T. Shigemasa

Bio: T. Shigemasa is an academic researcher from Toshiba. The author has contributed to research in topics: Automatic control & Model predictive control. The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

A model-driven PID control system and its case studies

Abstract: PID control systems are still used as a basic control technology in today's industries. However, there are issues on control performances for some controlled processes, such as long dead-time processes and unstable processes. In order to improve the control performances of PID Control systems, we proposed a new model-driven PID control system at the first ISA/JEMIMA/SICE Joint Technical Conference (2001) in Tokyo, which is combined with the following control blocks; PD feedback block, internal-model control (IMC) block and set-point filter block, based on the model driven control concept proposed by Kimura at the CDC2000 in Sydney. In this paper, the model-Driven PID control system and the design method are described and practical effectiveness, including two degrees of freedom properties, are shown through several case studies.

Performance-based parameter tuning method of model-driven PID control systems.

Abstract: In this paper, performance-based parameter tuning method of model-driven Two-Degree-of-Freedom PID (MD TDOF PID) control system has been proposed to enhance the control performances of a process. Known for its ability of stabilizing the unstable processes, fast tracking to the change of set points and rejecting disturbance, the MD TDOF PID has gained research interest recently. The tuning methods for the reported MD TDOF PID are based on internal model control (IMC) method instead of optimizing the performance indices. In this paper, an Integral of Time Absolute Error (ITAE) zero-position-error optimal tuning and noise effect minimizing method is proposed for tuning two parameters in MD TDOF PID control system to achieve the desired regulating and disturbance rejection performance. The comparison with Two-Degree-of-Freedom control scheme by modified smith predictor (TDOF CS MSP) and the designed MD TDOF PID tuned by the IMC tuning method demonstrates the effectiveness of the proposed tuning method.

Model-driven PID control system in single-loop controller

Abstract: PID control is widely used as a basic control technology in industries today, but tuning of PID control systems is not always easy. Based on model-driven control concept, we developed a model-driven PID control system, named MD PID controller, combining with PD local feedback, IMC and set point filter. This paper provides a brief introduction of new model driven two-degrees of freedom PID control system, named MD TDOF PID controller, and shows some case studies results. We have confirmed that MD TDOF PID can show better control results compared with conventional PID control.

A Hybrid Control Approach for Non-invasive Medical Robotic Systems

Abstract: In this paper, a hybrid supervisory control approach adopted for a non-invasive medical robot called Focused Ultrasound Surgical Robot--Breast Surgery (FUSBOT-BS) is elaborated. The system was built for the use in the breast surgery with high intensity focused ultrasound (HIFU) as the means of the treatment. A number of different control strategies such as PID and model-based control were incorporated into a family of controllers to create the hybrid control. Depending on the objective, the supervisory control determines the type of controller used for the specified task so as to maximize the advantages of each of the controllers. Before it was implemented into the actual robotic system the then proposed control approach was modeled and simulated using Matlab®. This control approach was developed based on a review of popular control approaches used in medical robotic systems, in order to look at the feasibility of having a uniform control strategy for a spectrum of medical robotic system. With unified control strategy it is possible to have a safety standard regulation for the medical robotic systems which is currently difficult to be done because of various control strategies adopted by each of the medical robotic systems.