Autotuning a PID Controller using Bayesian Optimization and JSL (2026-US-30MP-2947)

Proportional-integral-derivative (PID) controllers are ubiquitous in industrial settings, used for such applications as temperature regulation, flow and level control, and motor control. Achieving optimal performance often requires careful manual tuning of the three PID parameters.

In this presentation, we demonstrate automated PID tuning using Bayesian optimization, applied to controlling the angular position of a DC motor shaft. The process begins with a small set of manually executed runs. For each run, the PID parameter values and the error between the measured and desired responses are recorded in a JMP data table. These data are used to initialize the JMP Bayesian Optimization platform, which then recommends a new set of PID parameter settings.

A JSL script automates the tuning process by sending the suggested PID parameters to the physical controller, acquiring the resulting error signal, and appending the new results to the same data table. JMP Bayesian Optimization is then rerun to find the parameters for the next run. This iterative cycle continues automatically until convergence is achieved.

Finally, we use Graph Builder to create displacement vs. time curves to visualize how the improvement occurred. This presentation is a practical example of combining experimentation, scripting, and Bayesian optimization in JMP to solve a real-world control problem.