Differences

This shows you the differences between two versions of the page.

--- ece6554:project_invcartpend [2023/03/15 11:59] – [Tip 2: Implementing the Function Approximator] classes
+++ ece6554:project_invcartpend [2024/08/20 21:38] (current) – external edit 127.0.0.1
@@ Line 119: / Line 119: @@
 My units might be off for the friction, but the values are fine.
+===== Implementation =====
+Functional code stubs for the implementation are provided in the {{ ECE6554:projects:invpendcart.zip | invpendcart zipfile}}.  They implement a constant control signal that most definitely fails to do the job, but provide enough structure to complete the project.  Missing is a reasonable implementation of a single layer Gaussian radial basis function neural network. That's left to be coded up as a class and properly used as part of the Inverted Pendulum on a Cart neuro-adaptive controller.
 ====== Activities ======
 ----------------------
@@ Line 208: / Line 211: @@
 ----------------------
-Even though the neuro-adaptive controller was done first, a sequencing that makes sense goes from no adaptation to matched adaptation and eventually to neur-adaptation.  I might be mistaken, but the neuro-adaptive part should have some ability to correct for the unmatched since those dynamics are influencing the error.  To support each of these parts, the report should include the appropriate controlled equations of motion for the different realizations (linear, nonlinear, matched+unmatched, etc).  It should cover the controller design and control synthesis for static and adaptive cases. If attempting an adaptive structures slightly different from what was covered in the lectures, then its derivation should be included.  If using adaptive controllers covered in class, then only their setup and final adaptive laws should be covered. Trajectories applied should include regulation (moving to a new, feasible set point) and tracking. Just like in homeworks, attention should be paid to highlighting how the static controller fails to perform under incorrect parameters estimates. Otherwise, the //Final Deliverable// assignment item should cover what's needed.
+Even though the neuro-adaptive controller was done first, a sequencing that makes sense goes from no adaptation to matched adaptation and eventually to neuro-adaptation.  I might be mistaken, but the neuro-adaptive part should have some ability to correct for the unmatched since those dynamics are influencing the error.  To support each of these parts, the report should include the appropriate controlled equations of motion for the different realizations (linear, nonlinear, matched+unmatched, etc).  It should cover the controller design and control synthesis for static and adaptive cases. If attempting an adaptive structures slightly different from what was covered in the lectures, then its derivation should be included.  If using adaptive controllers covered in class, then only their setup and final adaptive laws should be covered. Trajectories applied should include regulation (moving to a new, feasible set point) and tracking. Repeat runs are a must to see how the adaption influences future performance (use the adaptive parameters from the previous run as the initial conditions for next run). Just like in homeworks, attention should be paid to highlighting how the static controller fails to perform under incorrect parameters estimates. Otherwise, the //Final Deliverable// assignment item should cover what's needed.
 ====== References ======
 ----------------------
-No references given.  This project is self-contained given the lecture notes and the tips.
+No references given.  This project is self-contained given the lecture notes and the tips. The Hovakimyan and the Lavretsky and Wise references also work.
 ------