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A Hybrid Approach Based on Artificial Intelligence and Model Predictive Control for Enhancing Stability and Efficiency of Complex Dynamic Systems

Journal of Robotics and Control (JRC) · Vol. 6 No. 5 (2025) · hlm. 2426–2435

DOI
10.18196/jrc.v6i5.28069
ISSN
2715-5072, 2715-5056
Terbit
2025-10-19
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Stability and operational performance in uncertain and time-varying nonlinear dynamic systems, such as robotic manipulators and autonomous vehicles, is an important problem in modern nonlinear dynamical systems. This study presents a hybrid model that integrates Long Short-Term Memory (LSTM)-based artificial intelligence (AI) to identify adaptive system changes with Model Predictive Control (MPC), provided with the help of an autoencoder that leads to dimensionality reduction. The proposed AI-MPC system offers conditional stability and real-time feasibility (guaranteed by a broad Lyapunov-based theoretical framework with a clear definition of AI in the stability guarantees). Among the contributions to research are scalable and computationally efficient control architecture that adaptively models complex system dynamics, minimizes control computation time, and remains robust to disturbances and uncertainties. It is also known that the implemented framework can be useful to a wide range of simulated and experimental case studies such as robotic arms and autonomous vehicle platforms, the framework has offered improved tracking performances with a root mean square error (RMSE) by more than 58 times compared to conventional MPC and PID controllers, uses less energy by up to 50 times less and disturbances with an order of magnitude (more than 20 times) reduced. The control iterations use 30-60 milliseconds that is why it is better to use it in real time. The extension of this AI-MPC system is that the control systems can be predictive in an unpredictable and nonlinear environment that may represent the interface to resource-constrained and safety-critical systems.

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  • Imam Ja’afar Al-Sadiq University
  • University of Anbar
  • Al-Farahidi University
  • University of Hillah
  • Baghdad College of Economic Sciences University
  • Prince Sattam Bin Abdulaziz University
  • Asia Pacific University of Technology & Innovation
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Rekan penulisOrganisasiArtikel bersama
Israa Al_BarazanchiAfiliasi tidak diketahui3
Amer IbrahimUnited Arab Emirates University3
Poh Soon JosephNgINTI International University3
Ali Najim AbdullahUniversity of Diyala2
Ali Talib JawadUniversity of Hillah2
Dalael Saad Abdul-ZahraUniversity of Hillah2
Saadaldeen Rashid AhmedAfiliasi tidak diketahui2
Ravi SekharAfiliasi tidak diketahui2
Jamal Fadhil TawfeqAl-Farahidi University2
Hala Shaker MehdyMustansiriyah University1
Nariman Jabbar QasimAl-Esraa University1
Haider Hadi AbbasAl Mansour University College1
Aws Hamed HamadMinistry of Higher Education and Scientific Research1
Adnan Yousif DawodUniversity of Kirkuk1
Mohammed Fakhrulddin AbdulqaderBaghdad College of Economic Sciences University1
Yitong NiuAnyang University1
Ahmed Mohammed MerzaUniversity of Warith Al-Anbiyaa1
Suhad Ibraheem KadhemBaghdad College of Economic Sciences University1
Hissah AlmutairiPrince Sattam Bin Abdulaziz University1
Abdullah AlqahtaniPrince Sattam Bin Abdulaziz University1
Zinah S. JabbarImam Ja’afar Al-Sadiq University1
Ahmed Lateef KhalafImam Ja’afar Al-Sadiq University1
Thuria Saad ZnadNahrain University1
Saif Saad HameedUniversity of Anbar1
Priyanka Tupe-WaghmareSymbiosis International University1
Rafah Kareem MahmoodAfiliasi tidak diketahui1
Ans Ibrahim MahameedAfiliasi tidak diketahui1
Noor Q. LateefAfiliasi tidak diketahui1
Hasanain M. JasimAfiliasi tidak diketahui1
Taha A. TahaAfiliasi tidak diketahui1
Abdulghafor Mohammed HashimAfiliasi tidak diketahui1
Ziyaad H. SalehAfiliasi tidak diketahui1
Basim Ghalib MejbelAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A Hybrid Approach Based on Artificial Intelligence and Model Predictive Control for Enhancing Stability and Efficiency of Complex Dynamic Systems</dc:title>
	<dc:creator>Nadweh, Safwan</dc:creator>
	<dc:creator>Al Sayed, Intisar A.M.</dc:creator>
	<dc:creator>Abdulbaqi, Azmi Shawkat</dc:creator>
	<dc:creator>Essa, Reyad Omran</dc:creator>
	<dc:creator>Sham, Rohana</dc:creator>
	<dc:creator>Gheni, Hassan Muwafaq</dc:creator>
	<dc:creator>Radhi, Ahmed Dheyaa</dc:creator>
	<dc:subject xml:lang="en-US">Dimensionality Reduction</dc:subject>
	<dc:subject xml:lang="en-US">Dynamic Systems</dc:subject>
	<dc:subject xml:lang="en-US">Hybrid AI-MPC Framework</dc:subject>
	<dc:subject xml:lang="en-US">LSTM Networks</dc:subject>
	<dc:subject xml:lang="en-US">Lyapunov Stability</dc:subject>
	<dc:subject xml:lang="en-US">Model Predictive Control (MPC)</dc:subject>
	<dc:subject xml:lang="en-US">Real-Time Control</dc:subject>
	<dc:subject xml:lang="en-US">Stability Enhancement</dc:subject>
	<dc:subject xml:lang="en-US">System Efficiency</dc:subject>
	<dc:description xml:lang="en-US">Stability and operational performance in uncertain and time-varying nonlinear dynamic systems, such as robotic manipulators and autonomous vehicles, is an important problem in modern nonlinear dynamical systems. This study presents a hybrid model that integrates Long Short-Term Memory (LSTM)-based artificial intelligence (AI) to identify adaptive system changes with Model Predictive Control (MPC), provided with the help of an autoencoder that leads to dimensionality reduction. The proposed AI-MPC system offers conditional stability and real-time feasibility (guaranteed by a broad Lyapunov-based theoretical framework with a clear definition of AI in the stability guarantees). Among the contributions to research are scalable and computationally efficient control architecture that adaptively models complex system dynamics, minimizes control computation time, and remains robust to disturbances and uncertainties. It is also known that the implemented framework can be useful to a wide range of simulated and experimental case studies such as robotic arms and autonomous vehicle platforms, the framework has offered improved tracking performances with a root mean square error (RMSE) by more than 58 times compared to conventional MPC and PID controllers, uses less energy by up to 50 times less and disturbances with an order of magnitude (more than 20 times) reduced. The control iterations use 30-60 milliseconds that is why it is better to use it in real time. The extension of this AI-MPC system is that the control systems can be predictive in an unpredictable and nonlinear environment that may represent the interface to resource-constrained and safety-critical systems.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-10-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/28069</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i5.28069</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 5 (2025); 2426-2435</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/28069/11951</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Safwan Nadweh, Intisar A.M. Al Sayed, Azmi Shawkat Abdulbaqi, Reyad Omran Essa, Rohana Sham, Hassan Muwafaq Gheni, Ahmed Dheyaa Radhi</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>