Model Reference Adaptive Control Based on KP Model for Magnetically Controlled Shape Memory Alloy Actuators

Abstract

J Appl Biomater Funct Mater 2017; 15 (Suppl 1): S31-S37 ORIGINAL RESEARCH ARTICLEmodel to compensate complex hysteresis of the MSMA actuators. The valid proportional-integral controller was adopted in this paper. Experimental results prove that tracking error of the hybrid control was reduced by 60% compared to the feedforward control. Ping and Jouaneh (7,8) proposed a classical Preisach model as a controller for the hysteresis nonlinearity compensation of the piezoelectric actuators. A hybrid control consisting of proportional-integral-derivative (PID) feed-back control and feed-forward control was adopted to further increase the positioning precision. Experimental results demonstrate that the maximum error of the hybrid control was reduced by 50% compared to the general PID control. Wang et al (9) proposed an inverse MPI model as a controller to increase the positioning accuracy of the piezoelectric actuators. Simulation results show that the positioning accuracy was doubled when the controller was introduced to compensate the intricate nonlinearity. Song et al (10) employed the inverse classical Preisach model as a controller to eliminate complex hysteresis of piezoelectric ceramic actuators for real-time control. Simulation results show that the error decreased by 50% to 70% when the hysteresis compensation was added to the feed-forward control. Feng et al (11) designed a robust adaptive controller based on the Duhem model to describe the severe hysteresis nonlinearity. Experimental results prove that the controller could effectively eliminate the nonlinearity. Ru et al (12) proposed an effective mathematical model to compensate

Parameter identification of Duhem model based on antlion-fish swarm hybrid algorithm and robust backstepping sliding mode control for rate-dependent hysteresis of piezoelectric actuators

“…H(t) represents the positive hysteresis process of PEA, and H À1 (t) represents the inverse model. The desired signal through the feedforward controller and the controlled object can be expressed by equation (9).…”

“…Therefore, according to dynamic characteristics of hysteresis model, these model are divided into rate-independent hysteresis model and rate-dependent hysteresis model. Among rate-independent hysteresis models, the classical and commonly used models include Preisach model, 6 PI (Prandtl-Ishlinskii) model, 7,8 KP (krasonsel’skii-Pokrovskii) model 9 and so on. While rate-dependent hysteresis models include Bouc-wen model, 10,11 Duhem model, 12 and so on.…”

“…Proc IMechE Part C: J Mechanical Engineering Science 237 (9) Parameter identification of Duhem model by antlion-fish swarm hybrid algorithm is shown in Figure 5(a). The antlion-fish hybrid algorithm only needs 55 iterations and the iteration operation time of 58.507 s, which has the advantages of fast convergence and less iteration consumption.…”

Dynamic surface control based on observer for a class of nonlinear systems with Prandtl-Ishlinskii hysteresis

“…The first approach reduces the effect of hysteresis by constructing an inverse model. [6][7][8] This control scheme has a strong dependence on the accuracy of hysteresis modeling, and its model parameters have strong sensitivity, which makes it difficult to analyze the system stability. The second approach is to directly adopt some adaptive robust feedback control systems without building the inverse hysteresis model.…”

Model order reduction for the Krasnoselskii–Pokrovskii (KP) model

“…The KP model is firstly proposed in [14,21], which is derived from the results of Krasnoselskii and Pokrovskii [22]. The KP model is typically used to predict the hysteresis effect in smart materials-based actuators, such as the shape memory alloy actuator [21,23] and piezoelectric actuator [3]. As indicated above, the KP kernel is the elementary operator to construct the KP model.…”