The SVM is one of the methods which can introduce the statistical learning theory for solving the pattern recognition
problem with small samples and learning problems such as function estimation. The relationships between the states with
desirable responses can be expressed by some functions and these functions are estimated by using SVM. There are
classification problems and regression problems in support vector machines. Only the support vector regression problem is
used in this paper. This paper proposes a very novel method which makes it possible that state feedback controller can be
designed for unknown dynamic system with measurable states. The SVR algorithm is used for the identification of inputoutput
relationship. A virtual state space representation is derived from the relationship and the SVM makes the relationship
between actual states and virtual states. For unknown dynamic systems, a state feedback controller can be designed based
on the virtual system and the SVM makes the controller being with actual states. The results of this paper can give many
opportunities that the state feedback control can be applied for unknown dynamic systems. The first step of this design
method is to identify the input-output relationship of the unknown system as a transfer function by using SVR. Next step is
to design a virtual system based on the transfer function. Final step is to derive the relationship between the actual states and
virtual states by using SVM. The linear kernel function is used in SVR. A state feedback controller is designed based on the
virtual system and the virtual system can be replaced by actual states by using the above relationship and it gives the actual
states feedback controller. And simulation results are provided to show the performance of the proposed control method.
Finally, the results of this paper make it is possible to the state feedback control theory to be used for unknown dynamic
systems. This result can be expected to be applied to unknown nonlinear systems.
In this paper, a novel sliding mode controller is proposed by using neural network for IPM machine. The current control for
interior permanent magnet machines is more complicate than surface permanent magnet machine because of its torque
characteristic depending on the reluctance. For high performance torque control, it requires state decoupling between the dcurrent
and q-current dynamics. However the variation of the inductances, which couples the state dynamics of the currents,
makes the state decoupling difficult. This paper presents a novel approach for fully decoupling the states cross-coupling
using sliding mode control with neural network. The sliding mode control method is based on the error between reference
currents and the currents with state decoupling which have to follow the references. In the conventional sliding mode
control, the dynamic of sliding surface is not as same as nominal dynamic of original system. To overcome this problem,
this paper proposes a new design method of a sliding surface without defining any additional dynamic state by using neural
network. Finally, the proposed sliding surface can have the dynamics of nominal system controlled by PI controller.
The current control for interior permanent magnet(IPM) machines is more complicate than surface permanent magnet (SPM) machine because of its torque characteristic depending on the reluctance. For high performance torque control, it requires state decoupling between the d-current and q-current dynamics. However the variation of the inductances, which couples the state dynamics of the currents, makes the state decoupling difficult. So some decoupling methods have
developed to cope this variations and each current can be regulated independently. This paper presents a novel approach for the decoupling of the states cross-coupling using sliding mode control. The sliding mode control method is based on the error between reference currents and the actual currents. The proposed method has the advantages of PI control performance and the sliding mode control robustness. Its first design step is to design PI controller, then the sliding mode control input term is added to it. This makes actual implementation of the controller easier.
A laser ultrasonic testing system using a confocal Fabry-Perot interferometer and a pulsed Nd:YAG laser is developed for the fatigue test of materials. To stabilize the fringe pattern of the confocal interferometer, an adaptive stabilization fringe control system is developed using two photodiode signals. The closed-loop fringe control system is operated automatically. The optical system is composed of many polarization components, such as a half-wave plate, quarter-wave plates and polarization beam splitters to improve the signal to noise ratio. The laser ultrasonic system carried out performance test. The optical configuration of the interferometer system and the stabilization module are investigated in this paper. The experimental results of the basic experiments are also described.
A robust measuring technique for the wavefront is one of the key parts for a stable adaptive optics system in the practical fields. Also, the measurement resolution of the wavefront is important for improving the performance of an adaptive optics system. In this paper, we propose a robust measuring technique for the wavefront using a Shack-Hartmann wavefront sensor for an adaptive optics system. The proposed measuring technique for the wavefront uses an iterative center of mass algorithm with the hierarchical sizes of a searching window and the hierarchical threshold values. The measurement accuracy and stability are investigated using the proposed hierarchical algorithm and compared with the conventional algorithm of the wavefront in the experiments. Also, we describe the hardware configuration of the adatptive optics system operating in our laboratory.
A new center position detecting algorithm of the spot image for the Shack-Hartmann wavefront sensor was developed. The new algorithm is a modified center of weight algorithm, which uses some power of the grey level intensity of the spot images instead of thr grey level intensity itself of the spot images. From experiments, the repeatability and accuracy of the center position detection of the spot images of the Shack-Hartmann wavefrond sensor which used the new algorithm were improved compared with the conventional center position detection algorithm using the center of weight. Applications of the algorithm to measurement of the displacement of the spot images and the Shack-Hartmann wavefront sensor for measuring wavefornt distortion and the experimental results of closed-loop wavefront correction are described in this paper.