Approximate non-linear model predictive control with safety-augmented neural networks

Abstract

Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems, but requires computationally expensive online optimization. This paper studies approximations of such MPC controllers via neural networks (NNs) to achieve fast online evaluation. We propose safety augmentation that yields deterministic guarantees for convergence and constraint satisfaction despite approximation inaccuracies. We approximate the entire input sequence of the MPC with NNs, which allows us to verify online if it is a feasible solution to the MPC problem. We replace the NN solution by a safe candidate based on standard MPC techniques whenever it is infeasible or has worse cost. Our method requires a single evaluation of the NN and forward integration of the input sequence online, which is fast to compute on resource-constrained systems. The proposed control framework is illustrated using two numerical non-linear MPC benchmarks of different complexity, demonstrating computational speedups that are orders of magnitude higher than online optimization. In the examples, we achieve deterministic safety through the safety-augmented NNs, where a naive NN implementation fails.

References in corpus (6)

• Efficient representation and approximation of model predictive control laws via deep learning
• Learning an Approximate Model Predictive Controller with Guarantees
• Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
• Robust adaptive MPC using control contraction metrics
• Probabilistic performance validation of deep learning-based robust NMPC controllers
• MPC-Net: A First Principles Guided Policy Search