Edgar Ramirez-Laboreo

Eloy Serrano-Seco, Edgar Ramirez-Laboreo, Eduardo Moya-Lasheras

Differentially flat models are frequently used to design feedforward controllers for electromechanical systems. However, control performance depends on model accuracy, which makes feedback imperative. This paper presents a control scheme for electromechanical systems in which measuring or estimating the output to be controlled -- typically the position -- is not feasible. It employs an identifiable-model-based controller and predictor, coupled with an iterative loop that updates model parameters using the error between a measurable output and its prediction. Simulations on electromechanical switching devices show effective tracking of the desired position trajectory using only coil current measurements.

Jorge Vicente-Martinez, Edgar Ramirez-Laboreo

This paper presents an optimal trajectory generation method for 3D overhead cranes by leveraging differential flatness. This framework enables the direct inclusion of complex physical and dynamic constraints, such as nonlinear friction and collision avoidance for both payload and rope. Our approach allows for aggressive movements by constraining payload swing only at the final point. A comparative simulation study validates our approach, demonstrating that neglecting dry friction leads to actuator saturation and collisions. The results show that friction modeling is a fundamental requirement for fast and safe crane trajectories.

Jorge Vicente-Martinez, Edgar Ramirez-Laboreo

This paper presents a new approach to accurately simulating 3D overhead cranes with friction. Although nonlinear friction dynamics has a significant impact on these systems, accurately modeling this phenomenon in simulations is a significant challenge. Traditional methods often rely on imprecise approximations of friction or require excessive computational times for reliable results. To address this, we present a hybrid dynamical model that features a trade-off between high-fidelity friction modeling and computational efficiency. Furthermore, we present a step-by-step algorithm for the comprehensive estimation of all unknown system parameters, including friction. This methodology is based on Bayesian Linear Regression and Least Squares (LS) estimations. Finally, experimental validation with a laboratory crane confirms the effectiveness of the proposed modeling and estimation approach.