2022/02/12 by Yaling Ma, Runze Gao, Ma, Yaling +7
Engineering · Materials Science · #Advanced Control Systems Optimization #Conducting polymers and applications #Electrochemical sensors and biosensors #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.06012
openalex publication_date 2022/02/12 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonlinearity, we devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multi-block alternating direction method of multipliers (ADMM) algorithm. This novel parallel multi-block ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints.