Scale reduction based efficient model predictive control and its application in vehicle following control

The recent progress in advanced vehicle control systems presents a great opportunity for application of model predictive control (MPC) in automotive industry. However, high computational complexity inherently associated with the receding horizon optimization must be addressed to achieve the real-time implementation. This paper presents a general scale reduction framework to reduce the online computational burden of MPC controllers. A lower dimensional MPC algorithm is developed by integrating an existing `move blocking´ (MB) strategy with a `constraint set compression´ (CSC) strategy, which is first proposed here. Good trade-off between control optimality and computational intensity is achieved by proper design of blocking and compression matrices. Application of the fast algorithm on vehicular following control (e.g. an adaptive cruise control system) was evaluated through real-time simulation. These results indicate that the proposed method significantly improves the computational speed while maintaining satisfactory control optimality without sacrificing the desired performance.