Adaptive Inverse System Control of Electromagnetic Linear Actuator

Abstract

Against EMLA (Electromagnetic Linear Actuator) in the long-running, the changed parameters of actuator coil resistance and inductance caused by temperature rise resulted in degradation of control performance, and parameters recondition in the inverse system control program. By using parameter identification method based on recursive least squares method (RLSM), the adaptive inverse system control is implemented in electromagnetic linear actuator. Under Matlab/Simulink it establishes the simulation model to simulate online identification process and the adaptive inverse control system strategy which achieve the control effect when the parameters change, and the test data are identified off-line verification. The results show that it is able to accurately identify the actuator coil resistance and inductance parameters, and at the same time, it can identify system's dynamic mass, viscous friction coefficient and disturbance force of load. Finally, the test platform was built to verify, the identification results was used to control parameters online real-time. And the parameters identification was used in the practical application of inverse system control, implemented system's adaptive control, improved system's robustness in long run.

Time Varying Back Propagating Algorithm for MIMO Adaptive Inverse Controller

“…Reference input is chosen as square signal for this simulation. The plant controlled results are presented in Figure (20) and (21) with respect to output-1 and output-2. The desired plant output ( blue dashed line) and the true system output (red solid line) are indicated in these results.…”

“…The parameter matrix is identified through a continuous modification process while the sum of squared error is achieved at its minimum range. Therefore, the identified parameters are kept closer to the actual parameters of the system [20]. Although RLS method is very fast process but it is highly complex in terms of computational cost.…”