Velocity-sensorless tracking control and identification of switched-reluctance motors

Abstract

a b s t r a c tWe present a solution to the velocity-sensorless control problem for switched-reluctance motors under parametric uncertainty. Our main results guarantee velocity tracking control for velocity references with constant reference acceleration under the assumption that the load torque, the rotor inertia, the resistance and inductances are unknown. Under a persistency of excitation condition on a function which depends only on reference trajectories, we guarantee uniform global asymptotic stability therefore, we establish conditions for the identification of the physical parameters of the system. Our theoretical findings are supported by simulation results.

Velocity Regulation in Switched Reluctance Motors under Magnetic Flux Saturation Conditions

“…An important difference of our approach with respect to that of other authors [10,[12][13][14][15] is that, aside from term ( , ) * , we do not require to complete by feedback of an error equation for the electrical subsystem of motor. This allows us to avoid necessity of feedback of complex functions of the state such aṡ * which we only have to dominate.…”

“…Some recent theoretical works on SRM control [10,[12][13][14][15] assume that only proportional electric current controllers are employed. However, the resulting control laws are very complex because their stability proofs require feedback of many nonlinear terms in order to complete the error equation for electric current.…”

Controlling a DC Motor through Lypaunov-like Functions and SAB Technique

“…Arranging (22), and applying Young's inequality ( [34]), yields: where c a is a positive constant that should be chosen to fulfill certain conditions that will be defined later. Substituting into (21), yields…”

“…Other works address the problem of designing a controller for different motors [19,20,21]; despite the fact that the controlled systems operate as it is expected, the main disadvantages of these works are: the size of the output error cannot be determined, and no analyses of the system behavior inlcuding measurement noise are presented. In [22] two coupled controllers are designed: a Linear Quadratic Gaussian (LQG) and a MRAS-based Learning Feed-Forward Controller (LFFC); eventhough the simulation results demonstrate the potential benefits of the proposed controlled, the LQG algorithm may fail to ensure closed-loop stability when variations in the uncertainties are large enough.…”

Nonlinear H-infinity control for switched reluctance machines

“…Control of SRMs is a nontrivial problem because their dynamic model is a highly nonlinear one [9][10][11][12][13][14]. Stability and robustness are also features of primary importance in the development of SRM control schemes [15][16][17][18][19][20]. It is noteworthy that the use of SRMs in traction of electric vehicles is gaining ground [21][22][23][24][25][26].…”