1. Balancing an Inverted Pendulum with a Multi-Layer Perceptron
ECE 539 Final Project
Spring 2000
Chad Seys

2. Outline The Inverted Pendulum The Problem Approach
Position Representation
Output Force Representation
Initialization
Convergence & Reinitialization
Results
Discussion

3. The Inverted Pendulum:
Abstraction is a rigid rod attached at its lower end to a pivot point.
Like balancing a broom on the palm of hand.
Useful in modeling:
Launching a rocket into space
look up another

4. The Problem: Train a multi-layer perceptron to...
keep an inverted pendulum in its upright position
move an inverted pendulum from any position to the upright position (keep it balanced there).

5. Approach: Divide the 180 degrees into M arc segments (where M is odd).
M odd to provide a central region where no force is applied.
There will be M input neurons, one per segment.
There will be two output neurons whose outputs will be interpreted as opposing force vectors of fixed magnitude.

6. Inverse Pendulum Position Representation
A few of the possibilities to explore:
(Chosen) A “1” in the input dimension corresponding to the arc segment which the inverse pendulum currently occupies, “0” in other dimensions.
As above, but have a gradual decline to “0” in neighboring segments.
Might help prevent overshoot at the top.
Alternatively, put “0” to the left of inv pendulum, “0.5” at the inv pendulum, and “1” to the right of the inv pendulum.
Might provide more directional information.

7. Output Force Representation
The output neuron force vector will act perpendicularly to the center of mass of the inv pendulum.
Will use a supervised learning paradigm.
Training data will be a fixed correcting force to return the inverse pendulum to the vertical.
Ideally would use a unsupervised learning paradigm allowing varying correcting force magnitudes, but unsure how to implement.

8. Initialization
at top with a small movement in one or the other direction
at increasing angles from the top with no movement. (not included in final version of project)

9. Convergence & Reinitialization
The standard: Amount of match between output and the teacher’s data.
Also, over how many simulation steps does the inv pendulum stay within a small number of degrees of the top. Stability.
This may be the criteria for reinitialization.
May not reset the network weights, only the inverse pendulum position.
(did not appear in the final version of project)

10. H Hidden Neurons M Input M Arc Segments Fixed Output Force1
H Hidden
Neurons
M Input H
M Arc Segments
Fixed Output Force
2 Output Neurons

11. Results (Force vs. Time Step):
Difficult to find a balance of force and sampling interval.
Using too large of a force would result in over-correction.

12. Results (Force vs. Time Step):
Too small of a force resulted in under correction.
Smaller time steps solve this problem, but increase memory usage and processing time.

13. Did not reach 100% convergence.
Ran one promising (which appeared not to be under or over corrected) simulation for a period of several days (>69000 iterations) and achieved a convergence rate of only 61.3%.
By the way the pendulum falls during the testing section of the simulation, the neural network does not yet appear to have “learned” to balance the inverse pendulum.

14. Results
Did not succeed in balancing a inverse pendulum during the duration of the simulation runs.