Towards parallelizable sampling-based Nonlinear Model Predictive Control
For real-time control of nonlinear systems, this provides a parallelizable, suboptimal MPC method with theoretical guarantees, addressing computational bottlenecks in existing solvers.
The paper proposes a sampling-based nonlinear MPC algorithm with O(N^2) complexity in prediction horizon N and O(samples) complexity, enabling parallelization. The algorithm guarantees recursive feasibility, stability, and cost improvement, converging rapidly to near-optimal solutions even with few samples.
This paper proposes a new sampling-based nonlinear model predictive control (MPC) algorithm, with a bound on complexity quadratic in the prediction horizon N and linear in the number of samples. The idea of the proposed algorithm is to use the sequence of predicted inputs from the previous time step as a warm start, and to iteratively update this sequence by changing its elements one by one, starting from the last predicted input and ending with the first predicted input. This strategy, which resembles the dynamic programming principle, allows for parallelization up to a certain level and yields a suboptimal nonlinear MPC algorithm with guaranteed recursive feasibility, stability and improved cost function at every iteration, which is suitable for real-time implementation. The complexity of the algorithm per each time step in the prediction horizon depends only on the horizon, the number of samples and parallel threads, and it is independent of the measured system state. Comparisons with the fmincon nonlinear optimization solver on benchmark examples indicate that as the simulation time progresses, the proposed algorithm converges rapidly to the "optimal" solution, even when using a small number of samples.