Design of plant-friendly PID controllers based on constrained optimization

Large variations of the control variable add to plant wear and tear. In view of this fact, this paper provides a plant-friendly PID controller design method by minimizing the weighted sum of the integral of squared time-weighted error and the integral of squared time-weighted derivative of the control variable with respect to a step set-point change. The robustness of the system is guaranteed by a constraint on the maximum sensitivity. The optimization problem is solved using a real-coded genetic algorithm. This method only requires the process frequency response, which can be directly obtained from input-output data. Applications to typical process models and comparative studies show that the proposed method can efficiently reduce the variations of control variable and usually obtains a smoother step response with a shorter settling time. It is also shown that the filter time constant of the PID controller can serve as a balance to make proper trade-offs between transient performance and plant-friendliness.