Parallel-Constraint Model Predictive Control: Exploiting Parallel Computation for Improving Safety

2025-09-03

AI Summary

Ensuring safety for safety-critical nonlinear systems—e.g., robotic manipulators—under state/input constraints and collision avoidance remains challenging for model predictive control (MPC). Method: This paper proposes a safety-enhanced nonlinear MPC (NMPC) framework leveraging parallel computation. Its core innovation is the explicit, time-domain-parallel expansion of control-invariant safe set constraints across the prediction horizon, enabling simultaneous optimization of multiple candidate trajectories. Leveraging multi-core architectures, it solves control sequences at all time steps in parallel and dynamically selects the optimal one. The approach integrates nonlinear optimization, control-invariant set theory, and parallel computing. Contribution/Results: The framework significantly improves real-time safety guarantees and decision flexibility. In simulations on a 3-DOF robotic arm, it achieves strict constraint satisfaction while delivering markedly faster response times and enhanced obstacle-avoidance robustness compared to serial NMPC—using only four CPU cores.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Non-convex Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Ensuring constraint satisfaction is a key requirement for safety-critical systems, which include most robotic platforms. For example, constraints can be used for modeling joint position/velocity/torque limits and collision avoidance. Constrained systems are often controlled using Model Predictive Control, because of its ability to naturally handle constraints, relying on numerical optimization. However, ensuring constraint satisfaction is challenging for nonlinear systems/constraints. A well-known tool to make controllers safe is the so-called control-invariant set (a.k.a. safe set). In our previous work, we have shown that safety can be improved by letting the safe-set constraint recede along the MPC horizon. In this paper, we push that idea further by exploiting parallel computation to improve safety. We solve several MPC problems at the same time, where each problem instantiates the safe-set constraint at a different time step along the horizon. Finally, the controller can select the best solution according to some user-defined criteria. We validated this idea through extensive simulations with a 3-joint robotic arm, showing that significant improvements can be achieved in terms of safety and performance, even using as little as 4 computational cores.

Problem

• Improving safety in nonlinear robotic systems via parallel computation
• Ensuring constraint satisfaction for safe Model Predictive Control
• Exploiting parallel MPC to enhance collision avoidance and performance

Innovation

• Parallel computation for multiple MPC problems
• Safe-set constraint at different time steps
• Selecting best solution via user criteria