2026-10-11T21:10:29Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32826092 2026-10-01T16:10:50Z portal_1172 item_type_8 month_year_10_2026

Risk-averse and nonlinear MPC: parallel numerical algorithms and applications to autonomous heavy equipment Ruairi John Moran (24304400) PUREID: 655813051 Risk-averse mpc mpc model predictive control nonlinear mpc parallel computing numerical algorithm numerical optimization optimal control gpu accelerated autonomous navigation automatic differentiation control The objective of this work is to advance risk-averse model predictive control (MPC) for solving risk-averse optimal control problems (RAOCPs). To this end, we devise SPOCK, a massively parallel algorithm for solving RAOCPs and implement it using CUDA C++ for GPU-enabled hardware. We benchmark the implementation with a Dolan-Moré plot and test a realistic application based on a networked control system with random time delay. The benchmarking results show that SPOCK outperforms current state-of-the-art interior-point solvers for solving large-scale RAOCPs.<br><br>The other objective of this work, that is, heavy equipment navigation, turns our attention to the case of nonlinear dynamics. We create a hierarchical nonlinear MPC controller for obstacle avoidance, with special emphasis on heavy equipment, particularly skid-steer track loaders. In order to improve the controller solve times, we develop gradgen, a new package for automatic differentiation, that is fast, memory-safe, and has a low memory footprint. The benchmarking results show that gradgen outperforms CasADi, the current state-of-the-art tool for automatic differentiation. 2026-10-01T16:10:50Z Text Thesis 10.17034/32826092.v1 https://figshare.com/articles/thesis/Risk-averse_and_nonlinear_MPC_parallel_numerical_algorithms_and_applications_to_autonomous_heavy_equipment/32826092 All Rights Reserved