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The recent progress of advanced vehicle control systems presents a great opportunity for the application of model predictive control (MPC) in the automotive industry. However, high computational complexity inherently associated with the receding horizon optimization must be addressed to achieve real-time implementation. This paper presents a generic scale reduction framework to reduce the online computational burden of MPC controllers. A lower dimensional MPC algorithm is formulated by combining an existing "move blocking " strategy with a "constraint-set compression" strategy, which is proposed to further reduce the problem scale by partially relaxing inequality constraints in the prediction horizon. The closed-loop stability is guaranteed by adding terminal zero-state constraint. The tradeoff between control optimality and computational intensity is achieved by proper design of the blocking and compression matrices. The fast algorithm has been applied on intelligent vehicular longitudinal automation, implemented as a fuel economy-oriented adaptive cruise controller and experimentally evaluated by a series of real-time simulations and field tests. These results indicate that the proposed method significantly improves the computational speed while maintaining satisfactory control optimality without sacrificing the desired performance.Index Terms-Adaptive cruise control (ACC), computation efficiency, fuel economy, model predictive control (MPC).

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Background The further application of conventional ultrasound (US) image-guided microwave (MW) ablation of liver cancer is often limited by two-dimensional (2D) imaging, inaccurate needle placement and the resulting skill requirement. The three-dimensional (3D) image-guided robotic-assisted system provides an appealing alternative option, enabling the physician to perform consistent, accurate therapy with improved treatment effectiveness.