LAUSR

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.