# Control of DC Motors to Guide Unmanned Underwater Vehicles
## 4 Discussion  
Formerly, the indirect self-tuner without process zero cancellation for minimum phase plants had superior performance (measured by mean and standard deviation) compared to the three other methods investigated, although a notable mention must go to the direct self-tuner with filtering where all process zeros are cancelled. This research qualitatively reveals deterministic artificial intelligence produces superior performance categorized by the second largest initial transient, but the transient was not repeated with each switch of the square wave like all other methods investigated. The indirect self-tuner without process zero cancellation (minimum phase plant) yielded a smaller initial transient, but the transient reoccurred with each switch of the square wave.  
This research quantitatively reveals that deterministic artificial intelligence yields 4.8% lower mean and 211% lower standard deviation of tracking errors as compared to the second-best method investigated (indirect self-tuner without process zero cancellation and minimum phase plant). The improved performance cannot be attributed to superior estimation. Coefficient estimation was merely on par with the best alternative methods (some coefficients were estimated more accurately, others less accurately). Instead, the superior performance must be attributed to the method itself.  
Considering the demonstrated ability to follow a challenging square wave closely in steady-state without repeated overshoots and settling after switching, future research should focus on elimination of the sole transient (on initial startup) to further improve results. Additional future research should include evaluation of the limits of uncertainty that can be autonomously counted by deterministic artificial intelligence (D.A.I.). Like the nonlinear adaptive control methods, D.A.I. is structured precisely to modify behavior in response to uncertainty and/or changes in operating conditions. Therefore, a direct comparison of each method's ability to counter these deleterious effects would be very interesting research.  
This research received no external funding.  
**Data Availability Statement:** Data supporting reported results can be provided by contacting the author.  
**Acknowledgments:** The inspiring genesis of this work was the following manuscript ([[https://www.mdpi.com/2077-1312/8/8/578](https://www.mdpi.com/2077-1312/8/8/578)]([https://www.mdpi.com/2077-1312/8/8/578](https://www.mdpi.com/2077-1312/8/8/578)), accessed on 25 February 2021) whose research was funded by grant money from the U.S. Office of Naval Research consortium for robotics and unmanned systems education and research described at [[https://calhoun.nps.edu/handle/10945/6991](https://calhoun.nps.edu/handle/10945/6991)]([https://calhoun.nps.edu/handle/10945/6991](https://calhoun.nps.edu/handle/10945/6991)), accessed on 25 February 2021. The grants did not cover the costs to publish in open access, instead the author self-funds open access publication to maximize benefit to mankind.  
**Conflicts of Interest:** The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Tracking Error 1**} \\ \cline{2-3}  & **Mean, \(\mu\)** & **Standard Deviation, \(\sigma\)** \\ \hline Indirect self-tuner without process zero cancellation (minimum phase plant) & 0.023534 & 0.58929 \\ Indirect self-tuner without process zero cancellation (non-minimum phase plant) & 23.2272 & 109.7158 \\ Direct self-tuner with filtering (all process zeros cancelled) & \(-\)0.35445 & 2.9984 1 \\ Deterministic artificial intelligence (D.A.I.) & \(\mathbf{-0.012239}\) & \(\mathbf{0.1895}\) \\ \hline \hline \end{tabular}  
* Tracking error comparison of proposed D.A.I. method to formerly compared methods in [11].  
\end{table}
Table 2: Tracking errors’ figures of merit (means and standard deviations) for each technique.