Akihiro Naganawa

Study of Force Feedback Control Based on Positioning Information of Elastic Support Mechanism

Nanotechnology is based on a combination of many technologies such as high precise positioning and force control, especially magnetic recording, biotechnology and semiconductor industry require the utilization of nanotechnology. Until now there have been various actuator systems proposed, but the structure models have only working distances of either under a millimeter or over ten millimeters. Structure models with working distance ranging several millimeters has been designed a little. Therefore we are proposing a new structure design of actuator that would allow us to build actuator systems with working distances between those parameters. This new actuator consists of a voice coil motor and a new guide with an elastic support mechanism. The elastic support mechanism (ESM) consists of a special spring which is restricted to moving in only one direction. This new ESM does not cause any lost motion, mechanical play or friction with motion. Since characteristically voice coil motor thrusts and displaces the elastic support mechanism linearly, highly precise positioning and force control can be realized using a simple controller. We will evaluate the force control method from the displacement of ESM. This paper will provide basic data for developing future Nano-Actuator systems.

A design method of a generalized predictive control system based on parametrization of two-degree-of-freedom integral controllers

A new design method for a generalized predictive control (GPC) system based on parametrization of two-degree-of-freedom integral controllers has been proposed. The objective is to guarantee stability of the control system without depending on the design parameters and to achieve low sensitivity against the plant perturbation and the disturbance. The design procedure consists of two steps. First, we design a basic integral controller for a nominal plant using the linear quadratic Gaussian (LQG) method and parametrize a class of two-degree-of-freedom stabilizing controllers. Next, we tune the feedforward controller to incorporate the GPC method into our control structure. A numerical example is presented to show the effectiveness of the proposed method by comparing it with the conventional GPC method.

Model Predictive Control Based on the Parametrization of Integral Controllers

Abstract We propose a new design method for a model predictive control system based on the parametrization of integral controllers. The objective is to design the model predictive control system which guarantees the stability of the control system for a perturbed plant and to realize the robust tracking performance. In our design method, if a free parameter is stable, the stability of the control system is guaranteed. Control design is carried out while identifying plant uncertainty, which is unmodeled dynamics of an actual plant. A numerical example is given to illustrate the effectiveness of our proposed method.

A design method of generalized predictive control system based on parametrization of two-degree-of-freedom integral controllers

A new design method for a generalized predictive control (GPC) has been proposed. The objective is to enhance the tracking performance of the GPC and to maintain the robustness of the control system. In our proposed method, the control system is based on a parametrization of two-degree-of-freedom integral controllers. The two parameters in the parametrization can be used to incorporate the GPC method into our control structure and to achieve low sensitivity properties via loop transfer recovery technique. A numerical example is presented to show the effectiveness of the proposed method.

Model Predictive Control Using Coprime Factorization Approach.

Lately, a great deal of attention has been paid to model predictive control in the field of process control. However, stability of a closed-loop system has not been guaranteed since the stability depends on the characteristics of a reference trajectory as well as the dynamics of a plant. Recovery methods for the stability have been proposed, but it seems that these methods cannot guarantee stability except for some special cases. In this paper, we propose a new design method for the model predictive control using a class of all stabilizing controllers, which is called Youla parametrization. In our method, if a free parameter is stable, the stability, which is not guaranteed using a conventional method, is guaranteed irrespective of reference trajectory. Control design is carried out while identifying plant uncertainty, which is unmodeled dynamics of an actual plant. A numerical example is given to illustrate the effectiveness of our proposed method.

Closed-Loop Learning Control Using Coprime Factorization Approach.

In this paper, we propose a new design method for a learning control system. The objectives of this paper are to enhance the tracking performance of learning controllers and to suppress disturbances acting on a plant. The structure of the learning controller is based on the parametrization of all stabilizing controllers, which is called Youla parametrization. The design procedure is divided into two steps. First, we design a basic controller using the LQG (linear quadratic Gaussian) method and consider the parametrization based on the LQG controller. Then, we adjust the free parameter to obtain good tracking performance by the learning algorithm. A numerical example is given to illustrate the effectiveness of the proposed method.