Fix typo

_i18n/en/_projects/uav_lqr.md

@@ -1,7 +1,5 @@
-Executive: **Barış Ata** 
-Researcher: Mashar Cenk Gençal
+**Barış Ata** (Executive), Mashar Cenk Gençal
 
-**Abstract**
-Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, there is one main difficulty that plays a significant role in the manner of determining the gain for the control signal: choosing appropriate weighting matrices. The selection of these matrices that directly affect the controller performance is generally performed by trial and error, which is laborious and time-consuming. Accordingly, various optimization algorithms have been utilized to determine the weighting matrices of the LQR controller. In this paper, the weighting matrices of the designed LQR controller were obtained using Standard Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Grey Wolf Optimization algorithms. The obtained weighting matrices of the LQR controller were tested on an unmanned aerial vehicle simulation, and the performances of optimization algorithms were presented comparatively.
+**Abstract:** Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, there is one main difficulty that plays a significant role in the manner of determining the gain for the control signal: choosing appropriate weighting matrices. The selection of these matrices that directly affect the controller performance is generally performed by trial and error, which is laborious and time-consuming. Accordingly, various optimization algorithms have been utilized to determine the weighting matrices of the LQR controller. In this paper, the weighting matrices of the designed LQR controller were obtained using Standard Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Grey Wolf Optimization algorithms. The obtained weighting matrices of the LQR controller were tested on an unmanned aerial vehicle simulation, and the performances of optimization algorithms were presented comparatively.
 
 This project was supported by the Research Fund of Cukurova University. Project Number: FBA-2022-14643.

_i18n/tr/_projects/uav_lqr.md

@@ -1,5 +1,4 @@
-Yürütücü: **Barış Ata**
-Araştırmacı: Mashar Cenk Gençal
+**Barış Ata** (Yürütücü), Mashar Cenk Gençal
 
 **Özet**: Lineer Kuadratik Regülatör (Linear Quadratic Regulator - LQR), insansız hava araçlarının kontrolünde kullanılan en yaygın yöntemlerden biridir. LQR denetleyicileri, kolay uygulanabilirlikleri ve yüksek performans yapıları gibi avantajlar nedeniyle genellikle doğrusal ve doğrusal olmayan sistemlerin kontrolünde kullanılırlar. Ancak, kontrol sinyali için kazançların belirlenme şeklinde önemli bir rol oynayan temel bir zorluk bulunmaktadır: uygun ağırlık matrislerini seçmek. Denetleyici performansını doğrudan etkileyen bu matrislerin seçimi genellikle deneme yanılma yoluyla gerçekleştirilir, bu da zahmetli ve zaman alıcı bir süreçtir. Bu bağlamda, LQR denetleyicisinin ağırlık matrislerini belirlemek için çeşitli optimizasyon algoritmaları kullanılmıştır. Bu çalışmada, tasarlanan LQR denetleyicisinin ağırlık matrisleri, Standart Genetik Algoritma, Diferansiyel Evrim, Parçacık Sürü Optimizasyonu ve Gri Kurt Optimizasyonu algoritmaları kullanılarak elde edilmiştir. Elde edilen LQR denetleyicisi ağırlık matrisleri bir insansız hava aracı simülasyonunda test edilmiş ve optimizasyon algoritmalarının performansları karşılaştırmalı olarak sunulmuştur.
 

_pages/projects.md

@@ -18,15 +18,30 @@ horizontal: false
   <h2 class="category">{% t localized_category %}</h2>
   {%- assign categorized_projects = site.projects | where: "category", category -%}
   {%- assign sorted_projects = categorized_projects | sort: "importance" %}
-  <div class="table-responsive">
-    <table class="table table-sm table-borderless">
      {%- for project in sorted_projects -%}
       <ul>
           <li> <a href="{{ project.url | prepend: site.baseurl }}">{% t project.title %}</a> - {% t project.description %} </li>
       </ul>
       {%- endfor %}
-    </table>
-  </div>
   {% endfor %}
+{%- else -%}
+<!-- Display projects without categories -->
+  {%- assign sorted_projects = site.projects | sort: "importance" -%}
+  <!-- Generate cards for each project -->
+  {% if page.horizontal -%}
+  <div class="container">
+    <div class="row row-cols-2">
+    {%- for project in sorted_projects -%}
+      {% include projects_horizontal.html %}
+    {%- endfor %}
+    </div>
+  </div>
+  {%- else -%}
+  <div class="grid">
+    {%- for project in sorted_projects -%}
+      {% include projects.html %}
+    {%- endfor %}
+  </div>
+  {%- endif -%}
 {%- endif -%}
 </div>