Hybrid control strategy using iterative learning and state-dependent Riccati equation for enhanced precision in parallel delta robots

• controller design for parallel delta robot. • Iterative Learning Control for parallel delta robot. • The SDRE/SDDRE Control Design to improve the optimality and stability of parallel delta robot. • SDRE/SDDRE augmented by iterative learning. • ILC with PD control to make high performance and error reduction. Parallel delta robots are widely used in industrial automation for high-speed, high-precision tasks such as pick-and-place operations. However, traditional control methods like Proportional-Derivative (PD) controllers often struggle to handle the nonlinear dynamics of these systems, leading to suboptimal performance and stability issues. This study proposes an innovative control framework that combines a PD-type controller with a State-Dependent Riccati Equation (SDRE) approach, augmented by Iterative Learning Control (ILC), to address these challenges. The SDRE framework dynamically adjusts system parameters to enhance stability margins, while the ILC component iteratively refines control inputs based on error patterns, achieving progressively improved precision. The proposed method was validated through both simulations and experimental testing on a parallel delta robot, focusing on regulation and tracking tasks. The experimental setup included a mechanical manipulator controlled via the hybrid SDRE-ILC framework, with performance benchmarks compared to traditional PD controllers. Results demonstrated an 85 % reduction in system errors across successive iterations, confirming the efficacy of the approach. The hybrid framework not only improved stability and tracking accuracy but also provided adaptability to dynamic conditions. This study offers a robust and scalable solution for enhancing the performance of parallel delta robots, particularly in applications demanding high precision and reliability, such as assembly lines, packaging, and surgical robotics. The findings emphasize the potential of integrating advanced control strategies to overcome the inherent limitations of conventional methods, paving the way for more efficient industrial automation systems.