically generate an obstacle-free path through collision detection; (4) unlike previous studies [23,24] without actual tests, the method proposed in this paper is verified by actual tests and achieved good experimental results.…”