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