This paper focuses on validating a model-free Value Iteration Reinforcement Learning (MFVI-RL) control solution on a visual servo tracking system in a comprehensive manner starting from theoretical convergence analysi...
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This paper focuses on validating a model-free Value Iteration Reinforcement Learning (MFVI-RL) control solution on a visual servo tracking system in a comprehensive manner starting from theoretical convergence analysis to detailed hardware and software implementation. Learning is based on a virtual state representation reconstructed from input-output (I/O) system samples under nonlinear observability and unknown dynamics assumptions, while the goal is to ensure linear output reference model (ORM) tracking. Secondary, a competitive model-free Virtual State-Feedback Reference Tuning (VSFRT) is learned from the same I/O data using the same virtual state representation, demonstrating the framework's learning capability. A model-based two degrees-of-freedom (2DOF) output feedback controller serving as a comparisons baseline is designed and tuned using an identified system model. With similar complexity and linear controller structure, MFVI-RL is shown to be superior, confirming that the model-based design issue of poor identified system model and control performance degradation can be solved in a direct data-driven style. Apart from establishing a formal connection between output feedback control, state feedback control and also between classical control and artificial intelligence methods, the results also point out several practical trade-offs, such as I/O data exploration quality and control performance leverage with data volume, control goal and controller complexity.
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