Hybrid Sphere Decoder for Long Prediction Horizon FCS-MPC

In finite control set model predictive control strategies, extending the prediction horizon length provides important closed-loop performance improvements. However, the computational costs are increased in exponential fashion. Transforming the problem to an equivalent box-constrained integer least-squares formulation enables the usage of sphere decoding algorithms (SDA) that can efficiently solve this problem. Recently, a K-best sphere decoder was proposed and designed for hardware platforms. This algorithm follows a breadth-first strategy different to the conventional SDA. In this work, a hybrid SDA that combines the merits of both the K-best SDA and the conventional SDA is proposed with the objective of increasing optimality likelihood and improve control performance. In particular, it is proposed that a K-best sphere decoder delivers a preliminary optimal solution. Then, a conventional SDA uses the available calculation time to search for a better solution. Simulation and experimental results confirm the validity of the proposal in terms of performance and computational efficiency.

Automated summary

In finite control set model predictive control strategies, extending the prediction horizon length improves closed-loop performance, but at the cost of increased computational complexity. By transforming the problem into an equivalent box-constrained integer least-squares formulation, sphere decoding algorithms (SDA) can efficiently solve the problem. In this study, a hybrid SDA, combining the merits of K-best SDA and conventional SDA, is proposed to increase optimality likelihood and improve control performance.