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ece6554:project_planarheli [2023/03/13 11:19] – [Adaptive Controller] classesece6554:project_planarheli [2024/08/20 21:38] (current) – external edit 127.0.0.1
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 ==== Equations of Motion ==== ==== Equations of Motion ====
-Defining $q = (x, y)^T$ to be the center of mass of the ducted fan, and $\theta$ to be the orientation of the ducted fan,+Defining $q = (x, y)^T$ to be the center of mass of the ducted fan, and $\theta$ to be the orientation of the ducted fan, the most general form of the equations is 
 +\begin{equation} 
 +\begin{split} 
 +  m \ddot q & = -d \dot q + R(\theta) \left[ \begin{matrix} 0 & 0 \\ 1 & 1 \end{matrix} \right] \vec f - m \vec g \\ 
 +  J \ddot \theta & = r \left[ \begin{matrix} 1 & -1 \end{matrix} \right] \vec f 
 +\end{split} 
 +\end{equation} 
 +while a more specialized form is
 \begin{equation} \begin{equation}
 \begin{split} \begin{split}
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 \end{equation} \end{equation}
 where the force vector $f$ is in the body frame of the ducted fan, generated from the two fans' thrust.  Each coordinate of $f \in \mathbb{R}^2$ can be independently controlled (but can never really go negative due to the nature of the fan blades used). where the force vector $f$ is in the body frame of the ducted fan, generated from the two fans' thrust.  Each coordinate of $f \in \mathbb{R}^2$ can be independently controlled (but can never really go negative due to the nature of the fan blades used).
-Though not used explicitly in the equation above, let $R$ be a planar rotation matrix for which $e_2$ is the vector generated from the second column of the rotation matrix,+Regarding the functions of $\theta$ used in the equations of motion, $R$ is a planar rotation matrix for which $e_2$ is the vector generated from the second column of the rotation matrix,
 \begin{equation} \begin{equation}
   R(\theta) = \left[ \begin{matrix} \cos(\theta) & -\sin(\theta) \\ \sin(\theta) & \cos(\theta) \end{matrix}  \right]   R(\theta) = \left[ \begin{matrix} \cos(\theta) & -\sin(\theta) \\ \sin(\theta) & \cos(\theta) \end{matrix}  \right]
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 As noted earlier, typical fans cannot produce negative thrust forces, thus the coordinates of $f$ are limited to non-negative values. When designing the baseline trajectories, try to have them results in forces not exceeding six times the baseline force needed to hover under ideal circumstances.  The actual adaptive, closed-loop design might violate this upper limit, but hopefully not by too much (say, less than ten times). It is best to design trajectories that do not hit these limits in the closed-loop. More aggressive trajectories is best saved for future self-study. As noted earlier, typical fans cannot produce negative thrust forces, thus the coordinates of $f$ are limited to non-negative values. When designing the baseline trajectories, try to have them results in forces not exceeding six times the baseline force needed to hover under ideal circumstances.  The actual adaptive, closed-loop design might violate this upper limit, but hopefully not by too much (say, less than ten times). It is best to design trajectories that do not hit these limits in the closed-loop. More aggressive trajectories is best saved for future self-study.
  
 +===== Implementation =====
  
 +Functional code stubs for the implementation are provided in the {{ ECE6554:projects:birotor.zip | birotor zipfile}}.  They implement a constant control signal that most definitely fails to do the job, but provide enough structure to complete the project.  Comments in the code should help to realize the necessary improvements.
 ====== Activities ====== ====== Activities ======
 ----------------------- -----------------------
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 ===== Step 3: Adaptive Controller ===== ===== Step 3: Adaptive Controller =====
  
-Once the system has been derived and controlled, it should look like a linear system with nonlinear terms in the span of the input space.  We have covered such a system. Augment the existing controller with a model-reference adaptive controller. Show that it behaves well under the same random change of parameters from Step #1. +Once the system has been derived and controlled, it should look like a linear system with nonlinear terms in the span of the input space.  We have covered such a system. Augment the existing controller with a model-reference adaptive controller. Show that it behaves well under the same random change of parameters from Step #1. Demonstrate improved performance relative to the static nonlinear controller design.  If possible, compare performance of linear vs nonlinear adaptive controllers (best to quantify).  
- +
- +
- +
-===== Step 3: Nonlinear Control Lyapunov Approach [Not Done] ===== +
- +
-Do not work out this part.+
  
-==== The PWMN Controller ====+//Note:// One thing to be careful about is the initial transient experienced by the adaptive controllers.  When comparing, it is usually best to separate the transient time period from the non-transient period. Also, one could argue that the most important comparison is with the repeated run since that would be the normal use case for an adaptive controller. Thus, limiting comparison to the repeat run outcomes is acceptable and may even be preferred for simplicity of data collected and analyzed.
  
-==== Performance Reference Adaptive Control ===== 
  
 ====== Report Considerations ======= ====== Report Considerations =======
ece6554/project_planarheli.1678720770.txt.gz · Last modified: 2024/08/20 21:38 (external edit)