Multistep Finite Control Set Model Predictive Control for Linear Induction Machines
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Solution Overview
Problem
Linear induction machines face challenges in generating high thrust at high speeds due to end effects and parameter changes, leading to difficulties in control and increased current distortion at low switching frequencies, which conventional control strategies fail to address effectively.
Innovation Solution
A multistep finite control set model predictive control method is developed, which compensates for end effects by optimizing voltage vector switching sequences and reducing calculation complexity, allowing for improved control performance and lower current distortion at low switching frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multistep predictive control is used to reduce current distortion at low switching frequency, then current quality improves, but the number of candidate voltage vector sequences increases exponentially making calculation more cumbersome
Solution Approach 1:
The patent segments the voltage vector selection process into multiple independent steps. Instead of evaluating all possible voltage vector sequences simultaneously, the controller divides the prediction into N steps where each step selects from a reduced set of candidate voltage vectors. This segmentation reduces the computational complexity from exponential to linear while maintaining current quality through the multistep prediction approach.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing the voltage vector sequences in advance. The controller pre-generates candidate voltage vector sequences based on the multistep prediction, then selects the optimal sequence from these pre-computed options. This preliminary computation reduces the real-time calculation burden while ensuring optimal current quality control.
2Ease of operation
If conventional control strategies are used, then control simplicity is maintained, but parameter changes due to end effect are not compensated leading to poor control results
Solution Approach 1:
The patent implements feedback by continuously monitoring the actual machine parameters and comparing them with the predicted values. The controller uses the prediction error to adjust the voltage vector selection in real-time, compensating for parameter changes caused by end effects. This feedback mechanism maintains control accuracy without significantly increasing operational complexity.
Solution Approach 2:
The patent explicitly accounts for parameter changes by incorporating end effect compensation into the prediction model. The controller dynamically adjusts the machine parameters in the prediction algorithm to reflect the actual operating conditions, including the influence of end effects. This allows the system to maintain reliable control accuracy while adapting to varying operating conditions.
Data Source
AI summary
A multistep finite control set model predictive control method for linear induction machines is provided, and the method belongs to a control technology field for linear induction machines. The method specifically includes the following steps: collecting a primary phase current of a linear induction machine in a three-phase coordinate system; solving a multistep reference voltage vector sequence by iteration according to a current value; keeping only two non-zero voltage vectors and one zero voltage vector that are closest to a reference voltage vector in each predictive step; and further eliminating a voltage vector sequence having a large cost function value through dynamic online comparison with a cost function value.