Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables
Friction systems are mechanical systems wherein friction is used for force transmission (e.g. mechanical braking systems or automatic gearboxes). For finding optimal and safe design parameters, engineers have to predict friction system performance. This is especially difficult in real-world applications, because it is affected by many parameters. We have used symbolic regression and genetic programming for finding accurate and trustworthy prediction models for this task. However, it is not straight-forward how nominal variables can be included. In particular, a one-hot-encoding is unsatisfactory because genetic programming tends to remove such indicator variables. We have therefore used so-called factor variables for representing nominal variables in symbolic regression models. Our results show that GP is able to produce symbolic regression models for predicting friction performance with predictive accuracy that is comparable to artificial neural networks. The symbolic regression models with factor variables are less complex than models using a one-hot encoding.
Code (0)
등록된 구현이 없습니다.
Tasks
FrictionregressionSymbolic RegressionSimilar Papers 제목 키워드 기반
Interpretable Robotic Friction Learning via Symbolic Regression
Accurately modeling the friction torque in robotic joints has long been challenging due to the request for a robust mathematical description. Traditional model-based approaches are often labor-intensive, requiring extens…
FrictionregressionSymbolic RegressionMotor State Prediction and Friction Compensation for Brushless DC Motor Drives Using Data-Driven Techniques
In order to provide robust, reliable, and accurate position and velocity control of motor drives, friction compensation has emerged as a key difficulty. Non-characterised friction could give rise to large position errors…
FrictionPositionPhysics-informed Machine Learning for Static Friction Modeling in Robotic Manipulators Based on Kolmogorov-Arnold Networks
Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are widely used due to their simple forms; h…
Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot
For model-based control, an accurate and in its complexity suitable representation of the real system is a decisive prerequisite for high and robust control quality. In a structured step-by-step procedure, a model predic…
FrictionModel Predictive ControlSymbolic Regression Methods for Reinforcement Learning
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to…
Decision MakingFrictionregressionreinforcement-learning+3