ADMM-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

by   Zilong Cheng, et al.

This paper investigates the multi-agent collision-free control problem for medium and large scale systems. For such multi-agent systems, it is the typical situation where conventional methods using either the usual centralized model predictive control (MPC), or even the distributed counterpart, would suffer from substantial difficulty in balancing optimality and computational efficiency. Additionally, the non-convex characteristics that invariably arise in such collision-free control and optimization problems render it difficult to effectively derive a reliable solution (and also to thoroughly analyze the associated convergence properties). To overcome these challenging issues, this work establishes a suitably novel parallel computation framework through an innovative mathematical problem formulation; and then with this framework and formulation, the alternating direction method of multipliers (ADMM) algorithm is presented to solve the sub-problems arising from the resulting parallel structure. Furthermore, an efficient and intuitive initialization procedure is developed to accelerate the optimization process, and the optimum is thus determined with significantly improved computational efficiency. As supported by rigorous proofs, the convergence of the proposed ADMM iterations for this non-convex optimization problem is analyzed and discussed in detail. Finally, a multi-agent system with a group of unmanned aerial vehicles (UAVs) serves as an illustrative example here to demonstrate the effectiveness and efficiency of the proposed approach.


Hierarchical ADMM for Nonconvex Cooperative Distributed Model Predictive Control

Distributed optimization is often widely attempted and innovated as an a...

Optimization Based Collision Avoidance for Multi-Agent DynamicalSystems in Goal Reaching Task

This work presents a distributed MPC-based approach to solving the probl...

Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints

In the recent literature, significant and substantial efforts have been ...

Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers

Bayesian optimization (BO) is a powerful black-box optimization framewor...

Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Developments in cooperative trajectory planning of connected autonomous ...

Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning

In this work, we adopt Wyner common information framework for unsupervis...

Semi-Definite Relaxation Based ADMM for Cooperative Planning and Control of Connected Autonomous Vehicles

This paper investigates the cooperative planning and control problem for...

Please sign up or login with your details

Forgot password? Click here to reset