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3/12/2002 - 1 Modeling and controlling the Caltech Ducted Fan Vehicle Steve Neuendorffer, Ptolemy Group, UC Berkeley
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3/12/2002 - 2 How do we get to an implementation? Abstract, high-level system model Detailed low-level system implementation
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3/12/2002 - 3 Some options… 1.Don't build a high-level model… Implement the system by hand. 2.Build a high-level model, but throw it away when implementation starts. 3.Build a high-level model, and validate implementation against it. 4.Build a low-level model, and generate an implementation from it. 5.Build a high-level model, refine it gradually into more detailed models that are more suitable for automatic code generation.
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3/12/2002 - 4 An example of heterogenous refinement Drive the ducted fan vehicle to a desired point. Continuous time vehicle and controller. A zero-delay discrete-time controller. A one-step delay discrete-time controller. An arbitrary delay discrete-time controller. A more sophisticated modal controller. A model for automatic system implementation.
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3/12/2002 - 5 1: Vehicle/Controller model (X,Y) Position, and direction of the vehicle Total forward thrust, and differential torque applied by the fans.
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3/12/2002 - 6 1: Continuous Vehicle model Specified as a differential equation, as in Simulink.
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3/12/2002 - 7 1: Continuous controller model A modified proportional controller…
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3/12/2002 - 8 2: Vehicle model with Discrete-time interface Zero-order hold models the Digital -> Analog conversion. The analog position of the vehicle is periodically sampled approximately every second.
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3/12/2002 - 9 2: Discrete Vehicle controller Heterogenous system modeling
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3/12/2002 - 10 3: Discrete Vehicle controller with one-sample delay SampleDelay added to model computation time of controller
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3/12/2002 - 11 4: Discrete Vehicle controller with arbitrary delay More Heterogeneity TimedDelay actor in Discrete Event domain models arbitrary delay. SDF control law CT vehicle model
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3/12/2002 - 12 5: A modal controller Yet More Heterogeneity
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3/12/2002 - 13 Breathe and watch the demo… A comparison of the modal controller versus the simple proportional controller.
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3/12/2002 - 14 Target specifics… (X,Y) Position, and direction of the vehicle come from video localization system. Control values are sent to motor controller by serial port.
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3/12/2002 - 15 6: A controller, with refined communication. Suitable for code generation One of the previous (possibly heterogenous) control laws
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3/12/2002 - 16 Code Generation Translation from a model to an efficient implementation is a highly skilled operation that is both error-prone and time-consuming. But, it is mostly a repeated application of well-known implementation patterns and optimization techniques. These patterns and techniques can be automated into modeling tools. Implementation team = System Modeler+Modeling Tool
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3/12/2002 - 17 Implementation Patterns in Ptolemy II Task 2.2: Customizing frameworks with generators Task 2.4: Generating embedded software from models
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3/12/2002 - 18 Code generation There are two parts to the generated code: Code generated from the model of computation Code generated from individual components. How is the code from individual actors generated? Option 1: The code generator can be implemented specifically for each component. Option 2: The code generator can be implemented generically for all components. However, being generic and efficient is difficult.
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3/12/2002 - 19 Mapping Ptolemy II Actors Actors and embedded code have very different design contraints. Actors are: parameterizable reusable reconfigurable generic Embedded code is: specialized optimized These are inevitably in conflict!
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3/12/2002 - 20 Mapping Java Actors We must avoid having a multitude of expensive, specialized implementations for each target platform. Method: Parse the Java code for an actor and specialize it.
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3/12/2002 - 21 Co-compilation Two main operations: 1)Optimize in the context in the model. (Inline parameter values) 2)Replace ports with communication primitives of platform. Actor Class file Abstract Syntax Tree Embedded Class file
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