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The typical characteristic of a magnetic shape memory alloy (MSMA) based actuator is rate-dependent and stress-dependent hysteresis. In addition, the hysteresis in an MSMA-based actuator includes memory, multi-valued mapping, and asymmetry properties, which seriously degrade the positioning accuracy. In this study, a feedforward neural network (FNN) based nonlinear autoregressive moving average with exogenous inputs (NARMAX) model is employed to describe the hysteresis in an MSMA-based actuator. Through a Taylor expansion, it is revealed how the FNN constructs the nonlinear function of the NARMAX model. For the controller implementation, an FNN-based iterative learning control (ILC) strategy is proposed to achieve the desired tracking performance. The contributions of this study include exploring the update rule of the FNN-based ILC and analyzing the convergence properties of the proposed controller when the initial state of the system varies. To verify the effectiveness of the proposed method, experiments on an MSMA-based actuator were conducted. The results illustrate that the proposed modeling and control methods achieve an excellent performance and that the theoretical results are correct.

Magnetic shape memory alloy-based actuator (MSMA-BA) has the capability to generate the micro–nano-scale precision positioning, which has unparalleled virtue in contrast with the traditional motion driving mechanism. Nevertheless, the output displacement of the MSMA-BA exhibits complex hysteresis nonlinearity, which hinders its utility in the high-precision positioning field. In this article, an online Volterra series model based on wavelet neural network (VSM-WNN) is proposed to describe the rate-dependent hysteresis of the MSMA-BA. The kernel function of the VSM is extracted with a WNN, in which the feasibility of this extraction is proved in theory via Taylor expansion of the wavelet function. Finally, the validity of the proposed model is confirmed by means of experiments. Experimental results indicate that the VSM-WNN has a remarkable performance in describing the traits of hysteresis behavior of the MSMA-BA.

This paper first presents a rate-dependent Krasnosel’skii-Pokrovskii (RKP) model to capture the hysteresis of piezo-nanopositioning stages. The dynamic density function of the RKP model is obtained via neural network with frequency behavior input signal. Under the persistently exciting condition, the convergence of the neural network with Krasnosel’skii-Pokrovskii (KP) operators is proved rigorously. In order to address the hysteresis issue, a direct compensation control (DCC) approach with the KP compensation operator is proposed, where its dynamic density function is same as that of the RKP model. Some experiments with different reference signals are conducted to verify the effectiveness of the proposed modeling and DCC method on piezo-nanopositioning stages.

The magnetic shape memory alloy (MSMA) is a new family of smart materials, which exhibits great strain deformation and high energy density. Based on these properties, the MSMA has excellent potential to represent an available means for developing a novel generation of actuators in the micro-positioning application. However, the MSMA-based actuator suffers from the inherent hysteresis and it has become a bottleneck in the industrial application. A hybrid hysteresis model, which consists of a simple dynamic hysteresis operator (SDHO) and chaotic neural network (CNN), is proposed in this article. This developed model possesses a concise construction and distinguished generalization capability. By conducting comparative experiments, the proposed approach has a superior ability to predict the hysteresis behaviors under various input signals.

The magnetic shape memory alloy (MSMA)-based actuator, as a new type of actuator, has a great application prospect in the micro-precision positioning field. However, the input-to-output hysteresis nonlinearity largely hinders its wide application. In this paper, a Takagi–Sugeno fuzzy neural network (TSFNN) model based on the modified bacteria foraging algorithm (MBFA) is innovatively utilized to describe the complex hysteresis nonlinearity of the MSMA-based actuator, and the parameters of TSFNN are optimized by the MBFA. The TSFNN is a combination of the fuzzy-logic system and neural network; thus, it has the capability of approximating the nonlinear mapping function and self-adjustment and is suitable for hysteresis modeling. The MBFA, which can obtain better optimization values, is employed for the parameter identification procedure. To demonstrate the effectiveness of the proposed model, a TSFNN based on the gradient descent algorithm (GDA) is used for comparison. Experimental results clearly show that the proposed modeling method can accurately describe the hysteresis nonlinearity of the MSMA-based actuator and has significance for its future application.

A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

The piezoelectric micro-positioning platform(PMP) has advantages in high displacement resolution and fast response, however, the serious hysteresis nonlinearity in the PMP restricts its positioning accuracy. This paper presents a discretization of Krasnosel'skii-Pokrovskii model to describe the hysteresis nonlinearity of the PMP. Then, the density function is identified online by the adaptive linear neural network. To compensate the intrinsic hysteresis nonlinearity in the PMP, a feedforward compensation control method is implemented by an innovative iterative learning control algorithm. Moreover, a hybrid control method based on iterative learning and fractional order PID control is proposed to further improve the control accuracy. A series of comparative experiments are carried out to validate the feasibility and effectiveness of the proposed control method.

Magnetic shape memory alloy (MSMA) based actuators are extensively applied in the fields of precision manufacturing and micro/nano technology. Nevertheless, the inherent hysteresis in the MSMA-based actuator severely hinders its further application. In this letter, the characteristics of hysteresis behavior under different input signals are investigated. Then, a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model based on a diagonal recurrent neural network (DRNN) is used to construct the rate-dependent hysteresis model. To improve the capability of characterizing the multi-valued mapping of the hysteresis loop, the play operator is adopted as the exogenous variable function of the NARMAX model. To verify the effectiveness of the proposed model, a series of comparisons are implemented. The experimental results show that the proposed NARMAX model based on the DRNN exhibits excellent modeling performance.

