A special electronic analogue computer facilitates a great number of instructive experiments on single-loop, multi-loop, multi-variable and multi-step control systems. At different points in the system, signals are measured and their concordance with theoretical results can be assessed. In contrast with digital computer techniques, simulation using analogue systems and signals is often more closely related to real-world continuous processes. Experience in measurement is also obtained and characteristic system behaviour can be recognised. The usefulness of this type of simulation of the transfer behaviour of systems and components is shown with reference to different examples.

In this chapter a problem of practical controller implementation that is often disregarded is presented, with the aim of demonstrating reasons for its occurrence, consequences if it is neglected, and some technical solutions for its removal. Close links to a correct control system simulation will be also shown.

This chapter is devoted to a significant problem in feedback control, namely the problem of delays in the loop. The longer the delays in signal processing, the poorer the control action is likely to be, including loss of the ability to control at all, i.e. the loss of stability. Many phenomena cause different types of delays, of which the most frequent are: Transport, i.e. a change occurring at one point in the process is detected elsewhere. Distributed process parameters, i.e. properties of continuous spatial variables. Latent changes in the process. In the following, the essential features of the control problems resulting from delays in the control loop are explained and some basic approaches for solutions are outlined. A method of complex plane representation of the control problem is presented and its application is demonstrated on a heat transfer process represented by a specific laboratory set-up. Three options of control, namely the classical PID control loop, a feed-forward application and state feedback, are presented, compared and discussed.

Traditionally, there are two different ways to show the need for a control system: to track a reference signal at either a higher power level or a distant location, or to keep a variable at a set point in the presence of external disturbances or changes in the process. Typical examples of the first case, so-called servosystems, are feedback amplifiers or steering systems. On the other hand, regulation systems are always present in the process industries. Although it is clear that both problems can be solved within a common framework, most control system design methodologies rely on controlled system behaviour under changes in the reference or set points. The purpose of this chapter is to deal with some industrial control problems where disturbance counteraction is the most relevant issue and to review the suitability of classical control solutions to deal with this particular viewpoint.

The control of thermal processes has been given much attention over recent years and the use of digital controllers has been suggested for such real-life applications. These controllers often consist of discrete time versions of classical PID controllers or sometimes modern predictive and adaptive controllers. In this chapter a comparison is made between predictive and PID control, exemplified on a practical control problem.Temperature control of a thermal treatment furnace with PID and predictive algorithms, respectively, has been presented. Experimental results show a better behaviour from predictive control because of its ability to deal with time delay processes. It is worth noting that predictive control is strongly sensitive to the value of the weighting factor λ.

Distributed parameter processes are dynamic systems the state of which depends not only on time but also on spatial coordinates. These processes are frequently encountered in important engineering problems. This chapter presents a practical approach to adaptive control of spatial temperature distributions in real thermal systems. Spline functions are used to move from the infinite-dimensional primary process representation to the finite-dimensional (lumped parameter) process description.Based on a spline approximation for spatially distributed signals and simple system decomposition, a technique for modelling and control of distributed parameter processes has been developed. The technique is applicable for distributed as well as boundary control. A reference signal for boundary control to be tracked by the control system, is generated on the basis of an inversion of the given distributed parameter model. Although the experiments were performed on spatially one-dimensional thermal systems, the ideas can be extended to more complex distributed systems.

The experience obtained working with adaptive control in real processes has some practical benefits in dealing with the implementation of this kind of control. In this chapter, relevant issues in adaptive control supervision have been highlighted to draw attention to the difficulties of adaptive control implementation in industrial environments. Based on this idea, a fuzzy controller supervising the correct behaviour of the adaptive loop has also been included. A laboratory set-up based on a configurable coupled-tank process has been used. In one of these configurations, it has chosen the more appropriate indicator set to carry out the supervisory functions, and two of the more relevant results, forgetting factor scheduling and estimator scheduling, have been presented. The tool it has developed is simple to handle and very suitable for training students, allowing for a number of process configurations with different control problems. Further, by means of a simulation package for discrete models, more exhaustive experimentation with other models which are more complicated in dynamic terms than the physical coupled-tank plant is available.

The cascade loop is a standard configuration used in DC motor drives. As an alternative, we can use the parallel loop scheme based on independent control of speed and current. Digital implementations of the two control methods are presented here for comparative purposes. Digital control algorithms have been implemented and tested on an IBM/PC or compatible computer provided with a commercial PCL812 interface card.