Nonlinear model predictive torque control of PMSMs for high performance applications

A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC)

“…If convergence is not reached yet, the multipliers and penalties are updated for the next iteration of the algorithm, as detailed in Section 3.5. Note that the penalty update in (18) relies on the last two iterates of the constraint functions (16). In the initial iteration i = 1 and if GRAMPC is used within an MPC setting, the constraint functions g 0 , h 0 , g 0 T , h 0 T are warm-started by the corresponding last iterations of the previous MPC run.…”

“…The new algorithm is based on an augmented Lagrangian formulation in connection with a real-time gradient method and tailored line search and multiplier update strategies that are optimized for a time and memory efficient implementation on embedded hardware. The performance and effectiveness of augmented Lagrangian methods for embedded nonlinear MPC was recently demonstrated for various application examples on rapid prototyping and ECU hardware level [28,46,16]. Beside the presentation of the augmented Lagrangian algorithm and the general usage of GRAMPC, the paper compares its performance to the nonlinear MPC toolkits ACADO and VIATOC for different benchmark problems.…”

Adaptive Speed Controller for a Permanent Magnet Synchronous Motor

“…The permanent magnet synchronous motors (PMSM) has paid and increased popularity in industrial and aerospace applications due the rapid advance in the development of semiconductor technology, new theories of electrical machines control and new magnetic materials that using rare earth. These machines are used in high performance applications for its excellent efficiency, no reactive power demand from the source, compact structure, low maintenance, low inertia and better dynamic performance compared with the induction motor (Omrane et al, 2015, Liu et al, 2018, Englert and Graichen, 2018. These benefits and performance are applied in electric vehicles, airplanes, nuclear power plants, submarines, robots, computer numerical control machines, elevators and propellers of ships (Englert and Graichen, 2018, Jun-Jien et al, 2015, Qutubuddin and Narri Yadaiah, 2018, Kumar et al, 2014.…”

“…These machines are used in high performance applications for its excellent efficiency, no reactive power demand from the source, compact structure, low maintenance, low inertia and better dynamic performance compared with the induction motor (Omrane et al, 2015, Liu et al, 2018, Englert and Graichen, 2018. These benefits and performance are applied in electric vehicles, airplanes, nuclear power plants, submarines, robots, computer numerical control machines, elevators and propellers of ships (Englert and Graichen, 2018, Jun-Jien et al, 2015, Qutubuddin and Narri Yadaiah, 2018, Kumar et al, 2014. However, in some situations the PMSM dynamic performance is diminished significantly by some parametric disturbances (variations in temperature, magnetic saturation, etc.…”

“…), external load torque variations and nonlinearities in the dynamic model. Recently, in the literature there are several studies, they use different control techniques to ensure robust performance despite this problem (Liu et al, 2018, Jun-Jien et al, 2015, Englert and Graichen, 2018, Kumar et al, 2014. Several advanced controllers use non-linear control techniques (Verrelli et al, 2017), adaptive schemes (Hu et al, 2017, Iqbal et al, 2014, linear control theory (Ortega et al, 2018), neural networks (Jon et al, 2017), sliding modes (Lu et al, 2017).…”