【正文】 es a rollgap control system posed of a hydraulic mill stand in a number of automatic gauge control systems for hot rolling processes. The system consists of structure spring to represent a modulus of mill stand, material spring to represent a modulus of rolled strip, rollgap control cylinder to control a deformation of material spring, and disturbance cylinder to give the control system a disturbance. The force applied to rollgap cylinder is measured through Load cell, the deformation of structure spring through linearvariable differential transformer (LVDT), and the deformation of material spring through Linear scale. The rollgap control cylinder is a cylinder of ram type as in real hot rolling process, and the disturbance cylinder is just used to apply a disturbance to the system during rollgap control. A control current signal (i1) of the closedloop is used to perform the position control of rollgap is used to perform the position control of rollgap control cylinder, which is inputted to a servo valve connected to ram cylinder. The other current signal (i2) of the openloop is used to generate a disturbance, which is inputted to a servo valve connected to disturbance cylinder. In this article, responses of the system with the ?xed disturbance cylinder are considered. Table 1 shows some of the technical speci?cations of the experimental setup posed of power unit, rollgap control cylinder, structure spring, material spring, linear scale, servo valve, and interface card. diagram of the electrohydraulic position control system. Table1 Specifications of the ponents used in the experiment . A time delay controller (TDC) for the position control system A selected controller for the position control of this system is the TDC (YoucefToumi amp。 Hsia amp。 Ito, 1990). However, when a TDC is applied to real system, the setting of controller gains on the TDC has several problems. First, when a reference input is given, it is some practical problem how the locations of poles will be assigned. Furthermore though a trajectory is determined in the form of reference model for following the reference input, the ability of the system for following the trajectory is unknown before the controller is applied to real system (Chin et al., 1994). Secondly the value of E is related to the inertia of the system. In real system, the estimation of the system inertia is another hard problem. The error between the system constant and the estimated E directly affects the error dynamics of the closedloop system, so it bees a reason why the system does not follow the prescribed reference model with accuracy (YoucefToumi amp。 Park, 2020). The results of the experiments with the theoretical settings of the TDC controller gains were as follows. Because of the pressibility of the working ?uid excluded to simplify the system modeling, it is necessary that the damping ratio of the reference model is designed to be overdamped (? 1). It is very dif?cult to determine proper n? and ? due to the friction characteristics on the hydraulic system—the friction of hydraulic actuator is generally larger than other actuators. Though the controller gains are tuned for the system to have some satis?ed performance via the above mentioned procedures, it is uncertain that they are the optimal gains under a given performance index. Hence, in the case of the real system, the optimal controller gains of TDC can be searched and settled in the given gain regions via the proposed ESbased method. diagram of the position control system with a time delay controller. 3. Evolution strategies Since ESs had been developed for solving experimental optimization problems applied to hydrodynamics, they have been successfully applied to various optimization problems. As other evolutionary algorithms, ESs also depend on the concept of a population of individual. Based on initial population selected randomly in speci?ed region, the population evolves toward better ?tness regions of the search space through randomized processes of mutation, rebination, and selection. The ?tness, given as survivability of each individual, re?ects the evaluation level of each individual with respect to a particular objective function de?ned in each application. The object of this algorithm is to search the optimal solution that maximizes the objective function. Comparing with other evolutionary algorithms, its unique and im