Model parameterization of robotic systems through the bio-inspired optimization

The accurate modeling of dynamic systems, particularly robotic ones, is crucial in the industry. It enables simulation-based approaches that facilitate various tasks without requiring the physical system, thereby reducing risks and costs. These approaches range from model-in-the-loop (MiL), where a simulated model of the real plant is used for controller design, to hardware-in-the-loop (HiL), which provides more realistic simulations on specialized real-time hardware. Among these, MiL is widely adopted due to its simplicity and effectiveness in developing control strategies. However, to fully leverage the advantages of MiL, developing a robust and accurate system model parameterization methodology is essential. This methodology should be adaptable to a wide range of applications, adopt a holistic approach, and balance the cost-benefit trade-offs in model characteristics. Achieving this, however, introduces additional challenges related to system complexity and the inherent properties of the model. To address these challenges, this work proposes a model parameterization approach for robotic systems using bio-inspired optimization to develop accurate and practical models for system design. The approach formulates an optimization problem to determine the dynamic model parameters of a robot, ensuring its behavior closely resembles that of the real system. Due to the complexity of this problem, bio-inspired optimization techniques are particularly well-suited. The proposed method is validated using a theoretical, non-conservative model of a three-degree-of-freedom serial robot. The dynamic parameters of its three links were identified to effectively generalize the real system. To solve the optimization problem, three bio-inspired algorithms were employed: the genetic algorithm, particle swarm optimization, and differential evolution. The optimal parameterization obtained for the robot model demonstrated the effectiveness of the proposed approach in a MiL simulation environment, achieving an overall correlation of 0.9019 in the experiments. This correlation highlights the model's ability to predict the robot's behavior accurately. Additionally, the methodology's efficacy was further validated in another electromechanical system, the reaction force-sensing series elastic actuator, yielding a correlation of 0.8379 in the resulting model.