Three‐time scale multi‐objective optimal PID control with filter design

Abstract

This paper presents the design of a multi-objective PID (proportional, integral, and derivative) controller in three-time scales for a system with load disturbances and sensor noise. The key to this design method is to divide the problem into three-time scales by following a singular perturbation approach, which allows for the optimization of fewer parameters in each time scale instead of all the three PID gains at once, hence less computation and design effort. The optimization objectives are the minimization of the overshoot and peak time, and the maximization of the closed-loop system's ability to reject noise and load disturbances. The impact of the integral action and filter on the optimal results is investigated by tuning the singular perturbation parameters. The obtained results show that the performance of the closed-loop system recovers the optimal solution, which is based on the fast subsystem, as the singular perturbation parameters get sufficiently small.

Engineering Fastest Control: A New Process Control Method for Thermal Power Units

“…Engineers have long pursued various control methods in their efforts to achieve automatic FGBC. These include optimal PID control [19], model predictive control (MPC) [20], dynamic matrix control (DMC) [21], internal model control (IMC) [22], and Smith estimated control [23]. However, achieving automatic FGBC has remained a persistent challenge.…”

Design of a decentralized control law for variable area coupled tank systems using H∞ complimentary sensitivity function

“…The unknown parameters   ,   ,   ,   ,   , and   in Equations ( 21) and (22) need to be determined. The unknowns are determined from Equations ( 23)- (28) where 𝛼 =…”

“…The implementation of FOPID controllers is reported in [20,21], where the controller parameters are determined with the teaching-learning optimization method and the Harris Hawks optimization algorithm, respectively. A multi-objective PID controller and a novel technique for maintaining the height of a QTS by reducing loop interactions with a feedback linearization controller are described in [22,23]. An adaptive SM controller for a QTS, considering disturbances and parametric uncertainties for robustness analysis is described in [24].…”