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Robust model predictive control with aperiodic actuation

机译：具有非周期性致动的鲁棒模型预测控制

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We consider a control design problem using wireless sensor/actuator networks. Such systems need to operate within the limited resources of available battery life and bandwidth. To address these concerns, we take a model predictive control (MPC) approach for perturbed LTI systems with constraints on the admissible input and state sets. We propose a triggering mechanism (TM) that aims to reduce the number of MPC updates, with the goal to reduce the communication and computation loads. The TM uses trajectories that have been computed at the last update instant and a current measurement to determine whether or not to trigger an update. The TM consists of two parts: 1) inequalities that are functions of the error signal between the observed states and the predicted trajectories, guaranteeing recursive feasibility, and 2) a scalar inequality, that is a function of a weighted version of the value function at the last triggering instant, guaranteeing closed-loop convergence. Numerical simulations demonstrate the effectiveness of our TM in reducing the number of MPC updates, thereby possibly reducing the communication load as well.
机译：我们考虑使用无线传感器/执行器网络进行控制设计问题。这种系统需要在可用电池寿命和带宽的有限资源范围内运行。为了解决这些问题，我们采用具有在可允许输入和状态集的约束的扰动LTI系统的模型预测控制（MPC）方法。我们提出了一种触发机制（TM），旨在减少MPC更新的数量，以减少通信和计算负载。 TM使用在上次更新即时计算的轨迹和当前测量来确定是否触发更新。 TM由两个部分组成：1）不等式，其是观察状态和预测轨迹之间的误差信号的函数，保证递归可行性和2）标量不等式，这是值函数的加权版本的函数最后触发即时，保证闭环收敛。数值模拟证明我们TM在减少MPC更新数量时的有效性，从而可能降低了通信负载。

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