...
首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >Concurrent learning adaptive control of linear systems with exponentially convergent bounds
【24h】

Concurrent learning adaptive control of linear systems with exponentially convergent bounds

机译:具有指数收敛范围的线性系统的并行学习自适应控制

获取原文
获取原文并翻译 | 示例
           

摘要

Concurrent learning adaptive controllers, which use recorded and current data concurrently for adaptation, are developed for model reference adaptive control of uncertain linear dynamical systems. We show that a verifiable condition on the linear independence of the recorded data is sufficient to guarantee global exponential stability. We use this fact to develop exponentially decaying bounds on the tracking error and weight error, and estimate upper bounds on the control signal. These results allow the development of adaptive controllers that ensure good tracking without relying on high adaptation gains, and can be designed to avoid actuator saturation. Simulations and hardware experiments show improved performance.
机译:开发了并发学习自适应控制器,该控制器同时使用记录的数据和当前数据进行自适应,用于不确定的线性动力系统的模型参考自适应控制。我们表明,对记录数据的线性独立性的可验证条件足以保证全局指数稳定性。我们利用这一事实来开发跟踪误差和权重误差的指数衰减边界,并估计控制信号的上限。这些结果允许开发自适应控制器,该控制器在不依赖高自适应增益的情况下确保良好的跟踪,并且可以设计成避免执行器饱和。仿真和硬件实验表明性能有所提高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号