Nonlinear Programming Optimization Towards Optimal Transition Design in Model Free Predictive Control

Model free predictive control (MFPC) is a promising control approach which not only obviates the need for electric machine model, but also it is substituted with the conventional PI current controller and the pulse width modulation (PWM) resulting in removal of integrator and PWM, consequently less control effort. Voltage/current slope lookup table-based MFPC method has attracted notable attention due to its merits in terms of robustness and low computational effort. Since online voltage/current lookup table data is required to drive the electric motor, this method is incapable of direct motor startup. This study presents an optimal transition design from conventional to voltage/current lookup table-based MFPC method. The optimal transition problem has been derived analytically at first as a nonlinear objective function with two linear constraints. Afterwards, a nonlinear programing optimization algorithm is employed to solve the problem and simulation results are provided to support the efficacy of the proposed method.