Asymptotic normality analysis of the estimation error of steady-state models for industrial processes: SISO case

The asymptotic normality of the estimation error of steady-state models for industrial processes is investigated under quite mild conditions. The estimate is formed from the estimated parameters of an approximate linear model which is strongly consistent with the steady-state gain of slow time-varying linear SISO systems. In the parameter estimation, the weighted least-squares method is employed. The input signal (the system set point) is the usual step change din the optimization procedure. The rate of convergence is given. The stationarity and the distribution of the stochastic process are not demanded. It is also worth mentioning that, under some acceptable conditions, robustness to the structure of the approximate linear model is achieved. A simulation study shows that, for limited length of the sampled data, the best choice for the structure of approximate models as regards estimation precision is dependent upon the realization of the stochastic noise.     

Optimal control for large-scale systems: a recursive approach

A recursive fixed-point-type method is presented for the study of the optimal control problem of large-scale systems. The control is obtained by decomposition of the system to ‘ε coupled’ subsystems so that only low-order systems are involved in algebraic computations. It is shown that the developed reduced-order parallel algorithms converge to the desired solution with the rate O(ε). Owing to its recursive nature, the presented method produces a considerable saving of computation. An illustrative numerical example is given to verify the proposed approach.     

Comments on ‘Robust stabilization of uncertain linear dynamical systems’

Based on the stabilizability of a nominal system, by making use of the Lyapunov stability criterion and combining with the algebraic Riccati equation, Wu and Mizukami (1993) present a new approach to designing a robust linear state feedback controller for uncertain linear dynamical systems. The robust stabilization conditions given by Wu and Mizukami (1993), however, seem to be conservative and can be easily improved upon. In this note we design another robust linear feedback controller to render the uncertain system asymptotically stable. An example is presented to demonstrate our new control law and to compare it with the existing one.     

On the existence of solutions of the general algebraic Riccati equation

This paper addresses the problem of real symmetric solutions of the general algebraic Riccati equation (GARE) with an indefinite quadratic term. The GARE arises in linear quadratic differential games, in the stabilization of uncertain systems, robust optimal control and disturbance attenuation problems. Using the properties of the solutions of the differential equation corresponding to the GARE and the related conclusions of differential games, we have established the main results of this paper. Theorems 1 and 2     

Fault detection of NCS based on eigendecomposition,adaptive evaluation and adaptive threshold

A method for fault detection (FD) of networked control systems with unknown network-induced delay and unknown input based on eigendecomposition, adaptive evaluation and adaptive threshold is proposed in this paper. In the method, a known structure matrix of network-induced delay which is supposed to be smaller than one sampling period is extracted by eigendecomposition and an observer based residual generator is constructed. Then an adaptive evaluation function is proposed to ensure the optimal robustness to network-induced delay and unknown input at the evaluation stage based on the Parity Space approach. Finally, based on the estimation of the maximal influence of unknown random delay and unknown input on the evaluation function, adaptive threshold is designed to enhance robustness of the FD system and reduce the miss detection rate as much as possible.     

Non-linear output frequency response functions for multi-input non-linear Volterra systems

The concept of non-linear output frequency response functions (NOFRFs) is extended to the non-linear systems that can be described by a multi-input Volterra series model. A new algorithm is also developed to determine the output frequency range of non-linear systems from the frequency range of the inputs. These results allow the concept of NOFRFs to be applied to a wide range of engineering systems. The phenomenon of the energy transfer in a two degree of freedom non-linear system is studied using the new concepts to demonstrate the significance of the new results.     

Model reduction of singular systems

In this paper, model reduction for singular systems will be investigated. First, a previous model reduction algorithm is presented and proved to be not appropriate in practice. Detailed examination of this existing algorithm will show that the difficulty of model reduction for singular systems is to retain its impulsive nature. Thus, based on this kind of acute observation, we investigate impulsive controllability and impulsive observability and propose a new decomposition approach for singular systems. Then a new model reduction algorithm will be proposed on the basis of this new decomposition via the Nehari approximation technique. This new model reduction algorithm will retain the impulsive nature of the original singular system. Finally, one example will be presented to illustrate the effectiveness of the proposed model reduction algorithm.     

Sensor adaptive fault tolerant control for non-linear processes

Adaptive fault tolerant control of non-linear processes is an open problem. In this paper, on the basis of a strong tracking filter (STF), an approach to sensor adaptive fault tolerant generic model control for non-linear processes is proposed. When the process runs normally, Adaptive Generic Model Control (AGMC) based on parameter estimation is used to control non-linear time-varying processes. A sensor fault model is set up by introducing a bias vector into the output equation of the process. The bias vector is estimated on-line based on the STF during every control period. With the estimated sensor bias vector and the time-varying parameters, a fault detection mechanism is developed to supervise sensors. When a sensor fault is detected, AGMC will be switched to a state estimation and soft-sensor-based GMC. This strategy constitutes a sensor-adaptive fault tolerant generic model control for non-linear processes. Experimental results on a three-tank system demonstrate the effectiveness of the proposed approach.     

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).