Shifting strategy for efficient block-based non-linear model predictive control using real-time iterations

摘要

Non-linear model predictive control requires the use of efficient solutions and strategies for its implementation in fast/real-time systems. A popular approach for this is the real-time iteration scheme, which uses a shifting strategy, namely the initial value embedding, that shifts the solution from one sampling time to the next. However, this strategy together with other efficient strategies such as move blocking, present a recursive feasibility problem. This study proposes a novel modified shifting strategy which preserve both recursive feasibility and stability properties, as well as achieves a significant reduction in the computational burden associated with the optimisation. The proposed approach is validated through a simulation of an inverted pendulum where it clearly outperforms other standard solutions in terms of performance and recursive feasibility properties. Additionally, the approach was tested on two computing platforms: a laptop with an i7 processor and a Beaglebone Blue Linux-based computer for robotic systems, where computational gains compared to existing approaches are shown to be as high as 100 times faster.