SDO and LDO relaxation approaches to complex fractional quadratic optimization

This paper examines a complex fractional quadratic optimization problem subject to two quadratic constraints. The original problem is transformed into a parametric quadratic programming problem by the well-known classical Dinkelbach method. Then a semidefinite and Lagrangian dual optimization approaches are presented to solve the nonconvex parametric problem at each iteration of the bisection and generalized Newton algorithms. Finally, the numerical results demonstrate the effectiveness of the proposed approaches.

Multi-parametric model predictive control for autonomous steering using an electric power steering system

Electric power steering systems have been used to generate assist torque for driver comfort. This study makes use of the functionality of electric power steering systems for autonomous steering control without driver torque. A column-type electric power steering test bench, equipped with a brushless DC motor as an assist motor, and the Infineon TriCore AURIX TC 277 microcontroller was used in this study. Multi-parametric model predictive control is based on a model predictive control–based approach that employs a multi-parametric quadratic programming technique. This technique allows the reduction of the huge computational burden resulting from the online optimization in model predictive control. The proposed controller obtains an optimal input based on multi-parametric quadratic programming at each sampling time. The weighting matrix definition, which is the main task when designing the proposed controller, was analyzed. The experimental results of the step response of the steering wheel angle verified the tracking ability of the proposed controller for different ranges of the prediction horizon. Since the computational loads are directly related to functional safety, the results of this study support the use of the multi-parametric model predictive control scheme as an effective control method for autonomous steering control.