Advanced Iterative Learning Control Strategies in Robotic Manipulators

<p dir="ltr">This thesis investigates advanced Iterative Learning Control (ILC) strategies aimed at enhancing the performance, robustness, and energy efficiency of robotic manipulators and other systems performing repetitive tasks. Traditional ILC methods, while effective in certain contexts, face limitations such as the need for manual parameter tuning, sensitivity to external disturbances, and high energy consumption. These constraints can significantly reduce their applicability in dynamic environments, particularly in the context of modern industry, where flexibility, adaptability, and efficiency are paramount. To address these challenges, this research explores the integration of optimization techniques and adaptive mechanisms into ILC frameworks, resulting in a series of hybrid approaches that improve the overall performance and robustness of the control systems. The thesis introduces several novel ILC frameworks, including Particle Swarm Optimization-ILC (PSO-ILC), Weightless Swarm Algorithm-ILC (WSA-ILC), Genetic Algorithm-ILC (GA-ILC), and Elite Genetic Algorithm-ILC (EGA-ILC). These hybrid methods utilize the global search capabilities of optimization algorithms to dynamically adjust control parameters, such as learning gains and feedback gains, during the iterative learning process. This dynamic adjustment significantly enhances the tracking performance, convergence speed, and robustness of the ILC systems in the presence of model uncertainties and external disturbances. Additionally, this research proposes a 2D ILC approach for Linear Time-Varying (LTV) systems based on the Roesser model. This approach provides a systematic framework for capturing the dynamics of the ILC process in both time and iteration domains, making it particularly effective in dealing with iteration-varying disturbances and complex system dynamics. The thesis also introduces the ”Green” ILC method, which integrates ILC with gradient descent optimization to create an energy-efficient control strategy. By balancing the tracking error and control energy, Green ILC achieves significant reductions in energy consumption without compromising tracking accuracy, making it well-suited for applications where energy efficiency is critical. The effectiveness of these advanced ILC strategies is validated through extensive simulation studies on robotic manipulator models with various degrees of freedom and complex motion trajectories, such as line, circular, and spiral paths. The results demonstrate that the proposed hybrid ILC frameworks outperform traditional and adaptive ILC methods in terms of tracking accuracy, convergence speed, and robustness. Furthermore, the Green ILC method shows superior energy efficiency, highlighting its potential for real-world applications in energy-sensitive control problems. The contributions of this thesis extend the current state of research in ILC by providing innovative solutions to overcome the limitations of traditional methods. The proposed hybrid ILC frameworks not only enhance the adaptability and robustness of control systems but also open new avenues for research in optimization-based control strategies. Future work includes extending these methods to nonlinear and timevarying systems, exploring adaptive mechanisms for handling model uncertainties and disturbances, and validating the proposed approaches through experimental implementations on physical systems, such as robotic manipulators and industrial automation setups.</p>