FPGA Implementation of Artificial Neural Networks for Model Predictive Control
Traditionally, the real-time implementation of Model Predictive Control (MPC) has been limited by processing and storage requirements. Recently, the idea of using Artificial Neural Networks (ANN) to approximate MPC control laws, including implementations on Field Programmable Gate Array (FPGA), has...
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| Vydané v: | 2024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA) s. 1 - 6 |
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20.10.2024
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| Abstract | Traditionally, the real-time implementation of Model Predictive Control (MPC) has been limited by processing and storage requirements. Recently, the idea of using Artificial Neural Networks (ANN) to approximate MPC control laws, including implementations on Field Programmable Gate Array (FPGA), has been explored. This work presents a complete design flow from software controller to hardware implementation, utilizing Keras and QKeras for ANN design and quantization and HLS4ML with the AMD-Xilinx Design Suite for FPGA implementation. The evaluation and analysis conducted provides insights into the trade-offs involved in the proposed workflow. Experimental results are validated on a PYNQ-Z1 board, achieving latencies of less than one microsecond in the case study, demonstrating a hardware precision comparable to traditional MPC methods. |
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| AbstractList | Traditionally, the real-time implementation of Model Predictive Control (MPC) has been limited by processing and storage requirements. Recently, the idea of using Artificial Neural Networks (ANN) to approximate MPC control laws, including implementations on Field Programmable Gate Array (FPGA), has been explored. This work presents a complete design flow from software controller to hardware implementation, utilizing Keras and QKeras for ANN design and quantization and HLS4ML with the AMD-Xilinx Design Suite for FPGA implementation. The evaluation and analysis conducted provides insights into the trade-offs involved in the proposed workflow. Experimental results are validated on a PYNQ-Z1 board, achieving latencies of less than one microsecond in the case study, demonstrating a hardware precision comparable to traditional MPC methods. |
| Author | Silva, CaEsar Carvajal, Gonzalo Aguero, Juan C. VaAsquez, Juan J. CortaEs, Alfonso |
| Author_xml | – sequence: 1 givenname: Juan J. surname: VaAsquez fullname: VaAsquez, Juan J. email: juan.vasquezca@sansano.usm.cl organization: Universidad Técnica Federico Santa María,Departamento de Electrónica – sequence: 2 givenname: Alfonso surname: CortaEs fullname: CortaEs, Alfonso email: alfonso.cortes@sansano.usm.cl organization: Universidad Técnica Federico Santa María,Departamento de Electrónica – sequence: 3 givenname: CaEsar surname: Silva fullname: Silva, CaEsar email: cesar.silva@usm.cl organization: Universidad Técnica Federico Santa María,Departamento de Electrónica – sequence: 4 givenname: Juan C. surname: Aguero fullname: Aguero, Juan C. email: juan.aguero@usm.cl organization: Universidad Técnica Federico Santa María,Departamento de Electrónica – sequence: 5 givenname: Gonzalo surname: Carvajal fullname: Carvajal, Gonzalo email: gonzalo.carvajalb@usm.cl organization: Universidad Técnica Federico Santa María,Departamento de Electrónica |
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| PublicationTitle | 2024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA) |
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| Snippet | Traditionally, the real-time implementation of Model Predictive Control (MPC) has been limited by processing and storage requirements. Recently, the idea of... |
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| SubjectTerms | artificial neural network Artificial neural networks field programmable gate array Field programmable gate arrays Hardware hls4ml Logic gates model predictive control Predictive control Predictive models quantization Quantization (signal) Real-time systems Software |
| Title | FPGA Implementation of Artificial Neural Networks for Model Predictive Control |
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