A two dimensional large scale laser digitizer system with a cordless cursor was developed. The coordinate detecting scheme of this digitizer is fundamentally based on the triangulation method, in which two laser-rays are scanned by the rotating plane mirrors, reflected backward by the cursor, reflected again by the rotating mirrors, and detected by optical sensors. From angles in which the cursor reflections are detected, we can determine the position of the cursor. But this method involves several problems about optical alignment and its calibration especially when it is applied to a large scale digitizer. In this paper an appropriate model for calculating the coordinate of the cursor is proposed. The model provides an easy way for setting devices and achieving high accuracy of a measurement. The time required for setting the device is about 5 minutes and the average accuracy of 2m×6m and 3m×4m digitizer is within±0.15mm.
In this paper, high speed synchronization of multiple motion control axes under adaptive feedforward control is considered. The adaptive feedforward control system for one axis system consists of a proportional feedback controller and an adaptive feedforward controller. To synchronize the multiple motion control axes, the synchronizing controller, which responds to the tracking errors, is introduced. The structure of the synchronizing controller is investigated. The parameters of the adaptive feedforward controller are adjusted by the adaptation algorithm so that the tracking errors and the synchronization errors are removed. This results in the significant improvement of the tracking responses and the quick removal of the synchronization errors during transient as well as steady state. The synchronization for a four axes motion control system, of which parameters are significantly different, is investigated. Stability of the proposed synchronizing system is analyzed. Experimental results demonstrate the effectiveness of the proposed control scheme for synchronization.
ILQ (Inverse Linear Quadratic) method is a new design method for control systems which utilizes the result of the inverse problem of optimal regulator. This method is more effective when it is applied to the design of an optimal servo system. It is called “ILQ optimal servo system”. The existing theory has assumed the minimum phase condition for the plant. But this condition can be relaxed to a class of MIMO non- minimum phase systems which includes all SISO systems. This paper describes an extension of this theory to such a class of systems and at the same time it shows this extension is an achievable limit. In the extension, proofs of “analytical description of optimal gains” and “asymptotic property of a closed loop transfer function matrix” which are the basic properties of ILQ optimal servo system, are performed in a way different from the past papers. This paper also contains a numerical example to illustrate the effectiveness of this method for the non-minimum phase plant.
This paper proposes a robust design of the discrete time model reference adaptive control system in the presence of modeling errors and shows the results of the feasibility test applied to position control of a DC-motor. In this scheme, the dominant parameters of the discrete time plant-model expressed in terms of δ-operator are estimated by an adaptive law with dead zone and then the current estimates are used to determine the controller parameters by calculation. In the synthesis of the control input, the output error is fed back through a fixed compensator to reduce the tracking error due to the parameter adjustment-mismatch. Stability of the adaptive system is analytically proved to be guaranteed by setting the width of the dead zone of adaptive law appropriately. The feasibility test has verified that the proposed scheme can realize satisfactory control performance in spite of the presence of modeling errors.
A multiinput-multioutput type GMDH algorithm using regression-principal component analysis is described. In conventional GMDH algorithms, estimated values of output variables are used as intermediate variables and partial polynomials are constructed by using these intermediate variables. So, multiinput-multioutput type nonlinear system can not be indentified by using conventional GMDH algorithms because a large number of intermediate variables are generated in each selection layer and GMDH calculations can not be continued. The GMDH algorithm in this paper uses total characteristic variables, which can explain variation of all output variables, and optimal partial polynomials are constructed by combining these total characteristic variables.