A data-driven computational heuristic is proposed to control MIMO systems without prior knowledge of their dynamics. The heuristic is illustrated on a two-input two-output balance system. It integrates a self-adjusting nonlinear threshold accepting heuristic with a neural network to compromise between the desired transient and steady state characteristics of the system while optimizing a dynamic cost function. The heuristic decides on the control gains of multiple interacting PID control loops. The neural network is trained upon optimizing a weighted-derivative like objective cost function. The performance of the developed mechanism is compared with another controller that employs a combined PID-Riccati approach. One of the salient features of the proposed control schemes is that they do not require prior knowledge of the system dynamics. However, they depend on a known region of stability for the control gains to be used as a search space by the optimization algorithm. The control mechanism is validated using different optimization criteria which address different design requirements.

翻译：提出一种数据驱动的计算逻辑,以控制MIMO系统,而不事先了解其动态。在二进制的双输出平衡系统中演示了脉冲。它将自调整的非线性阈值与神经网络结合起来,以在系统理想的瞬态和稳定状态特性之间达成妥协,同时优化动态成本功能。关于多个互动PID控制循环的控制收益的脉冲决定。神经网络在优化一个像目标成本功能那样的加权衍生功能方面受过培训。发达机制的性能与使用PID-Riccati组合法的另一个控制器进行了比较。拟议的控制计划的一个突出特征是,它们不需要事先了解系统动态。然而,它们取决于一个已知的稳定区域,控制收益将被优化算法用作搜索空间。使用不同的优化标准来验证控制机制,这些优化标准可以满足不同的设计要求。