Abstract
AbstractThe article proposes a nonlinear H-infinity control method for switched reluctance machines. The dynamic model of the switched reluctance machine undergoes approximate linearization round local operating points which are redefined at each iteration of the control algorithm. These temporary equilibria consist of the last value of the reluctance machine’s state vector and of the last value of the control signal that was exerted on it. For the approximate linearization of the reluctance machine’s dynamics, Taylor series expansion is performed through the computation of the associated Jacobian matrices. The modelling errors are compensated by the robustness of the control algorithm. Next, for the linearized equivalent model of the reluctance machine an H-infinity feedback controller is designed. This requires the solution of an algebraic Riccati equation at each time-step of the control method. It is shown that the control scheme achieves H-infinity tracking performance, which implies maximum robustness to modelling errors and external perturbations. The stability of the control loop is proven through Lyapunov analysis.
Highlights
Switched reluctance machines (SRM) exhibit specific advantages comparing to other electric machines
A nonlinear H-infinity control method has been developed for the dynamic model of switched reluctance machines
The nonlinear dynamic model of the SRM has undergone approximate linearization around a temporary operating point which was recomputed at each iteration of the control algorithm
Summary
Switched reluctance machines (SRM) exhibit specific advantages comparing to other electric machines. G. Rigatos et al, Nonlinear H-infinity control for switched reluctance machines makes use of Taylor series expansion [48,49,50,51]. Nonlinear H-infinity control for switched reluctance machines makes use of Taylor series expansion [48,49,50,51] This relies on the computation of the Jacobian matrices of the SRM’s state-space model. This allows for the implementation of state-estimation based feedback control through the processing of measurements from a limited number of sensors.
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