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Purpose <p>This paper aims to design and implement a dual-axis inertially stabilized platform (DISP) for high-precision target tracking from moving vehicles. The main objectives are to isolate a payload from base disturbances through active gimbal control and to address the challenge of nonlinear disturbances and parameter variations by developing and comparing three distinct control strategies: a conventional Proportional-Integral (PI) controller, an optimal Linear-Quadratic Regulator (LQR), and a proposed Nonlinear Fuzzy PI (NLFPI) controller.</p> Methods <p>The DISP was developed with active pitch and yaw gimbals. The three control strategies were implemented and rigorously evaluated. The proposed NLFPI controller introduces three specific novelties: (i) a minimal 3 × 3 rule-base that achieves coupling rejection ratios exceeding 97% despite a small inertia ratio of 0.0125; (ii) computational efficiency verified on an 8-bit Arduino Nano (ATmega328P) using only 27% flash and 25% SRAM with 780&#xa0;Hz loop frequency; and (iii) asymmetric Gaussian membership functions tuned to the plant’s physical disturbance spectrum (0.1–10&#xa0;Hz). The evaluation was conducted through both simulations and physical experiments on an Arduino-based testbed. Performance metrics included disturbance rejection, sensor noise attenuation, and robustness to ± 50% variations in payload and motor parameters.</p> Results <p>The results revealed a clear performance hierarchy. The proposed NLFPI controller demonstrated superior disturbance rejection, achieving a 98% reduction in peak Line-of-Sight (LOS) error compared to the LQR and a 62% reduction compared to the PI controller under a challenging deterministic disturbance. The large peak error in the LQR response is attributed to actuator saturation, which the NLFPI’s adaptive fuzzy logic avoids.</p> Conclusion <p>The study confirms that the NLFPI controller provides a highly robust and effective solution for dual-axis inertial stabilization, significantly enhancing tracking precision in moving vehicles by dynamically adapting to nonlinear disturbances and parameter variations.</p>

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Experimental Implementation and Validation of Enhanced Dual-Axis Inertial Stabilization Using PI and Nonlinear Fuzzy PI Controller Approaches

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Karim Ahmed,
  • Wael Galal Ata,
  • Amgad Mohamed Salem,
  • Amr Roshdy

摘要

Purpose

This paper aims to design and implement a dual-axis inertially stabilized platform (DISP) for high-precision target tracking from moving vehicles. The main objectives are to isolate a payload from base disturbances through active gimbal control and to address the challenge of nonlinear disturbances and parameter variations by developing and comparing three distinct control strategies: a conventional Proportional-Integral (PI) controller, an optimal Linear-Quadratic Regulator (LQR), and a proposed Nonlinear Fuzzy PI (NLFPI) controller.

Methods

The DISP was developed with active pitch and yaw gimbals. The three control strategies were implemented and rigorously evaluated. The proposed NLFPI controller introduces three specific novelties: (i) a minimal 3 × 3 rule-base that achieves coupling rejection ratios exceeding 97% despite a small inertia ratio of 0.0125; (ii) computational efficiency verified on an 8-bit Arduino Nano (ATmega328P) using only 27% flash and 25% SRAM with 780 Hz loop frequency; and (iii) asymmetric Gaussian membership functions tuned to the plant’s physical disturbance spectrum (0.1–10 Hz). The evaluation was conducted through both simulations and physical experiments on an Arduino-based testbed. Performance metrics included disturbance rejection, sensor noise attenuation, and robustness to ± 50% variations in payload and motor parameters.

Results

The results revealed a clear performance hierarchy. The proposed NLFPI controller demonstrated superior disturbance rejection, achieving a 98% reduction in peak Line-of-Sight (LOS) error compared to the LQR and a 62% reduction compared to the PI controller under a challenging deterministic disturbance. The large peak error in the LQR response is attributed to actuator saturation, which the NLFPI’s adaptive fuzzy logic avoids.

Conclusion

The study confirms that the NLFPI controller provides a highly robust and effective solution for dual-axis inertial stabilization, significantly enhancing tracking precision in moving vehicles by dynamically adapting to nonlinear disturbances and parameter variations.