The Auto-Multilift framework can be extended to handle uncertainties in the system model by incorporating robust control techniques and adaptive learning strategies. One approach is to integrate a model uncertainty representation within the deep neural networks (DNNs) that tune the model predictive controllers (MPCs). This can be achieved by augmenting the input features of the DNNs to include estimates of uncertainty, such as variations in cable stiffness, damping ratios, and load mass distribution.
Additionally, the framework can employ a probabilistic model that captures the uncertainties in cable properties and load characteristics. By using techniques such as Gaussian processes or Bayesian optimization, the DNNs can learn to adaptively adjust the MPC hyperparameters based on real-time feedback from the system. This would allow the Auto-Multilift framework to dynamically update its control strategies in response to observed discrepancies between the predicted and actual system behavior.
Furthermore, incorporating a robust optimization approach within the MPC formulation can enhance the system's resilience to uncertainties. This involves modifying the cost function to penalize deviations from desired performance metrics while considering worst-case scenarios for the uncertain parameters. By doing so, the Auto-Multilift framework can maintain effective load transportation even in the presence of unknown or varying cable properties and load mass distributions.