FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim

Vincent Andrew Akpan; Dimitrios Chasapis; George Dimitriou Hassapis

doi:doi:10.11648/j.ajesa.20220901.13

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FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim

Vincent Andrew Akpan, Dimitrios Chasapis, George Dimitriou Hassapis

Published in American Journal of Embedded Systems and Applications (Volume 9, Issue 1)

Received: 24 April 2022 Accepted: 9 June 2022 Published: 5 September 2022

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Abstract

Model predictive control (MPC) is an advanced receding horizon control strategy, for difficult multivariable control systems, that leads to constrained optimization problems which are solved online at each sampling time interval, and takes full advantage of the computational power available in modern control computer hardware for hard real-time constraint systems with short sampling time. However, nonlinear MPC (NMPC) attracts additional computational overload to satisfy nonlinear systems with hard real-time constraint and relatively short sampling time. In other to deploy NMPC for the control of nonlinear systems with hard real-time constraint and relatively short sampling time, a new model-based design (MBD) approach for the implementation of nonlinear MPC, called adaptive general predictive control (AGPC), on field programmable gate array (FPGA) for the auto-pilot control of a nonlinear F-16 aircraft is presented in this paper. The new MBD approach consists of four parts: (i) In the model identification part, the nonlinear F-16 aircraft model is approximated by a neural network autoregressive moving average with exogenous inputs (NNARMAX) model which is trained by an adaptive recursive least squares (ARLS) algorithm; (ii) In the adaptive control part, the nonlinear F-16 aircraft is controlled by a constrained neural network-based adaptive generalized predictive control (AGPC) algorithm; (iii) The third part is the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft; and (iv) The modeling, synthesis, verification and FPGA-in-the-loop hardware co-simulation (HW Co-Sim) of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft. The training and validation data for the neural network model identification are obtained from the open-loop simulation of first-principle nonlinear F-16 aircraft model. The online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft demonstrates the efficiency of the ARLS and the AGPC algorithms in tracking the desired reference trajectories of the nonlinear F-16 aircraft. The FPGA-in-the-loop hardware co-simulation of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft shows significant reduction in the computation time between the floating-point MATLAB AGPC and fixed-point C++ AGPC algorithms. Hence, the effort in this work has been directed towards reducing the computation time of the AGPC algorithm at each sampling time instant through modeling, synthesizing and mapping the AGPC algorithm to Virtex-5 FX70T ML507 FPGA embedded system development board via FPGA-in-the-loop hardware co-simulation verification which has been successfully achieved and validated.

Published in	American Journal of Embedded Systems and Applications (Volume 9, Issue 1)
DOI	10.11648/j.ajesa.20220901.13
Page(s)	6-36
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Adaptive Generalized Predictive Control, Adaptive Recursive Least Squares, Auto-pilot Control, Hardware Co-simulation, Nonlinear Model-Based Design, NMPC, Neural Networks, Nonlinear F-16 Aircraft

References

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Vincent Andrew Akpan, Dimitrios Chasapis, George Dimitriou Hassapis. (2022). FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim. American Journal of Embedded Systems and Applications, 9(1), 6-36. https://doi.org/10.11648/j.ajesa.20220901.13

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ACS Style

Vincent Andrew Akpan; Dimitrios Chasapis; George Dimitriou Hassapis. FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim. Am. J. Embed. Syst. Appl. 2022, 9(1), 6-36. doi: 10.11648/j.ajesa.20220901.13

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AMA Style

Vincent Andrew Akpan, Dimitrios Chasapis, George Dimitriou Hassapis. FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim. Am J Embed Syst Appl. 2022;9(1):6-36. doi: 10.11648/j.ajesa.20220901.13

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@article{10.11648/j.ajesa.20220901.13,
  author = {Vincent Andrew Akpan and Dimitrios Chasapis and George Dimitriou Hassapis},
  title = {FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {9},
  number = {1},
  pages = {6-36},
  doi = {10.11648/j.ajesa.20220901.13},
  url = {https://doi.org/10.11648/j.ajesa.20220901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20220901.13},
  abstract = {Model predictive control (MPC) is an advanced receding horizon control strategy, for difficult multivariable control systems, that leads to constrained optimization problems which are solved online at each sampling time interval, and takes full advantage of the computational power available in modern control computer hardware for hard real-time constraint systems with short sampling time. However, nonlinear MPC (NMPC) attracts additional computational overload to satisfy nonlinear systems with hard real-time constraint and relatively short sampling time. In other to deploy NMPC for the control of nonlinear systems with hard real-time constraint and relatively short sampling time, a new model-based design (MBD) approach for the implementation of nonlinear MPC, called adaptive general predictive control (AGPC), on field programmable gate array (FPGA) for the auto-pilot control of a nonlinear F-16 aircraft is presented in this paper. The new MBD approach consists of four parts: (i) In the model identification part, the nonlinear F-16 aircraft model is approximated by a neural network autoregressive moving average with exogenous inputs (NNARMAX) model which is trained by an adaptive recursive least squares (ARLS) algorithm; (ii) In the adaptive control part, the nonlinear F-16 aircraft is controlled by a constrained neural network-based adaptive generalized predictive control (AGPC) algorithm; (iii) The third part is the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft; and (iv) The modeling, synthesis, verification and FPGA-in-the-loop hardware co-simulation (HW Co-Sim) of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft. The training and validation data for the neural network model identification are obtained from the open-loop simulation of first-principle nonlinear F-16 aircraft model. The online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft demonstrates the efficiency of the ARLS and the AGPC algorithms in tracking the desired reference trajectories of the nonlinear F-16 aircraft. The FPGA-in-the-loop hardware co-simulation of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft shows significant reduction in the computation time between the floating-point MATLAB AGPC and fixed-point C++ AGPC algorithms. Hence, the effort in this work has been directed towards reducing the computation time of the AGPC algorithm at each sampling time instant through modeling, synthesizing and mapping the AGPC algorithm to Virtex-5 FX70T ML507 FPGA embedded system development board via FPGA-in-the-loop hardware co-simulation verification which has been successfully achieved and validated.},
 year = {2022}
}

