自动化专业英语(王树青)3.4

3.4.1 Introduction

Model (Based) Predictive Control (MBPC or MPC), is not a specific control strategy but more of a very ample range of control methods developed around certain common ideas. These design methods lead to linear controllers which have practically the same structure and present adequate degrees of freedom. The ideas appearing in greater or lesser degree in all the predictive control family are basically：

.Explicit use of a model to predict the process output at future time instants (horizon).

.Calculation of a control sequence minimizing a certain objective function.

.Receding strategy, so that at each instant the horizon is displaced towards the future, which involves the application of the first control signal of the sequence at each step.

The various MPC algorithms (also called long-range Predictive Control or LRPC) only differ amongst themselves in the model used to represent the process and the noises and the cost function to be minimized. This type of control is of an open nature within which many works have been developed, being widely received by the academic world and by industry. There are many applications of predictive control successfully in use at the present time, not only in the process industry but also applications to the control of a diversity of processes ranging from robot manipulators to clinical anesthesia. Applications in the cement industry, drying towers and in robot arms, are described, whilst developments for distillation columns, PVC plants, steam generators or servos are presented. The good performance of these applications shows the capacity of the MPC to achieve highly efficient control systems able to operate during long periods of time with hardly any intervention。

MPC presents a series of advantages over other methods, amongst which stand out:

·it is particularly attractive to staff with only a limited knowledge of control ,because the concepts are very intuitive and at the same time the tuning is relatively easy

.it can be used to control a great variety of processes, from those with relatively simple dynamics to other more complex ones, including systems with long delay times or of non-minimum phase or unstable ones.

.the multivariable case can easily be dealt with.

.it intrinsically has compensation for dead times.

.it introduces feed forward control in a natural way to compensate for measurable disturbances.

.the resulting controller is an easy to implement linear control law.

.its extension to the treatment of constraints is conceptually simple and these can be included systematically during the design process.

.it is very useful when future references (robotics or batch processes) are known.

.it is a totally open methodology based on certain basic principles which allow for future extensions.

As is logical, however, it also has its drawbacks. One of these is that although the resulting control law is easy to implement and requires little computation, its derivation is more complex than that of the classical PID controllers. If the process dynamic does not change, the derivation of the controller can be done beforehand, but in the adaptive control case all the computation has to be carried out at every sampling time. Although this, with the computing power available today, is not an essential problem one should bear in mind that many industrial process control computers are not at their best as regards their computing power and, above all that most of the