Today, many engineers don't choose just one. They use or "Model-Based PID tuning," which uses predictive math to set the PID gains automatically. This offers the stability of PID with the "foresight" of predictive control.

PID and Predictive Control of Electrical Drives: Finding the Right Balance

It requires a high-performance processor and an accurate mathematical model of the drive. If your motor parameters change (like getting hot), the model might become inaccurate.

It struggles with "multi-variable" systems (like controlling torque and flux simultaneously) and doesn't handle physical limits—like voltage saturation—very gracefully.

It handles constraints (like current or voltage limits) natively. It is also exceptionally fast at responding to sudden changes in load or speed, often outperforming PID in dynamic precision.

PID control has been the industry workhorse for decades. It works by calculating an "error" (the difference between where the motor is and where you want it to be) and applying a correction based on the past, present, and predicted future of that error.