scholarly journals Fast Model Predictive Control Combining Offline Method and Online Optimization with K-D Tree

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Ding ◽  
Zuhua Xu ◽  
Jun Zhao ◽  
Zhijiang Shao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Computation Time ◽  
Online Optimization ◽  
Small Scale ◽  
Worst Case ◽  
Online Searching ◽  
Partial Enumeration ◽  
Speedup Factor

Computation time is the main factor that limits the application of model predictive control (MPC). This paper presents a fast model predictive control algorithm that combines offline method and online optimization to solve the MPC problem. The offline method uses a k-d tree instead of a table to implement partial enumeration, which accelerates online searching operation. Only a part of the explicit solution is stored in the k-d tree for online searching, and the k-d tree is updated in runtime to accommodate the change in the operating point. Online optimization is invoked when searching on the k-d tree fails. Numerical experiments show that the proposed algorithm is efficient on both small-scale and large-scale processes. The average speedup factor in the large-scale process is at least 6, the worst-case speedup factor is at least 2, and the performance is less than 0.05% suboptimal.

scholarly journals Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

2021 ◽  
Author(s):  
Andrea Favato ◽  
Paolo Gherardo Carlet ◽  
Francesco Toso ◽  
Riccardo Torchio ◽  
Ludovico Ortombina ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Optimal Solution ◽  
Computation Time ◽  
Experimental Tests ◽  
Current Control ◽  
Computational Time ◽  
Case Scenario ◽  
Worst Case

<div>This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications.</div><div>A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests.</div><div>The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.</div>

scholarly journals Fast, large-scale model predictive control by partial enumeration

Automatica ◽  
2007 ◽  
Vol 43 (5) ◽  
pp. 852-860 ◽  
Author(s):  
Gabriele Pannocchia ◽  
James B. Rawlings ◽  
Stephen J. Wright
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Scale Model ◽  
Large Scale Model ◽  
Partial Enumeration

Online optimization based model predictive control on two wheel Segway system

2020 ◽  
Vol 33 ◽  
pp. 3846-3853
Author(s):  
G. Prabhakar ◽  
S. Selvaperumal ◽  
P. Nedumal Pugazhenthi ◽  
K. Umamaheswari ◽  
P. Elamurugan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Online Optimization

Collision prediction and avoidance for satellite ultra-close relative motion with zonotope-based reachable sets

2018 ◽  
Vol 233 (11) ◽  
pp. 3920-3937
Author(s):  
Zhanpeng Xu ◽  
Xiaoqian Chen ◽  
Yiyong Huang ◽  
Yuzhu Bai ◽  
Qifeng Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Reachable Sets ◽  
Close Relative ◽  
Worst Case ◽  
Reference Orbit ◽  
Robust Model Predictive Control ◽  
Collision Prediction ◽  
Robust Model

Collision prediction and avoidance are critical for satellite proximity operations, and the key is the treatment of satellites' motion uncertainties and shapes, especially for ultra-close autonomous systems. In this paper, the zonotope-based reachable sets are utilized to propagate the uncertainties. For satellites with slender structures (such as solar panels), their shapes are simplified as cuboids which is a special class of zonotopes, instead of the classical sphere approach. The domains in position subspace influenced by the uncertainties and shapes are determined, and the relative distance is estimated to assess the safety of satellites. Moreover, with the approximation of the domains, the worst-case uncertainties for path constraints are determined, and a robust model predictive control method is proposed to deal with the line of sight and obstacle avoidance constraints. With zonotope representations of satellites, the proposed robust model predictive control is capable of handling the shapes of the satellite and obstacle simultaneously. Numerical simulations demonstrate the effectiveness of the proposed methods with an elliptic reference orbit. 1

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