基于记忆的数据驱动MRAC架构确保参数收敛
Memory-Based Data-Driven MRAC Architecture Ensuring Parameter Convergence
AI总结：
本文提出一种基于记忆的数据驱动MRAC架构，无需PE条件和系统矩阵知识，实现跟踪和控制器参数估计误差的有限时间收敛，并通过Lyapunov证明了方法的有效性。
摘要：
标准模型参考自适应控制（MRAC）中控制器参数的收敛性要求系统状态持久激励（PE），这是一个需要在线验证的严格条件。一种近期的数据驱动方法，即并发学习，利用丰富的过去数据与标准参数更新定律同时进行，以保证参数收敛而无需PE条件。该方法保证了跟踪和控制器参数估计误差的指数收敛到零，而经典MRAC仅保证跟踪误差渐近收敛到零。然而，该方法需要知道状态导数，至少在状态值存储于内存的时间实例。该方法还假设知道控制分配矩阵。本文通过使用基于记忆的有限时间系统标识符与数据驱动方法相结合，解决了这些限制，从而在不需PE条件和系统矩阵及状态导数知识的情况下实现跟踪和控制器参数估计误差的收敛。通过Lyapunov基于的稳定性证明来证明所提出的数据驱动方法的有效性。仿真结果展示了所提方法的有效性。
英文摘要：
Convergence of controller parameters in standard model reference adaptive control (MRAC) requires the system states to be persistently exciting (PE), a restrictive condition to be verified online. A recent data-driven approach, concurrent learning, uses information-rich past data concurrently with the standard parameter update laws to guarantee parameter convergence without the need of the PE condition. This method guarantees exponential convergence of both the tracking and the controller parameter estimation errors to zero, whereas, the classical MRAC merely ensures asymptotic convergence of tracking error to zero. However, the method requires knowledge of the state derivative, at least at the time instances when the state values are stored in memory. The method further assumes knowledge of the control allocation matrix. This paper addresses these limitations by using a memory-based finite-time system identifier in conjunction with a data-driven approach, leading to convergence of both the tracking and the controller parameter estimation errors without the PE condition and knowledge of the system matrices and the state derivative. A Lyapunov based stability proof is included to justify the validity of the proposed data-driven approach. Simulation results demonstrate the efficacy of the suggested method.
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