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摘要: 在资源约束的网络控制系统中, 控制性能和服务质量之间的折衷是不可避免的. 为了寻求它们的最佳结合点, 提出了结合约束条件的多目标规划问题来优化控制性能和网络带宽需求. 考虑算法的非线性逼近能力和计算速度, 采用了神经网络作为优化求解器. 它提供的优化解对每一个控制回路的带宽需求进行动态分配, 使得全局系统性能最大化的同时使带宽需求最小化. 仿真表明在网络控制应用中该优化策略对控制性能和网络带宽需求之间是一种有效的折衷方法.Abstract: In networked control systems (NCSs) with resource constraints, there is an unavoidable tradeoff between the control performance and the quality of service. To address these problems, we present multi-objective programming with a set of constraints to optimize control performance and bandwidth consumption for the first time. Thanks to robust nonlinear approximate function and computational cost, a feed-forward neural network as optimal approximator is employed. The role of the neural network, which provides a good approximation to the optimal solution, dynamically allocates the bandwidth of each control loop so that the overall system performance is maximized while bandwidth consumption is minimized. Preliminary simulation results show that the proposed optimal strategy is an effective tradeoff method between the control performance and bandwidth consumption in networked control applications.
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