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Chapter 10 Controllers for automation 251

10.1.2 Advanced control systems

It is current industrial practice to treat each axis of a multiaxis system as an individual
servo mechanism. This approach models the varying dynamics of a system inadequately,
because it neglects the motion and therefore the changes of the configuration within the
system, particularly those changes that occur in manipulators. These changes can be
significant, and they may render the conventional-control-strategy approach ineffective.
The result of this approach is a reduction in the servo’s response speed and in damping,
which limits the speed and the precision of the system. Any significant gain in perfor-
mance requires consideration of more efficient dynamic models of sophisticated control
techniques, and of the use of advanced computer architectures. With advances in
real-time computing, the implementation of a range of advanced techniques is now
possible; techniques based on either adaptive control or on artificial-intelligence
approaches (for example, fuzzy logic or neural networks) are of particular interest.
Among the various adaptive-control methods being developed, model-reference
adaptive control is the most widely implemented, see for example Liu, Lara-Rosano
and Chan (2004) and Syam et al. (2015). This concept is based on the selection of an
appropriate reference model and on an adaptation algorithm that can modify the
feedback gains of the control system. The adaptation algorithm is driven by the errors
between the reference-model outputs and those of the actual system. As a result of this
approach, the control scheme only requires moderate computation, and it can therefore
be implemented on a low-cost microprocessor. Such a model-reference adaptive control
algorithm does not require complex mathematical models of the system dynamics, nor
does it require an a priori knowledge of the environment of the load. The resultant
system can give good performance over a wide range of motions and loads. This
approach is discussed further in Section 8.3, when applied to the vector control of
induction motors.
A majority of currently available motor-drive system requires a considerable amount
of signal processing - which has been made easier by the development of the digital
signal processor or DSP. A DSP is a powerful microprocessor that is capable of
processing analogue data in real time. This real-time capability makes a DSP suitable for
applications such as the sensorless control of brushless motors. As the algorithms used
for digital signal processing require many mathematical operations to be performed
quickly and repeatedly on a series of data samples, the architecture of a DSP has been
optimised specifically for digital signal processing. In additions signals (for example
phase currents or voltages) are constantly converted from analogue to digital, manipu-
lated digitally, and then converted back to analogue form. A DSP also supports some of
the features typically found in an applications processor or microcontroller, since signal
processing is rarely the only task required to be undertaken. DSP instruction sets often
consist of instruction sets optimized for digital signal processing contain instructions
for common mathematical operations that occur frequently in DSP calculations, for
example a modern DSP is capable of handling 40-bit IEEE Floating-Point Mathematics