INTELLIGENT COMPUTER INTEGRATED MANUFACTURING (I-CIM): Research Perspectives

Abstract

Manufacturing automation has progressed through various stages from simple data transfer to the intelligence-intensive systems. The future of CIM relies heavily on intelligence-intensive systems because manufacturing is no longer confined to one local site and manufacturing systems have become complex because of their global nature. In this article, the authors study the future manufacturing environment as a collaborative effort. The essential characteristics-the requirements for integration from a process and communication perspective-are identified as are steps in the process requiring further study. Finally, Intelligent-Computer Integrated Manufacturing (I-CIM) scenarios are presented for specific problems.     

Parameter identification of distributed parameter systems in the presence of measurement noise

A new approach to recursive parameter identification of second-order distributed parameter systems in the presence of measurement noise under unknown initial and boundary conditions is proposed. A two-dimensional low-pass filter is introduced to pre-filter the observed data corrupted by measurement noise. The low-pass filter is designed in the continuous time-space domain and discretized by bilinear transformation. Thus a discrete estimation model of the system under study is easily constructed with filtered input-output data for recursive identification algorithms. The recursive least squares method is still efficient in the presence of low measurement noise if the filter parameters are designed so that the noise effects are reduced sufficiently. Using filtered input data as instrumental variables, a recursive instrumental variable method is also presented to obtain consistent estimates when the digital low-pass filters are not designed successfully or when the output data is corrupted by high measurement noise. Illustrative examples are given to demonstrate the applicability of the proposed methods.     

Integral-equation approach to parameter identification without consideration of initial conditions

The widely used integral-equation approach for identification of continuous systems is investigated. Attention is focused on the initial condition problem which has been puzzling many researchers. A new calculation procedure for the multiple integrations of the system signal derivatives is proposed and a new integral-equation approach is proposed for which the initial conditions need not be identified as unknown parameters. Therefore, the burden of the identification algorithms can be greatly reduced compared to conventional methods. Discussions on the unification of the integral-equation approach with the other methods are also provided.     

A new bias-compensating LS method for continuous system identification in the presence of coloured noise

A new bias-compensating least squares (LS) method is presented for the parameter estimation of linear single-input single-output (SISO) continuous-time systems. A discrete-time model obtained by using the linear integral filter is augmented by introducing a pre-filter on the input and then the parameters of the augmented model are estimated by the conventional LS method. The distinct characteristic roots of the pre-filter are used to estimate the bias in the LS estimate. The pre-filter should be chosen so that its frequency bandwidth is wider than those of the system and the input signals. Since the new method requires minimal information on the noise characteristics, it is easily applicable to the case of coloured noise.     

Implementation of multi-rate model reference adaptive control for continuous systems via bilinear transformation based on block-pulse functions

The discretization or approximation techniques for continuous systems using the well-known delta operator and the bilinear transformation based on block-pulse functions or the trapezoidal rule are discussed. Then implementation techniques of multi-rate indirect model reference adaptive control for continuous systems purely using digital computers arc described. The scheme is composed of three components: a general recursive least-squares type parameter estimator, a continuous plant model and a controller designed in continuous-time domain. To reduce the computational burden, the algorithm is implemented in a multi-rate manner with a small sampling interval of the system signals and a relatively large parameter estimation interval. Comparisons of the discretization methods for the adaptive system using block-pulse functions, the trapezoidal integrating rule and the well-known delta operator are discussed through simulation study. It is shown that the block-pulse function method is the most effective one.     

Optimal scanning measurement problem for a stochastic distributed-parameter system

This paper deals with the problem of determining the optimal measurement scheduling for a stochastic distributed-parameter system (DPS) based on spatially continuous and discrete-scanning observations. These two types of measurement are realized by the optimal motion of spatially-movable sensors and the optimal selection of measurement data from spatially-fixed sensors, respectively. For the continuous scanning case, the existence of optimal solutions for the problem is proved and the N-modal approximation problem is established. For the discrete case, however, it is impossible to derive the existence conditions. Therefore, it is shown that there exist optimal relaxed solutions by introducing the relaxed control theory. A practical method for constructing an approximate solution for the relaxed problem is proposed. Necessary conditions for approximate-optimality are represented in the form of a matrix minimum principle, and a feasible algorithm is developed to determine the approximate solution. A numerical example is considered and the present two types of optimal measurement trajectory are compared.