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TY  - JOUR
T1  - FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim
AU  - Vincent Andrew Akpan
AU  - Dimitrios Chasapis
AU  - George Dimitriou Hassapis
Y1  - 2022/09/05
PY  - 2022
N1  - https://doi.org/10.11648/j.ajesa.20220901.13
DO  - 10.11648/j.ajesa.20220901.13
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 6
EP  - 36
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20220901.13
AB  - Model predictive control (MPC) is an advanced receding horizon control strategy, for difficult multivariable control systems, that leads to constrained optimization problems which are solved online at each sampling time interval, and takes full advantage of the computational power available in modern control computer hardware for hard real-time constraint systems with short sampling time. However, nonlinear MPC (NMPC) attracts additional computational overload to satisfy nonlinear systems with hard real-time constraint and relatively short sampling time. In other to deploy NMPC for the control of nonlinear systems with hard real-time constraint and relatively short sampling time, a new model-based design (MBD) approach for the implementation of nonlinear MPC, called adaptive general predictive control (AGPC), on field programmable gate array (FPGA) for the auto-pilot control of a nonlinear F-16 aircraft is presented in this paper. The new MBD approach consists of four parts: (i) In the model identification part, the nonlinear F-16 aircraft model is approximated by a neural network autoregressive moving average with exogenous inputs (NNARMAX) model which is trained by an adaptive recursive least squares (ARLS) algorithm; (ii) In the adaptive control part, the nonlinear F-16 aircraft is controlled by a constrained neural network-based adaptive generalized predictive control (AGPC) algorithm; (iii) The third part is the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft; and (iv) The modeling, synthesis, verification and FPGA-in-the-loop hardware co-simulation (HW Co-Sim) of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft. The training and validation data for the neural network model identification are obtained from the open-loop simulation of first-principle nonlinear F-16 aircraft model. The online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft demonstrates the efficiency of the ARLS and the AGPC algorithms in tracking the desired reference trajectories of the nonlinear F-16 aircraft. The FPGA-in-the-loop hardware co-simulation of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft shows significant reduction in the computation time between the floating-point MATLAB AGPC and fixed-point C++ AGPC algorithms. Hence, the effort in this work has been directed towards reducing the computation time of the AGPC algorithm at each sampling time instant through modeling, synthesizing and mapping the AGPC algorithm to Virtex-5 FX70T ML507 FPGA embedded system development board via FPGA-in-the-loop hardware co-simulation verification which has been successfully achieved and validated.
VL  - 9
IS  - 1
ER  -

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Author Information

Vincent Andrew Akpan

Department of Biomedical Technology, The Federal University of Technology, Akure, Nigeria
Dimitrios Chasapis

Barcelona Supercomputing Center, Group Sonar, Barcelona, Spain
George Dimitriou Hassapis

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

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Vincent Andrew Akpan, Dimitrios Chasapis, George Dimitriou Hassapis. (2022). FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim. American Journal of Embedded Systems and Applications, 9(1), 6-36. https://doi.org/10.11648/j.ajesa.20220901.13

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Vincent Andrew Akpan; Dimitrios Chasapis; George Dimitriou Hassapis. FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim. Am. J. Embed. Syst. Appl. 2022, 9(1), 6-36. doi: 10.11648/j.ajesa.20220901.13

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AMA Style

Vincent Andrew Akpan, Dimitrios Chasapis, George Dimitriou Hassapis. FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim. Am J Embed Syst Appl. 2022;9(1):6-36. doi: 10.11648/j.ajesa.20220901.13

