Abstract
Trajectory optimization problem for hypersonic vehicles has long been recognized as a difficult problem. This paper brings control constraints into the trajectory optimization to make the optimal trajectory meet the requirements of control performance. The strong nonlinear characteristic of the ascent phase aerodynamics makes the trajectory optimization problem difficult to be solved by the optimal control theory. A trajectory optimization algorithm based on the improved pigeon-inspired optimization (PIO) algorithm is proposed to solve the complex trajectory optimization problem under multiple constraints. To overcome the obstacle of premature convergence and deceptiveness, the evolutionary strategy of qubit in quantum evolutionary algorithm (QEA) is introduced into the PIO to maintain population diversity and judge the optimal solution. To handle constraints, the penalty function is used to construct the fitness function. The optimal ascent trajectory is obtained by utilizing the improved PIO algorithm. Then, the trajectory inverse algorithm is used to verify the feasibility of the optimal trajectory to ensure that a feasible optimal trajectory is obtained. The comparison results show that the proposed algorithm outperforms particle swarm optimization (PSO) and standard PIO on trajectory optimization. Meanwhile, the simulation result shows that the performance of the optimal ascent trajectory with control constraints is improved and the trajectory is feasible. Therefore, the method is potentially feasible for solving the ascent trajectory optimization problem under control constraint for hypersonic vehicles.
Highlights
The hypersonic vehicle has received wide attention as it has high speed and large flight range
A deceptive direction of convergence forestalls the exploration. To overcome these obstacles and obtain better solutions, this paper introduces quantum representation and quantum rotation gate in the quantum evolutionary algorithm (QEA) to improve the pigeon-inspired optimization (PIO) algorithm
The second part is to verify the feasibility of the optimal trajectory obtained by the improved PIO algorithm using the trajectory inverse algorithm
Summary
The hypersonic vehicle has received wide attention as it has high speed and large flight range. Fu et al solved the ascent trajectory optimization problem for hypersonic vehicles with the improved chicken swarm optimization (ICSO) algorithm [9]. A deceptive direction of convergence forestalls the exploration To overcome these obstacles and obtain better solutions, this paper introduces quantum representation and quantum rotation gate in the quantum evolutionary algorithm (QEA) to improve the PIO algorithm. If the current optimal solution still exists after iteration, the deceptive probability amplitude will decrease In this way, the accuracy of the PIO algorithm is improved.
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