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Stochastic optimal control of unknown linear networked control system in the presence of random delays and packet losses

机译:存在随机时延和丢包的未知线性网络控制系统的随机最优控制

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In this paper, the stochastic optimal control of linear networked control system (NCS) with uncertain system dynamics and in the presence of network imperfections such as random delays and packet losses is derived. The proposed stochastic optimal control method uses an adaptive estimator (AE) and ideas from Q-learning to solve the infinite horizon optimal regulation of unknown NCS with time-varying system matrices. Next, a stochastic suboptimal control scheme which uses AE and Q-learning is introduced for the regulation of unknown linear time-invariant NCS that is derived using certainty equivalence property. Update laws for online tuning the unknown parameters of the AE to obtain the Q-function are derived. Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control signals converge to optimal or suboptimal control inputs. Simulation results are included to show the effectiveness of the proposed schemes. The result is an optimal control scheme that operates forward-in-time manner for unknown linear systems in contrast with standard Riccati equation-based schemes which function backward-in-time.
机译:本文推导了线性网络控制系统(NCS)的随机最优控制,该系统具有不确定的系统动力学特性并且存在网络缺陷,例如随机延迟和丢包。所提出的随机最优控制方法使用自适应估计器(AE)和来自Q学习的思想,来解决带有时变系统矩阵的未知NCS的无限水平最优调节。接下来,介绍了一种利用AE和Q学习的随机次优控制方案,用于调节使用确定性等价性推导的未知线性时不变NCS。导出用于在线调整AE未知参数以获得Q函数的更新定律。使用李雅普诺夫理论证明所有信号都是渐近稳定的(AS),并且估计的控制信号会收敛到最佳或次优控制输入。仿真结果包括在内,以证明所提方案的有效性。结果是一种最优控制方案,与基于标准Riccati方程的方案在时间上向后起作用,该最优控制方案对未知的线性系统按时间向前进行操作。

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