30, no. 9, pp. 5036-45, in responses to a few inquiries made by audiences. It is shown that due to parameter variations with stator currents, any technique for MTPA tracking based on piecewise constant parameter assumption, i.e., the machine parameters are assumed as constants during the calculation of , would result in tracking error even though the machine parameters are obtained from lookup table or online machine parameter estimations. The error is dependent on machine non-linear characteristics and operating conditions. It is also shown that for the prototype IPMSM the virtual signal injection control (VSIC) technique described in the paper yields a better tracking accuracy.Index Terms-maximum torque per ampere (MTPA), signal injection; virtual signal injection control (VSIC), interior permanent magnet synchronous machine (IPMSM) drives
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interests range from motion control and electromechanical energy conversion to electric drives for applications in automotive, renewable energy, household appliances and aerospace sectors. He is a fellow of the IET and a senior member of IEEE.
This paper describes a simple but effective novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control scheme (SLC) generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the on-line training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations and experiments under various operation conditions on a prototype IPMSM drive system. Index Terms-Maximum torque per ampere control (MTPA), Permanent magnet synchronous machine (IPMSM), Self-learning control (SLC), Signal injection, Signal processing, Torque control, Virtual signal injection (VSIC) Xiao Chen (S'13) born in Taian, China, in 1988, received B.Eng. degree in electrical engineering from Harbin Institute of Technology at Weihai, Weihai, China, in 2009, and received M.Eng. degree in electrical engineering from Harbin Institute of Technology, Harbin, China, in 2011, respectively. Now he is working towards the Ph.D degree in the Dept. of Electronic and Electrical Engineering, The University of Sheffield, UK. His current research interests include the modeling, design and analysis of permanent-magnet synchronous machines for traction applications.
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