Standard benchmarks are important repositories to establish comparisons between competing model and control methods, especially when a new method is proposed. This paper presents details of an Arduino micro-controller temperature control lab as a benchmark for modeling and control methods. As opposed to simulation studies, a physical benchmark considers real process characteristics such as the requirement to meet a cycle time, discrete sampling intervals, communication overhead with the process, and model mismatch. An example case study of the benchmark is quantifying an optimization approach for a PID controller with 5.4% improved performance. A multivariate example shows the quantified performance improvement by using model predictive control with a physics-based model, an autoregressive time series model, and a Hammerstein model with an artificial neural network to capture the static nonlinearity. These results demonstrate the potential of a hardware benchmark for transient modeling and regulatory or advanced control methods.

标准基准是建立竞争模型与控制方法之间比较的重要存储库，尤其是在提出新方法时。本文介绍了一个Arduino微控制器温度控制实验室的详细信息，以此作为建模和控制方法的基准。与模拟研究相反，物理基准测试考虑了真实的过程特征，例如满足周期时间，离散采样间隔，与过程的通信开销以及模型不匹配的要求。基准测试的一个案例研究是量化PID控制器的优化方法，该方法的性能提高了5.4％。一个多变量示例显示了通过将模型预测控制与基于物理的模型，自回归时间序列模型结合使用来实现的量化性能改进，以及带有人工神经网络的Hammerstein模型来捕获静态非线性。这些结果证明了用于瞬态建模和监管或高级控制方法的硬件基准测试的潜力。