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Robust control of linear synchronous motor servodrive using disturbance observer and recurrent neural network compensator

Robust control of linear synchronous motor servodrive using disturbance observer and recurrent neural network compensator

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Robust control of a permanent magnet (PM) linear synchronous motor (LSM) servodrive is achieved by using a disturbance observer and a recurrent neural network (RNN) compensator. An integral-proportional (IP) controller is introduced to control the mover position of the LSM. The IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. A disturbance observer is implemented and the observed disturbance force is fed forward to increase the robustness of the LSM servodrive. Moreover, to increase the control performance of the LSM servodrive under the occurrence of large disturbance, a RNN compensator is proposed to reduce the influence of parameter variations and external disturbances of the LSM servodrive system as a force controller. In addition, a dynamic backpropagation algorithm is developed to train the RNN online using the delta adaptation law. The effectiveness of the proposed control schemes is demonstrated by some simulated and experimental results.

Inspec keywords: backpropagation; recurrent neural nets; robust control; neurocontrollers; machine theory; tracking; control system analysis; synchronous motor drives; control system synthesis; position control; compensation; machine control; permanent magnet motors; servomotors; linear synchronous motors

Other keywords: force controller; integral-proportional controller; parameter variations; dynamic backpropagation algorithm; disturbance observer; robustness; recurrent neural network compensator; robust control design; control simulation; PM linear synchronous motor servodrive; external disturbances; positioning; time-domain command tracking specifications

Subjects: Control of electric power systems; Linear machines; Spatial variables control; Neurocontrol; Stability in control theory; Small and special purpose electric machines; Drives; Neural nets (theory); Learning in AI (theory); Synchronous machines; Control system analysis and synthesis methods

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