Benchmark temperature microcontroller for process dynamics and control.

• Physics-based and data-driven modeling approaches are quantified with the TCLab. • A PID tuning method is proposed to replay disturbances from closed-loop data. • Model predictive control is benchmarked with three standard models. • Real-time results are presented to motivate further benchmark studies. 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. [ABSTRACT FROM AUTHOR]