scholarly journals An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1123
Author(s):  
Yao Wei ◽  
Yanjun Wei ◽  
Yening Sun ◽  
Hanhong Qi ◽  
Mengyuan Li

In nonlinear model predictive control (NMPC), higher accuracy can be obtained with a shorter prediction horizon in steady-state, better dynamics can be obtained with a longer prediction horizon in a transient state, and calculation burden is proportional to the prediction horizon which is usually pre-selected as a constant according to dynamics of the system with NMPC. The minimum calculation and prediction accuracy are hard to ensure for all operating states. This can be improved by an online changing prediction horizon. A nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method has been proposed in which the prediction horizon is improved as a discrete-time integer variable and can be adjusted during each sampling period. A permanent magnet synchronous motor (PMSM) rotor position control system with the proposed strategy is accomplished. Tracking performances including rotor position Integral of Time-weighted Absolute value of the Error (ITAE), the maximal delay time, and static error are improved about 15.033%, 23.077%, and 10.294% respectively comparing with the conventional NMPSC strategy with a certain prediction horizon. Better disturbance resisting performance, lower weighting factor sensitivities, and higher servo stiffness are achieved. Simulation and experimental results are given to demonstrate the effectiveness and correctness.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yao Wei ◽  
Yanjun Wei ◽  
Yang Gao ◽  
Hanhong Qi ◽  
Xiaoqiang Guo ◽  
...  


Author(s):  
Faten Ben Aicha ◽  
Faouzi Bouani ◽  
Mekki Ksouri

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.





2021 ◽  
pp. 613-622
Author(s):  
Rajen Pudur ◽  
Brajagopal Datta


Sign in / Sign up

Export Citation Format

Share Document