Solving Constrained Trajectory Planning Problems Using Biased Particle Swarm Optimization

Constrained trajectory optimization has been a critical component in the development of advanced guidance and control systems. An improperly planned reference can be main cause poor online performance. Due to existence various mission-related constraints, feasible solution space model may restricted relatively narrow corridor, thereby easily resulting local minimum or infeasible detection. In this article, we are interested making an attempt handle constrained design problem using biased particle swarm approach. The proposed approach reformulates original unconstrained multicriterion version by introducing additional normalized objective reflecting total amount constraint violation. Besides, enhance progress during evolutionary process, algorithm is equipped with exploration operation, novel ?-bias selection method, evolution RS. Numerical simulation experiments, obtained from atmospheric entry example, provided verify effectiveness strategy. Main advantages associated method also highlighted executing number comparative case studies.