Approximate non-linear model predictive control with safety-augmented neural networks
This addresses the problem of real-time control for resource-constrained systems by providing a safe and efficient alternative to traditional MPC, though it is incremental as it builds on existing MPC and neural network methods.
The paper tackles the computational expense of online optimization in nonlinear model predictive control (MPC) by approximating MPC controllers with neural networks to enable fast evaluation, achieving orders of magnitude speedups in benchmarks while ensuring deterministic safety guarantees for convergence and constraint satisfaction.
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.