Multivariable controller design in an adaptive control scheme

A multivariable control system design approach in the frequency domain is presented. The issue of control system redesign in an adaptive control scheme is discussed. Expert system techniques have been used for the automated design of controllers for multivariable systems. Design knowledge has been represented using rules, facts and objects. The design process consists of a sequence of operations obtained by heuristics and experience in the the design techniques. An automated design session of a multivariable plant using the expert system demonstrates that the expert system appears to work well in its prototype implementation.     

Pole shifting for matrices over real algebras

Pole shifting of multivariable control system over finite dimensional real algebras is studied. The main results are expressed in terms of the minimal polynomial of A + BF, where F is a feedback.     

Accidents,often the result of an ‘uncontrolled business process’—a study in the (Dutch) chemical industry

Often companies in the (petro‐) chemical industry claim that all possible countermeasures against potential accidents have been taken and therefore accidents are unforeseeable. In this paper we question this statement by analysing the pre‐warning signals (precursors) preceding a number of industrial accidents. 17 accidents that occurred in the (petro‐) chemical industry have been investigated by exploring FACTS, an accident database containing information about industrial accidents worldwide. This paper will demonstrate that the existence of precursor information could have been used to foresee and even prevent these accidents if a proper control action had been initiated. The accidents are analysed further, according to a control model, which was adapted from that of C. Argyris. It demonstrates the ineffectiveness of several elements of business process control loops and that the so‐called ‘double‐loop learning’ cycle is more important than the ‘single‐loop learning’ cycle if one considers safety improvement. Copyright © 2003 John Wiley & Sons, Ltd.     

A new voltage control equipment using fuzzy inference

Power system control equipment needs higher sensitivity and operational reliability. Advanced voltage control equipment is needed for reducing the frequency of tap changes and improving the characteristics (the relationship between the actual voltage and reference voltage) of the voltage to meet today's power system requirements. However, these objectives are in a trade-off relationship. Studies of voltage control derived from a knowledge base suitable for electric power systems can satisfy these objectives using fuzzy inference. Compared with corresponding conventional equipment, the new equipment improved the deviation of 30 min average voltage of 30 percent. This paper describes the design concept of new voltage control equipment using fuzzy inference. In addition, field test results are described along with rules of fuzzy inference, membership functions, and the deviation of 30 min average voltage through detailed simulation.     

On a responsive replenishment system: a fuzzy logic approach

In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.     

A self-organizing fuzzy control of weld pool size in GMA welding processes

In arc welding processes, because of the complexity and nonlinearity of heat transfer phenomena, it is often difficult to design an effective control system based upon an exact mathematical model. In this paper, a self-organizing fuzzy control method is proposed to obtain a uniform weld quality by regulation of the surface temperature at a desired level. The proposed control system is designed to comprise a combined structure of the fuzzy SOC with conventional PD control for improving the system's stability. The control experiments show that the proposed controller guarantees a uniform weld quality for various welding conditions.     

Adaptive control in a heating vessel: A comparative study

This paper presents experimental results obtained in temperature adaptive control of a heating vessel. A non-linear model of the process showed that the feed flow rate causes severe variations in time constant and process gain. An extended horizon self-tuning controller and two different model reference adaptive controllers were tested. The influence of design parameters on control performance for each algorithm was assessed. The control system was disturbed with load and setpoint changes.