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Networked iterative learning control design for discrete-time systems with stochastic communication delay in input and output channels

机译:输入和输出通道中具有随机通信延迟的离散时间网络迭代学习控制设计

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This paper develops two kinds of derivative-type networked iterative learning control (NILC) schemes for repetitive discrete-time systems with stochastic communication delay occurred in input and output channels and modelled as 0-1 Bernoulli-type stochastic variable. In the two schemes, the delayed signal of the current control input is replaced by the synchronous input utilised at the previous iteration, whilst for the delayed signal of the system output the one scheme substitutes it by the synchronous predetermined desired trajectory and the other takes it by the synchronous output at the previous operation, respectively. In virtue of the mathematical expectation, the tracking performance is analysed which exhibits that for both the linear time-invariant and nonlinear affine systems the two kinds of NILCs are convergent under the assumptions that the probabilities of communication delays are adequately constrained and the product of the input-output coupling matrices is full-column rank. Last, two illustrative examples are presented to demonstrate the effectiveness and validity of the proposed NILC schemes.
机译:针对输入和输出通道中出现随机通信延迟的重复离散时间系统,开发了两种导数型网络迭代学习控制(NILC)方案,并以0-1伯努利型随机变量为模型。在这两种方案中,电流控制输入的延迟信号被先前迭代中使用的同步输入代替,而对于系统输出的延迟信号,一种方案由同步的预定期望轨迹替代,而另一种采用分别由上一操作的同步输出。根据数学期望,对跟踪性能进行了分析,结果表明,对于线性时不变和非线性仿射系统,两种NILC都在通信延迟概率受到适当约束且乘积为乘积的前提下收敛。输入输出耦合矩阵是全列秩。最后,给出了两个说明性示例,以证明所提出的NILC方案的有效性和有效性。

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