具有通信约束的反馈辅助PD型量化迭代学习控制

本文针对网络线性系统, 研究了具有通信约束的反馈辅助PD型迭代学习控制问题. 信号从远程设备传输到 迭代学习控制器过程中, 存在数据量化与数据包丢失的情况. 将数据包丢失模型描述为具有已知概率的伯努利二 进制序列, 采用扇形界方法处理数据量化误差, 提出了一种反馈辅助PD型迭代学习控制算法. 采用压缩映射法分析 证明了在存在数据量化和丢失的情况下, 所提控制算法依然可以保证跟踪误差渐近收敛到零. 并进一步对存在初 始状态偏移时所提算法的鲁棒性进行了讨论. 最后, 通过仿真示例, 对比验证了理论结果的有效性和优越性.

Feedback-assisted PD-type quantized iterative learning control with communication constraints

The feedback-assisted PD-type iterative learning control (ILC) problem is studied for network linear systems with communication constraints in this paper, where data quantization and dropouts occur during transmission from the remote device to controller. The quantization error issue is handled by the sector bound method and the packet loss is modeled by a Bernoulli binary sequence with known probability distribution. A feedback-assisted PD-type ILC is proposed and the asymptotical convergence of the tracking error to zero is proved by the contraction mapping technique. The robustness of the proposed algorithm in the presence of initial state shifts is further discussed. Simulation examples are provided to demonstrate the effectiveness and superiority of the proposed scheme.