Copy | Download

@article{10.11648/j.ajesa.20220901.13,
  author = {Vincent Andrew Akpan and Dimitrios Chasapis and George Dimitriou Hassapis},
  title = {FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {9},
  number = {1},
  pages = {6-36},
  doi = {10.11648/j.ajesa.20220901.13},
  url = {https://doi.org/10.11648/j.ajesa.20220901.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20220901.13},
  abstract = {Model predictive control (MPC) is an advanced receding horizon control strategy, for difficult multivariable control systems, that leads to constrained optimization problems which are solved online at each sampling time interval, and takes full advantage of the computational power available in modern control computer hardware for hard real-time constraint systems with short sampling time. However, nonlinear MPC (NMPC) attracts additional computational overload to satisfy nonlinear systems with hard real-time constraint and relatively short sampling time. In other to deploy NMPC for the control of nonlinear systems with hard real-time constraint and relatively short sampling time, a new model-based design (MBD) approach for the implementation of nonlinear MPC, called adaptive general predictive control (AGPC), on field programmable gate array (FPGA) for the auto-pilot control of a nonlinear F-16 aircraft is presented in this paper. The new MBD approach consists of four parts: (i) In the model identification part, the nonlinear F-16 aircraft model is approximated by a neural network autoregressive moving average with exogenous inputs (NNARMAX) model which is trained by an adaptive recursive least squares (ARLS) algorithm; (ii) In the adaptive control part, the nonlinear F-16 aircraft is controlled by a constrained neural network-based adaptive generalized predictive control (AGPC) algorithm; (iii) The third part is the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft; and (iv) The modeling, synthesis, verification and FPGA-in-the-loop hardware co-simulation (HW Co-Sim) of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft. The training and validation data for the neural network model identification are obtained from the open-loop simulation of first-principle nonlinear F-16 aircraft model. The online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft demonstrates the efficiency of the ARLS and the AGPC algorithms in tracking the desired reference trajectories of the nonlinear F-16 aircraft. The FPGA-in-the-loop hardware co-simulation of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft shows significant reduction in the computation time between the floating-point MATLAB AGPC and fixed-point C++ AGPC algorithms. Hence, the effort in this work has been directed towards reducing the computation time of the AGPC algorithm at each sampling time instant through modeling, synthesizing and mapping the AGPC algorithm to Virtex-5 FX70T ML507 FPGA embedded system development board via FPGA-in-the-loop hardware co-simulation verification which has been successfully achieved and validated.},
 year = {2022}
}

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TY  - JOUR
T1  - FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-pilot Control: Part 1 – Modeling, Synthesis, Verification and FPGA-in-Loop Co-Sim
AU  - Vincent Andrew Akpan
AU  - Dimitrios Chasapis
AU  - George Dimitriou Hassapis
Y1  - 2022/09/05
PY  - 2022
N1  - https://doi.org/10.11648/j.ajesa.20220901.13
DO  - 10.11648/j.ajesa.20220901.13
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 6
EP  - 36
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20220901.13
AB  - Model predictive control (MPC) is an advanced receding horizon control strategy, for difficult multivariable control systems, that leads to constrained optimization problems which are solved online at each sampling time interval, and takes full advantage of the computational power available in modern control computer hardware for hard real-time constraint systems with short sampling time. However, nonlinear MPC (NMPC) attracts additional computational overload to satisfy nonlinear systems with hard real-time constraint and relatively short sampling time. In other to deploy NMPC for the control of nonlinear systems with hard real-time constraint and relatively short sampling time, a new model-based design (MBD) approach for the implementation of nonlinear MPC, called adaptive general predictive control (AGPC), on field programmable gate array (FPGA) for the auto-pilot control of a nonlinear F-16 aircraft is presented in this paper. The new MBD approach consists of four parts: (i) In the model identification part, the nonlinear F-16 aircraft model is approximated by a neural network autoregressive moving average with exogenous inputs (NNARMAX) model which is trained by an adaptive recursive least squares (ARLS) algorithm; (ii) In the adaptive control part, the nonlinear F-16 aircraft is controlled by a constrained neural network-based adaptive generalized predictive control (AGPC) algorithm; (iii) The third part is the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft; and (iv) The modeling, synthesis, verification and FPGA-in-the-loop hardware co-simulation (HW Co-Sim) of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft. The training and validation data for the neural network model identification are obtained from the open-loop simulation of first-principle nonlinear F-16 aircraft model. The online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft demonstrates the efficiency of the ARLS and the AGPC algorithms in tracking the desired reference trajectories of the nonlinear F-16 aircraft. The FPGA-in-the-loop hardware co-simulation of the online closed-loop NNARMAX model identification and AGPC control of the nonlinear F-16 aircraft shows significant reduction in the computation time between the floating-point MATLAB AGPC and fixed-point C++ AGPC algorithms. Hence, the effort in this work has been directed towards reducing the computation time of the AGPC algorithm at each sampling time instant through modeling, synthesizing and mapping the AGPC algorithm to Virtex-5 FX70T ML507 FPGA embedded system development board via FPGA-in-the-loop hardware co-simulation verification which has been successfully achieved and validated.
VL  - 9
IS  - 1
ER  -

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