rebuilding site Thu Feb 5 09:34:04 EST 2026

Author: plancherb1

404.html

@@ -469,9 +469,9 @@ <h2>Latest</h2>
 
       <li><a href="/publication/phireman/">Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition</a></li>
 
-      <li><a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a></li>
+      <li><a href="/publication/fpgatinympc/">MPC Solver Hardware Generation Framework with Model-Specific Operation Fusion and Pruning</a></li>
 
-      <li><a href="/publication/hjcdik/">HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent</a></li>
+      <li><a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a></li>
 
   </ul>
 

authors/brianplancher/index.html

@@ -632,6 +632,10 @@ <h3>Latest</h3>
           <a href="/publication/phireman/">Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition</a>
         </li>
 
+        <li>
+          <a href="/publication/fpgatinympc/">MPC Solver Hardware Generation Framework with Model-Specific Operation Fusion and Pruning</a>
+        </li>
+        
         <li>
           <a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a>
         </li>

authors/brianplancher/index.xml

@@ -72,6 +72,13 @@
       <guid>https://a2r-lab.github.io/publication/phireman/</guid>
       <description>This paper introduces and formalizes the problem of Robust Task Networking Under Attrition (RTNUA), which extends connectivity maintenance in multi-robot systems to explicitly address proactive redundancy and attrition recovery. We introduce Physics-Informed Robust Employment of Multi-Agent Networks (ΦIREMAN), a topological algorithm leveraging physics-inspired potential fields to solve this problem. Through simulation across 25 problem configurations, ΦIREMAN consistently outperforms the DCCRS baseline, and on large-scale problems with up to 100 tasks and 500 drones, maintains &amp;gt;99.9% task uptime despite substantial attrition, demonstrating both effectiveness and scalability.</description>
     </item>
+    <item>
+      <title>MPC Solver Hardware Generation Framework with Model-Specific Operation Fusion and Pruning</title>
+      <link>https://a2r-lab.github.io/publication/fpgatinympc/</link>
+      <pubDate>Mon, 01 Dec 2025 00:00:00 +0000</pubDate>
+      <guid>https://a2r-lab.github.io/publication/fpgatinympc/</guid>
+      <description>We developed a flexible MPC solver hardware generation framework which includes a parameterized and programmable vector architecture template that accommodates instruction-level and data-level parallelism in vector and matrix functional units, and a model-specific fused architecture. Implementation of the proposed processor on the Ultra96 platform achieves up to a 9.73x speedup compared to existing generic solutions on MCUs. Moreover, end-to-end performance tests reveal that this speedup reduces the overall control error by 25.96%.</description>
+    </item>
     <item>
       <title>Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</title>
       <link>https://a2r-lab.github.io/publication/admmslack/</link>