Advanced Control Methods
1. Adaptive Control
An adaptive control system can be defined as a feedback
control system intelligent enough to adjust its characteristics
in a changing environment so as to operate in an optimal
manner according to some specified criteria.
Generally speaking, adaptive control systems have
achieved great success in aircraft, missile, and spacecraft
control applications. It can be concluded that traditional
adaptive control methods are mainly suitable for (1)
mechanical systems that do not have significant time
delays; and (2) systems that have been designed so
that their dynamics are well understood.
In industrial process control applications, however,
traditional adaptive control has not been very successful.
The most credible achievement is probably the above-described
PID self-tuning scheme that is widely implemented
in commercial products but not very well used or accepted
by the user.
Traditional adaptive control methods, either model
reference or self-tuning, usually require some kind
of identification for the process dynamics. This contributes
to a number of fundamental problems such as (1) the
amount of off line training required, (2) the tradeoff
between the persistent excitation of signals for correct
identification and the steady system response for
control performance, (3) the assumption of the process
structure, and (4) the model convergence and system
stability issues in real applications.
In addition, traditional adaptive control methods
assume the knowledge of the process structure. They
have major difficulties in dealing with nonlinear,
structure variant, or large time delayed processes.
2. Robust Control
Robust control is a controller design method that
focuses on the reliability (robustness) of the control
algorithm. Robustness is usually defined as the minimum
requirement a control system has to satisfy to be
useful in a practical environment. Once the controller
is designed, its parameters do not change and control
performance is guaranteed.
The robust control methods, either in time domain
or frequency domain, usually assume the knowledge
of process dynamics and its variation ranges. Some
algorithms may not need a precise process model but
then require some kind of off-line identification.
The design of a robust control system is typically
based on the worst case scenario, so that the system
usually does not work at optimal status in sense of
control performance under normal circumstances.
Robust control methods are well suited to applications
where the control system stability and reliability
are the top priorities, process dynamics are known,
and variation ranges for uncertainties can be estimated.
Aircraft and spacecraft controls are some examples
of these systems.
In process control applications, some control systems
can also be designed with robust control methods,
especially for those processes that are mission critical
and naturally have (1) large uncertainty ranges, and
(2) small stability margins.
However, the design of a robust control system requires
high level expertise. Once the design is properly
accomplished, the system should work well without
the need of much operator attention. But on the other
hand, if upgrades or major modifications are required,
the system has to be redesigned.
3. Predictive Control
Predictive control, or model predictive control (MPC),
is one of only a few advanced control methods used
successfully in industrial control applications.
The essence of predictive control is based on three
key elements: (1) predictive model, (2) optimization
in range of a temporal window, and (3) feedback correction.
These three steps are usually carried on continuously
by computer programs on-line.
Predictive control is a control algorithm based on
the predictive model of the process. The model is
used to predict the future output based on the historical
information of the process as well as the future input.
It emphasizes the function of the model, not the structure
of the model. Therefore, state equation, transfer
function, and even step response or impulse response
can be used as the predictive model. The predictive
model has the capability of showing the future behavior
of the system. Therefore, the designer can experiment
with different control laws to see the resulting system
output, using computer simulation.
Predictive control is an algorithm of optimal control.
It calculates future control action based on a penalty
function or performance function. The optimization
of predictive control is limited to a moving time
interval and is carried on continuously on-line. The
moving time interval is sometimes called a temporal
window. This is the key difference compared to traditional
optimal control that uses a performance function to
judge global optimization. This idea works well for
complex systems with dynamic changes and uncertainties
since there is no reason in this case to judge the
optimization performance based on the full time range.
Predictive control is also an algorithm of feedback
control. If there is a mismatch between the model
and process, or if there is a control performance
problem caused by the system uncertainties, the predictive
control could compensate for the error or adjust the
model parameters based on on-line identification.
Due to its essence of predictive control, the design
of such a control system is very complicated and requires
high level expertise although the predictive control
system works well in controlling various complex process
control systems. This expertise requirement appears
to be the main reason why predictive control is not
used as widely as it deserves to be.
Due to its nature, predictive control is well suited
for advanced process control (APC) and supervisory
control applications, where the control outputs are
mainly a trajectory of setpoints. Predictive control
is not well suited to deal with regulatory control
4. Optimal Control
Optimal control is an important component in modern
control theory. Its great success in space, aerospace,
and military applications has changed our lives in
The statement of a typical optimal control problem
can be expressed in the following: The state equation
and its initial condition of a system to be controlled
are given. The defined objective set is also provided.
Find a feasible control such that the system starting
from the given initial condition transfers its state
to the objective set, and minimizes a performance
In principal, optimal control problems belong to the
Calculus of Variations. Pontryagin’s Maximum
Principal and Bellman’s Dynamic Programming
are two powerful tools to solve closed set constrained
variation problems, which are related to most optimal
In practice, optimal control is very well suited for
space, aerospace, and military applications such as
the moon landing of a spacecraft, flight control of
a rocket, and the missile blocking of a defense missile.
In industrial systems, there are some optimal control
related issues such as the control of bacteria content
in a bioengineering system, etc. However, most process
control problems are related to the control of flow,
pressure, temperature, and level. They are not well
suited to the use of traditional optimal control techniques.
5. Intelligent Control
Intelligent control is another major field in modern
control technology. There are different definitions
regarding intelligent control, but it is referred
to a control paradigm that uses various artificial
intelligence techniques, which may include the following
methods: (1) learning control, (2) expert control,
(3) fuzzy control, and (4) neural network control.
Learning control uses pattern recognition techniques
to obtain the current status of the control loop;
and then makes control decisions based on the loop
status as well as the knowledge or experience stored
previously. Since learning control is limited by its
stored knowledge, its application has never been popular.
Expert control, based on the expert system technology,
uses a knowledge base to make control decisions. The
knowledge base is built by human expertise, system
data acquired on-line, and inference machine designed.
Since the knowledge in expert control is represented
symbolically and is always in discrete format, it
is suitable for solving decision making problems such
as production planning, scheduling, and fault diagnosis.
It is not well suited for continuous control issues.
Fuzzy control, unlike learning control and expert
control, is built on mathematical foundations with
fuzzy set theory. It represents knowledge or experience
in a mathematical format that process and system dynamic
characteristics can be described by fuzzy sets and
fuzzy relational functions. Control decisions can
be generated based on the fuzzy sets and functions
Although fuzzy control has great potential for solving
complex control problems, its design procedure is
complicated and requires a great deal of specialty.
In addition, fuzzy math does not belong to the Field
of Mathematics since many basic mathematical operations
do not exist. For instance, the inverse addition is
not available in fuzzy math. Then, it is very difficult
to solve a fuzzy equation, yet solving a differential
equation is one of the basic practices in traditional
control theory and applications. Therefore, lack of
good mathematical tools is a fundamental problem for
fuzzy control to overcome.
Neural Network Control
Neural network control is a control method using artificial
neural networks. It has great potential since artificial
neural networks are built on a firm mathematical foundation
that includes versatile and well understood mathematical
tools. Artificial neural networks are also used as
one of the key elements in the Model-Free Adaptive