Scheduling method have been investigated for Just-In-Time (JIT) production systems with painting process that has longer lead time than the planning period of assemble line, followed by the painting prosess. In such JIT production systems, it is difficult to keep stable production between painting process and assemble line.Therefore, it is necessary that painting process has independent production schedule forecasted on the processing requirement jobs of assemble line. However, when multi-volume production is done or defective jobs occur in painting process, production balance between painting process and assemble line is destroyed, and then delays of due dates often occurred. To cope with this difficulty, we propose a scheduling method based on heuristic algorithm for painting process in the JIT production system in order to prevent delays of due date and to keep the smallest inventory in buffer of painting process. We have evaluated the scheduling method by computer simulation.
For the problem of estimating unknown parameters of the transfer function model from input and output (I/O) data contaminated by colored measurement noise, a three-step estimation procedure has been previously proposed to exploit the I/O correlation information with respect to the correlation time. This paper analyzes this procedure from the compatibility between the parameter transformation and the bias-compensated least-squares (BCLS) estimation used in the third step. The transformation is made from the over-parameterized model in the first and second steps to the final model with the true order in the final step, and is based on the minimization of the mean square error (MSE) between the outputs of the two models. The BCLS estimation is based on the minimization of MSE between the final model output and the process output. It is verified that the condition for compatibility, i.e. the equivalence of two minimizations, is a consequence of the two over-parameterized estimation equations in the first two steps, and that the over-parameterization assures an inclusive expansion of the I/O correlation information. Simulation results are presented to demonstrate the equivalence.
The system representation in the input-output data space, which was proposed recently, leads to a new control strategy for a linear time-invariant plant. The control input is computed directly from the input-output data of the plant without using any traditional mathematical model, such as transfer function or state space equation. This paper considers a dead-beat tracking problem for arbitrary reference signals in that framework, and presents the optimal control for a quadratic performance index
In this paper, we discuss methods for improving the generalization ability of a fuzzy classifier with ellipsoidal regions. In the fuzzy classifier, each cluster is approximated by a center and a covariance matrix, and the membership function is calculated using the inverse of the covariance matrix. Thus when the number of training data is small, the covariance matrix becomes singular and the generalization ability decreases. In addition, when the characteristics of the training and test data differ, the generalization ability decreases. In this paper, we improve the generalization ability controlling the number of singular values in the covariance matrix. Then we propose to divide the sampled data set into training and test data sets so that the centers and the covariance matrices of each class become similar. Finally, we demonstrate the validity of our methods by computer simulations.
In this paper, the generalized predictive control (GPC) with a terminal matching condition for single-input single-output (SISO) plants proposed by Kwok and Shah  is extended so that it can be applied to multi-input multi-output (MIMO) plants. For the extension to MIMO case, a state-space approach is used in the proposed method while a polynomial approach is used in the conventional method. This is because a state-space approach for the design of the proposed GPC makes the extension to MIMO case possible. In addition, a steady-state predictor originally derived in this paper using a state-space approach is shown to be completely equivalent to that in the conventional method when SISO plant is designed. Finally, a numerical example is demonstrated to show that a terminal matching condition in the MIMO case has the same role for improving performances of the controlled system as in the SISO case using conventional method.
This paper proposes an interesting technique for LMI-based multiobjective control design. We deal with strictly positive real H2 controller synthesis. If we partially fix both LMI solutions and controllers in the first step, a set of jointly convex LMI formula is obtained in the second step. The first step restricts the range of the parameter set to some degree, and the second step searches within these boundaries. However, if these constraints include the standard result for a common LMI solution, then the second step may improve this result while allowing uncommon LMI solutions. A numerical example is included.