Hybrid Stochastic CF Model with ABC-Based Model Selection for Accurate Learning

In this study, we propose a hybrid control framework that combines different control models to improve the accuracy and robustness of the control system. The proposed model uses a Bayesian approach to learn the optimal parameters for each control model based on the observed data. We experiment with different control models and compare the results with those obtained using individual models stochastically learned with the incorporation of only one CF model in the learning process.
The proposed hybrid framework consists of four components: (1) a probabilistic model to approximate the posterior distribution of the hybrid CF model, (2) a learning algorithm to optimize the parameters of each component, (3) a data-driven method to learn the stochastic behavior of the system, and (4) a fusion module to combine the predictions of the individual models.
The proposed method is evaluated through simulations using a robotic arm. The results show that the hybrid framework outperforms the individual models in terms of accuracy and robustness. Specifically, the hybrid framework achieves better average position, speed, and acceleration errors than the individual models. Additionally, the hybrid framework shows more robust performance in the presence of disturbances.
The key contribution of this study is the proposal of a hybrid control framework that combines different control models to improve the accuracy and robustness of the control system. The proposed method uses a Bayesian approach to learn the optimal parameters for each control model based on the observed data, which allows for more accurate predictions and better handling of disturbances.
In summary, this study proposes a hybrid control framework that combines different control models to improve the accuracy and robustness of the control system. The proposed method uses a Bayesian approach to learn the optimal parameters for each control model based on observed data and shows promising results in simulations using a robotic arm.