The following are comments by Aptronix engineers on the
differences between designing with traditional PID
control versus fuzzy logic control. There is an
assumption that you understand the classic control
problem of balancing an inverted pendulum. For more
details please see the Aptronix Quick Start Quide, User
Manual and Reference Manual.
------------------------------------------------------
Fuzzy Control Systems
Aptronix, Inc.
1. For Non-linear, Dynamic System.
As is well known, the conventional linear model-based
controllers can be designed according to some optimal
criteria, and the optimality and stability can be proved.
However, it is difficult to design a optimal or stable
controller for a nonlinear, dynamic, and ill-understood
process, which is common in the real world. One practical
method is to simplify and linearize the non-linear model.
After the simplification, the optimality and stability are
only for the simplified model. For an ill-understood
process, its model is unknown and it is impossible to use
conventional methods to design a controller. In these cases,
human experience should be utilized.
For example, in the designing of a controller for an
inverted pendulum, one must first simplify the real process
model by linear equations and then design an optimal
controller for the simplified model.
Fuzzy logic controllers utilize human knowledge by
describing the control strategies by linguistic rules. For
the inverted pendulum example, a basic control strategy will
be `if the pendulum declines to the right Fast, then move
the cart to the right Fast'. The fuzzy term `Fast' can be
represented by a fuzzy set. By considering the cases more
carefully, we can fine tune these rules.
Such kind of knowledge exists in many industrial control
processes. Clearly, the control rules are model-free: no
matter how (mathematically) difficult the process is, an
experienced operator can still give some control rules.
Fuzzy logic controllers are suitable for non-linear,
dynamic, and ill-understood processes.
2. Robustness
PID (Proportional-Integral-Derivative) control is the
major practical technology that is widely used in
industries. However, the performance of PID controllers
depends heavily on the operating parameters of the system.
If there is any change in the system, a significant amount
of time is required to tune the controllers. As a result,
the average industrial plant operator ends up running over
50% of his PID loops in manual mode.
For example, if the length of the pendulum changes, the
parameters of the linear controller should be changed
accordingly.
Fuzzy controllers are more robust. If the length of the
pendulum changes in a certain range, the control rules and
fuzzy sets need not change. Physical demonstrations have
proven this robustness in several international conferences
of fuzzy systems.
3. Short Development Period
In the design of a linear controller, one should do
the following steps after selecting the sensors:
1. Modelling: Build a mathematical model describing
the process.
2. Linearization: Linearize the model.
3. Solving equations: Make a trial design based on
optimal control or other criteria.
4. Simulation: Simulate the design. If not satisfied,
go to step 1.
For a fuzzy controller, the steps are:
1. Analysis: Analyze the process.
2. Acquisition of rules: Acquire control rules from
experience operators.
3. Simulation: Simulate the fuzzy controller. If not
satisfied, got to step 1.
For the processes that are difficult to model but have
straightforward control rules, the fuzzy controllers are
easy to design and implement. Since the fuzzy controllers
are designed directly from the properties of the process,
the development time will be shorter than for conventional
controllers.
4. Transparency
Since fuzzy controllers are designed according to
experience, they are more transparent than conventional
controllers.
The parameters of conventional controllers are computed from
equations under certain conditions. The parameters and
fuzzy sets in fuzzy controllers are defined according to
experience. Because of transparency, maintenance
and upgrading are easy.
This information is provided by
Aptronix FuzzyNet
408-428-1883 Data USR V.32bis
Voice 408-428-1888
FAX 408-428-1884