论文标题
动态系统的在线多目标自适应跟踪机制
Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems
论文作者
论文摘要
机器人文献中通过使用各种健壮和自适应控制方法来解决最佳跟踪问题。但是，这些方案与实现限制有关，例如在不确定的动态环境中适用性，具有完全或部分模型的控制结构，离散时间环境中的复杂性和完整性以及复杂耦合的动态系统中的可扩展性。开发了一种在线自适应学习机制来应对上述限制，并为一类跟踪控制问题提供了通用的解决方案平台。该方案可将跟踪误差最小化，并使用同时使用线性反馈控制策略来优化整体动力学行为。采用了基于价值迭代过程的强化学习方法来求解基础的Bellman最优方程。最终的控制策略会以交互方式实时更新，而无需有关基础系统动态的任何信息。自适应批评家的手段用于实时近似最佳解决价值函数和相关的控制策略。在模拟中说明了所提出的自适应跟踪机制，以控制不确定的空气动力学学习环境下的柔性翼飞机。
The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical environments with complete or partial model-based control structures, complexity and integrity in discrete-time environments, and scalability in complex coupled dynamical systems. An online adaptive learning mechanism is developed to tackle the above limitations and provide a generalized solution platform for a class of tracking control problems. This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneous linear feedback control strategies. Reinforcement learning approaches based on value iteration processes are adopted to solve the underlying Bellman optimality equations. The resulting control strategies are updated in real time in an interactive manner without requiring any information about the dynamics of the underlying systems. Means of adaptive critics are employed to approximate the optimal solving value functions and the associated control strategies in real time. The proposed adaptive tracking mechanism is illustrated in simulation to control a flexible wing aircraft under uncertain aerodynamic learning environment.
