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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
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
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