Hysteresis Compensation and Sliding Mode Control with Perturbation Estimation for Piezoelectric Actuators

Based on the background of atomic force microscope (AFM) driven by piezoelectric actuators (PEAs), this paper proposes a sliding mode control coupled with an inverse Bouc–Wen (BW) hysteresis compensator to improve the positioning performance of PEAs. The intrinsic hysteresis and creep characteristics degrade the performance of the PEA and cause accuracy loss. Although creep effect can be eliminated by the closed-loop control approach, hysteresis effects need to be compensated and alleviated by hysteresis compensators. For the purpose of dealing with the estimation errors, unmodeled vibration, and disturbances, a sliding mode control with perturbation estimation (SMCPE) method is adopted to enhance the performance and robustness of the system. In order to validate the feasibility and performance of the proposed method, experimental studies are carried out, and the results show that the proposed controller performs better than a proportional-integral-derivative (PID) controller at 1 and 2 Hz, reducing error to 1.2% and 1.4%, respectively

Identification of Preisach Model Parameters Based on an Improved Particle Swarm Optimization Method for Piezoelectric Actuators in Micro-Manufacturing Stages

The Preisach model is a typical scalar mathematical model used to describe the hysteresis phenomena, and it attracts considerable attention. However, parameter identification for the Preisach model remains a challenging issue. In this paper, an improved particle swarm optimization (IPSO) method is proposed to identify Preisach model parameters. Firstly, the Preisach model is established by introducing a Gaussian−Gaussian distribution function to replace density function. Secondly, the IPSO algorithm is adopted to Fimplement the parameter identification. Finally, the model parameter identification results are compared with the hysteresis loop of the piezoelectric actuator. Compared with the traditional Particle Swarm Optimization (PSO) algorithm, the IPSO algorithm demonstrates faster convergence, less calculation time and higher calculation accuracy. This proposed method provides an efficient approach to model and identify the Preisach hysteresis of piezoelectric actuators

Univariate Gaussian model for multimodal inseparable problems

It has been widely perceived that a univariate Gaussian model for evolutionary search can be used to solve separable problems only. This paper explores whether and how the univariate Gaussian model may also be used to solve inseparable problems. The analysis is followed up with experimental tests. The results show that the univariate Gaussian model stipulates no inclination towards separable problems. Further, it is revealed that the model is not only an efficient but also an effective method for solving multimodal inseparable problems. To verify its relative convergence speed, a restart strategy is applied to a univariate Gaussian model (the univariate marginal distribution algorithm) on inseparable problems. The results confirm that the univariate Gaussian model outperforms the five peer algorithms studied in this paper

Susceptibility of Chickens to Porcine Deltacoronavirus Infection

Porcine deltacoronavirus (PDCoV) is a novel swine enteropathogenic coronavirus with worldwide distribution. PDCoV belongs to the Deltacoronavirus (DCoV) genus, which mainly includes avian coronaviruses (CoVs). PDCoV has the potential to infect human and chicken cells in vitro, and also has limited infectivity in calves. However, the origin of PDCoV in pigs, the host range, and cross-species infection of PDCoV still remain unclear. To determine whether PDCoV really has the ability to infect chickens in vivo, the three lines of chicken embryos and specific pathogen free (SPF) chickens were inoculated with PDCoV HNZK-02 strain to investigate PDCoV infection in the current study. Our results indicated that PDCoV can infect chicken embryos and could be continuously passaged on them. Furthermore, we observed that PDCoV-inoculated chickens showed mild diarrhea symptoms and low fecal viral RNA shedding. PDCoV RNA could also be detected in multiple organs (lung, kidney, jejunum, cecum, and rectum) and intestinal contents of PDCoV-inoculated chickens until 17 day post-inoculation by real-time quantitative PCR (qRT-PCR). A histology analysis indicated that PDCoV caused mild lesions in the lung, kidney, and intestinal tissues. These results prove the susceptibility of chickens to PDCoV infection, which might provide more insight about the cross-species transmission of PDCoV