Deep Imitation Learning for Optimal Trajectory Planning and Initial Condition Optimization for an Unstable Dynamic System
Bo-Hsun Chen,
Pei-Chun Lin
Abstract:In this article, an innovative offline deep imitation learning algorithm for optimal trajectory planning is proposed. While many state‐of‐the‐art works achieved optimal trajectory planning, their systems were stable or quasistable, and their approaches rarely optimized the system's initial conditions (ICs). Here, a new unstable dynamic system task called “internal sliding object stabilization control” is proposed, modeled, and solved by deep imitation learning. Given the system's ICs, the neural networks (NNs)… Show more
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