Experimental Evaluation of Cascaded Continuous and Finite Set Model Predictive Speed Control for Electrical Drives

The proposed paper describes and experimentally validates a cascaded continuous and finite set model predictive control (CCF-MPC) algorithm for a mechatronic drive system. This approach is advantageous for the speed control of electrical drives in mechatronic systems with high requirements on the electrical and mechanical controlled system equally. CCF-MPC enables, on the one hand, the optimization of the steady-state current performance, as indicated by a reduced total harmonic distortion, and a highly dynamic current behavior by using the advantages and direct control nature of finite control set MPC. On the other hand, due to the integration of a continuous control set MPC concept, CCF-MPC allows a foresighted and active damping of mechanical oscillations in the load speed. This is beneficial for the overall predictive optimization of mechatronic systems (e.g., two-mass systems), which are present, e.g., for electrical drives in machine tools. 1 1The research leading to these results has received funding from the Bavarian Ministry of Economic Affairs, Energy and Technology and is managed by VDI/VDE under grant agreement ESB048/004.     «

The proposed paper describes and experimentally validates a cascaded continuous and finite set model predictive control (CCF-MPC) algorithm for a mechatronic drive system. This approach is advantageous for the speed control of electrical drives in mechatronic systems with high requirements on the electrical and mechanical controlled system equally. CCF-MPC enables, on the one hand, the optimization of the steady-state current performance, as indicated by a reduced total harmonic distortion, and...     »