Multiobjective Tuning and Performance Assessment of PID Using Teaching-Learning-Based Optimization.

ABSTRACT: There have been many studies on the optimal tuning and control performance assessment (CPA) of the PID controller. In the optimal tuning, the trade-off between the setpoint tracking and the disturbance rejection performance is a challenge. Minimum output variance (MOV) is very widely used as a benchmark for CPA of PID, but it is difficult to be observed due to the non-convex optimization problem. In this paper, a new multiobjective function, considering both the OV in the CPA problem and integral of absolute error, is proposed to tune PID for this trade-off. The CPA-related non-convex problem and tuning-related multiobjective problem are solved by teaching-learning-based optimization, which guarantees a tighter lower bound for MOV due to the excellent capability of local optima avoidance a

SUBMITTER: Zhang W 

PROVIDER: S-EPMC8638005 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature