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

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使用偏置粒子群优化解决约束轨迹规划问题

约束轨迹优化一直是先进制导和控制系统开发的关键组成部分。规划不当的参考轨迹可能是导致在线控制性能不佳的主要原因。由于存在各种与任务相关的约束，轨迹优化模型的可行解空间可能被限制在相对狭窄的走廊内，从而容易导致局部极小或不可行解检测。在本文中，我们有兴趣尝试使用有偏粒子群优化方法处理约束轨迹设计问题。所提出的方法通过引入一个反映约束违反总量的附加规范化目标，将原始问题重新表述为无约束多准则版本。除了，$\varepsilon$ -bias 选择方法和进化 RS。提供了从受约束的大气进入轨迹优化示例获得的数值模拟实验，以验证所提出的优化策略的有效性。通过执行一些比较案例研究，还突出了与所提出方法相关的主要优点。