A Finite-Memory Discretization Algorithm for the Distributed Parameter Maxwell-Slip Model

Modeling and compensating for hysteresis are widely adopted to eliminate hysteresis. The distributed parameter Maxwell-slip (DPMS) model is developed from the Maxwell-slip model by replacing the spring-slider elements with an elastic-sliding cell with distributed parameters. Motivated by the mechanism of human memory, this article proposes a finite-memory (FM) discretization approach for the DPMS model. The change in the infinite internal state is represented by updating the finite peak points. The FM approach is verified using a piezoelectric actuator, and the normalized mean square error is 0.27%. Thus, the FM approach is also advantageous for managing small-amplitude excitations.