Software Listing of Author : "Alan Jennings"

A 4 DOF arm based on the Motoman IA20 was built in AutoDesk Inventor. SimMechanics is used to generate a Simulink model. This model generates the dynamics for the robot implicitly, so there is no need to solve the equations of motion by hand. The dynamics are used by an control optimization software: DIDO, available commercially. Sample results are included along with a video and analysis which compares it against the traditional trajectory.

  • Platform: Matlab, Scripts

SimMechanics is used to simulate a pendulum at the tip of a rotating arm, called a furuta pendulum. The equation of motion are then simulated by ode45 to show they are the same. All nonlinear dynamics are simulated (only one on file exchange I've seen that does that), so the equations can be used to design swing up controller. Also shows how to use function handles so to use ode with a single file. Simulation does NOT include friction or inertia besides the axial inertia. File still works without SimMechanics, but cannot show comparison. If there's interest for a mat file with an example output, just let me know.

  • Platform: Matlab, Scripts

The form of a single layer feed forward neural network lends itself to finding the gradient. This is useful when the network is used for surrogate optimization or other algorithms that use gradients. Requires creating a file by modifying a NN toolbox file.

  • Platform: Matlab, Scripts

Suspended and inverted pendulums were built in AutoDesk Inventor. SimMechanics is used to generate a Simulink model. This model generates the dynamics implicitly, so there is no need to solve the equations of motion by hand. The dynamics are used by control optimization software: DIDO, available commercially. Sample results are included along with analysis which compares it against a linearly-optimal state transformation and the linear-quadratic-regulator. In addition, optimality conditions are verified based on the explicit equations of motion.

  • Platform: Matlab, Scripts

Given a set of data points, this function calculates the center and radius of the data in a least squared sense. The least squared equations are used to reduce the matrix that is inverted to a 3x3, opposed to doing it directly on the data set. Does not require a large arc or many data points. Assumes points are not singular (co-planar) and real... Created on R2010b, but should work on all versions.

  • Platform: Matlab, Scripts
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