An alternating direction method of multipliers algorithm for symmetric model predictive control

2020 ◽  
Author(s):  
Claus Danielson
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Alternating Direction Method ◽  
Symmetric Model ◽  
Method Of Multipliers ◽  
Alternating Direction

scholarly journals Fast Model Predictive Control Based on Adaptive Alternating Direction Method of Multipliers

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yu Li ◽  
Qiming Zou ◽  
Xiaoru Ji ◽  
Chanyuan Zhang ◽  
Ke Lu
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Large Scale ◽  
Alternating Direction Method ◽  
Control Input ◽  
Method Of Multipliers ◽  
Time Step ◽  
Prediction Horizon ◽  
Alternating Direction

Model Predictive Control (MPC) can effectively handle control problem with disturbances, multicontrol variables, and complex constraints and is widely used in various control systems. In MPC, the control input at each time step is obtained by solving an online optimization problem, which will cause a time delay in real time on embedded computers with limited computational resources. In this paper, we utilize adaptive Alternating Direction Method of Multipliers (a-ADMM) to accelerate the solution of MPC. This method adaptively adjusts penalty parameter to balance the value of primal residual and dual residual. The performance of this approach is profiled via the control of a quadcopter with 12 states and 4 controls and prediction horizon ranging from 10 to 40. The simulation results demonstrate that the MPC based on a-ADMM has a significant improvement in real-time and convergence performance and thus is more suitable for solving large-scale optimal control problems.

scholarly journals Model Predictive Control for an Aerial Tree-Pruning Robot Based on Alternating Direction Method of Multipliers

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Changliang Xu ◽  
Zhong Yang ◽  
Hao Xu ◽  
Qiuyan Zhang ◽  
Dongsheng Zhou ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Uncertain System ◽  
Alternating Direction Method ◽  
Rotational Inertia ◽  
Method Of Multipliers ◽  
Alternating Direction ◽  
Attitude Tracking ◽  
Control Scheme ◽  
Tree Pruning

Obstacles of some trees within the electric power transmission line channel are of great threat to the electricity supply. Nowadays, the tasks of clearing threatening tree branches are still mostly operated by hand and simple tools. In this article, an aerial tree-pruning robot with a novel structure is designed to improve the pruning operation efficiency and enhance the safety of the staff. However, the long arm of the pruning tool results in much higher rotational inertia of the robot, which brings difficulties for the robot to remain stable. Therefore, a control scheme based on model predictive control is proposed for the aerial tree-pruning robot and to deal with an uncertain system during the pruning operation period. One of the main contributions is that an ADMM (alternating direction method of multipliers) algorithm that solves the constrained QP (quadratic programming) is adopted to implement the model predictive control on embedded computers with limited computational power. The dynamic model of the pruning robot is firstly presented. Then, the control scheme of MPC for the pruning robot is presented. Moreover, the QP problem of robot control is addressed with ADMM. Finally, simulation experiments of attitude tracking as well as the antidisturbances capability verification have been conducted. Results for the system of aerial tree-pruning robot are given to demonstrate the effectiveness of the developed attitude tracking control scheme using ADMM-based MPC.

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