In recent years, due to the mass retirement of skilled workers, loss of expertise has emerged as a problem in Japan. Meanwhile, the performance of computer hardware has been drastically improving. Skill-based PID controllers utilizing a database have been proposed as a potential solution to this problem. However, these controllers may not respond to multiple demands of control performance from users because the controller was not considered in the evaluation from the users. As a solution to this problem, an expert controller based on a skilled worker's operating information with control performance assessments has been proposed. According to the method, I/O data, PID parameters that are estimated using the operating data and evaluation values of the skill of the skilled worker are stored in the database. From this, information vectors with high scores are selected, and a local PID controller is designed in response to the user's requirements. In the conventional research, the least squares method is applied for estimating the PID parameters from the operating data of the skilled worker, and there are no restrictions on their values. This risks a loss of physical meanings of PID parameters in the case that they have negative values. In this research, an expert controller using particle swarm optimization (PSO) is proposed. In this method, data obtained by human control of a control simulator constructed on a computer is used to estimate human's skill as PID parameters. Moreover, providing restrictions for the estimation of PID parameters enables them to preserve their physical meanings. In this research, the effectiveness of the proposed expert controller is verified using a control simulator.