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FPGA Implementation of Neural Network-Based AGPC for Nonlinear F-16 Aircraft Auto-Pilot Control: Part 2 – Implementation of Embedded PowerPC™440 with AGPC

The computational overload involved in the implementation of nonlinear model predictive control (NMPC) cannot be over-emphasized due to the double optimizations involved for the nonlinear model identification phase as well as the NMPC controller design phase. The computational burden becomes more time-critical for constrained multivariable control systems with relatively short sampling time. This paper presents a novel and comprehensive model-based design (MBD) approach for real-time closed-loop implementation of a version of NMPC referred here as adaptive generalized predictive control algorithmic co-processor (AGPC algorithmic co-processor) integrated with a well-designed embedded PowerPC™440 processor core on Virtex-5 FX70T ML507 FPGA (field programmable gate array) for the auto-pilot control of a nonlinear F-16 aircraft with a sampling time of 0.5 second. The result shows that the real-time closed-loop implementation of the neural network identification and the AGPC algorithms on the FPGA with embedded PowerPC™440 processor combined with an AGPC algorithmic co-processor at each sampling time is accomplished within 0.16502 microseconds (μs) when compared to the 6.1048 seconds obtained using Intel® Core™2 CPU personal computer for the control of the auto-pilot unit of a nonlinear F-16 aircraft. The demonstrated and validated model-based FPGA implementation techniques can be adapted and deployed for the real-time control of multivariable control systems having relatively short sampling time. The computation time and FPGA device utilization at each stage of the MBD implementation are also presented.

AGPC Algorithmic Co-processor, Embedded PowerPC™440 Processor, FPGA, Model-Based Design (MBD), Nonlinear Model Predictive Control (NMPC), Nonlinear F-16 Aircraft, NMPC, Virtex-5 FX70T ML507 FPGA

APA Style

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 2 – Implementation of Embedded PowerPC™440 with AGPC. American Journal of Embedded Systems and Applications, 9(2), 37-65. https://doi.org/10.11648/j.ajesa.20220902.11

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 2 – Implementation of Embedded PowerPC™440 with AGPC. Am. J. Embed. Syst. Appl. 2022, 9(2), 37-65. doi: 10.11648/j.ajesa.20220902.11

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 2 – Implementation of Embedded PowerPC™440 with AGPC. Am J Embed Syst Appl. 2022;9(2):37-65. doi: 10.11648/j.ajesa.20220902.11

Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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