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Adaptive MPC for ground vehicles: review and implementation

US & West ·

A review of adaptive model predictive control (AMPC) methods for autonomous vehicles is provided. Strategies that dynamically adapt the model, cost function, constraints, or prediction horizon based on real-time data are considered. It is shown that AMPC overcomes the limitations of traditional MPC in dynamic environments.

Why it matters

The review systematizes approaches to adaptive control, which is useful for developing robust control systems under uncertainty.

Relevant to DT products

Original headline
Adaptive Model Predictive Control for Ground Vehicles: Review and Demonstrative Implementation
Read the original: arXiv — Systems and Control (eess.SY)

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Parallel branch MPC on GPUs

A GPU-based solver for trajectory planning problems using branch model predictive control is presented. The solver is based on iterative LQR, multiple-shooting formulation, and augmented Lagrangian method for constraint handling. Numerical experiments show superiority over a CPU-based solver on large-scale problems.

DT.ForecastarXiv — Systems and Control (eess.SY) ↗