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<p>This paper presents a comprehensive study on the implementation and optimization of proportional–integral–derivative (PID) controllers using bio-inspired algorithms on a system-on-chip field-programmable gate array device, specifically the Zynq 7020 present on the Pynq Z2 board, with 11-bit, 16-bit, and 23-bit floating-point precision. The methodology begins with a theoretical overview of PID controllers and proceeds with the application of various bio-inspired optimization algorithms, including artificial bee colony, differential evolution, particle swarm optimization, and moth flame optimization, for the optimal tuning of PID parameters. The statistical hypothesis test is conducted to compare the performance of these algorithms, using metrics such as overshoot, settling time, and steady-state error to assess the effectiveness of the PID controller implementation, both in Hardware-in-the-Loop simulations and physical robot tests. The optimized PID controller was implemented on the physical EVA robot, demonstrating the practical applicability and effectiveness of the proposed approach. These findings highlight the potential of bio-inspired algorithms in enhancing the performance of embedded control systems and provide insights into their use for optimizing PID controllers in real-time applications.</p>

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Integrating Bio-inspired Optimization with FPGA for Enhanced PID Control in Robotics

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Mario Andrés Pastrana Triana,
  • Mateus Souza Santana,
  • Jose Alfredo Mendoza Peñaloza,
  • Luiz Henrique Nunes de Oliveira,
  • Daniel M. Muñoz

摘要

This paper presents a comprehensive study on the implementation and optimization of proportional–integral–derivative (PID) controllers using bio-inspired algorithms on a system-on-chip field-programmable gate array device, specifically the Zynq 7020 present on the Pynq Z2 board, with 11-bit, 16-bit, and 23-bit floating-point precision. The methodology begins with a theoretical overview of PID controllers and proceeds with the application of various bio-inspired optimization algorithms, including artificial bee colony, differential evolution, particle swarm optimization, and moth flame optimization, for the optimal tuning of PID parameters. The statistical hypothesis test is conducted to compare the performance of these algorithms, using metrics such as overshoot, settling time, and steady-state error to assess the effectiveness of the PID controller implementation, both in Hardware-in-the-Loop simulations and physical robot tests. The optimized PID controller was implemented on the physical EVA robot, demonstrating the practical applicability and effectiveness of the proposed approach. These findings highlight the potential of bio-inspired algorithms in enhancing the performance of embedded control systems and provide insights into their use for optimizing PID controllers in real-time applications.