Grey wolf optimizer (GWO) is an up-to-date nature-inspired optimization algorithm which has been used for solving many of the real-world applications since it was proposed. In the standard GWO, individuals are guided by the three dominant wolves alpha, beta, and delta in the leading hierarchy of the swarm. These three wolves provide their information about the potential locations of the global optimum in the search space. This learning mechanism is easy to implement. However, when the three wolves are in conflicting directions, an individual may not obtain better knowledge to update its position. To improve the utilization of the population knowledge, in this paper, we proposed a grey wolf optimizer based on the dimensional learning strategy (DLGWO). In the DLGWO, the three dominant wolves construct an exemplar wolf through the dimensional learning strategy (DLS) to guide the grey wolves in the swarm. Thereafter, to reinforce the exploration ability of the algorithm, the Levy flight is also utilized in the proposed method. 23 classic benchmark functions and engineering problems are used to test the effectiveness of the proposed method against the standard GWO, variants of the GWO, and other metaheuristic algorithms. The experimental results show that the proposed DLGWO has good performance in solving the global optimization problems.

It is of practical significance for rural revitalization to clarify the gap in resilience development among different rural areas and improve the ability to cope with external interference. Combined with the strategic policy of rural revitalization in China, the evaluation index system of rural comprehensive resilience was constructed from the five dimensions of productive resilience, ecological resilience, social resilience, institutional resilience, and economic resilience. The advantages and disadvantages of rural development are determined based on a quantitative evaluation of the comprehensive resilience of rural development. This could provide a reference for decision making in rural development. This study uses the rural statistical data of nine cities (prefectures) in Jilin Province in 2019 and 2020 as an example as well as the entropy weight method to evaluate the impact of COVID-19 on rural resilience development. The results showed that the pandemic situation has an obvious impact on rural economic resilience; rural areas with high ecological resilience have a strong ability to cope with the panidemic situation; and rural areas with excellent ecological environment resources have strong comprehensive resilience.

This paper presents the precision tracking control for a piezoelectric-driven micropositioning stage (PDMS) by resorting to a new adaptive output feedback control strategy. The dynamics model with the hysteresis state is established for the PDMS, where the Duhem model is used to illustrate the hysteresis behavior. Considering that the information of the hysteresis state is unknown in practice, a new extended hysteresis observer (NEHO) is proposed to estimate this state. With the help of the Takagi-Sugeno fuzzy neural network (TSFNN) approximator, the parameter identification of the PDMS can be circumvented. Then, based on the proposed NEHO, a new adaptive output feedback control (NEHO-AOFC) framework combining the dynamic surface control (DSC) and sliding mode control (SMC) is introduced, and the asymptotic stability of the closed-loop system is proved in theory. Finally, a series of comparative experiments are carried out on the PDMS to demonstrate the effectiveness of the constructed controller.

In this paper, a RBFNNs based output feedback adaptive dynamic surface sliding mode control (DSSMC) method is proposed for a class of uncertain nonlinear system with actuator hysteresis. The main contributions are listed as follows: 1) by comprehensively considering the mutual influence between the tracking errors of each subsystem, a sliding mode dynamic surface is defined. Also, the recursive method is used to design the control law, which reduces the computational burden; 2) by introducing the PI hysteresis model and the state observer, the hysteresis behavior exists in the actuators can be mitigated when only the system output is measurable; 3) by using RBFNNs, the unknown uncertainties and external disturbances can be estimated. It is proved by Lyapunov method that all signals in the closed-loop system are semi-globally uniformly bounded. The simulation results verify the effectiveness of the proposed control method.

The desired fuel rail pressure is a crucial factor for guaranteeing the gasoline direct injection (GDI) engine to work stably. In order to solve the rail pressure control problem, the detailed nonlinear model of GDI is derived and reasonable simplification of this model is carried out for the following controller design. Terminal sliding mode control strategy is proposed to design the rail pressure controller with Lyapunov stability. The designed approach with the fast terminal sliding mode surface makes the system have the capacity of global fast convergence and achieves precise tracking control. To demonstrate the validity of the designed control method, simulations are conducted by tracking the different reference rail pressures. Results show that the designed controller tracks the given reference accurately and has strong robustness.

As the main regulator of microbial community composition, bacteriophages exist widely on Earth. However, since they are hidden in metagenomes, most of them are unknown. To identify phages from metagenomes more effectively, a new tool named VFM (Virus Finding & Mining) is presented in this paper. VFM has two versions, i.e., bin-VFM and unbin-VFM. Eighteen new features describing the codon usage bias, the proportion of hits of clusters of orthologous groups of proteins (COG), and 1-mer and 2-mer frequency are introduced to improve the performance of the classifiers. By using missing value interpolation, bin-VFM improves the classification performance for short sequence bins significantly. Compared with previous tools for virus mining, bin-VFM and unbin-VFM perform much better for simulated and real metagenomes with short and long sequences respectively. Thus, VFM may play a helpful role in studies of metagenome-related problems, such as horizontal gene transfer and antibiotic resistance. VFM is freely available at https://github.com/liuql2019/VFM.

As a core component in the micro/nano-manufacturing, the piezo-actuated stage has superior performance of ultra-precision positioning, fast response and compact size. However, the inherent nonlinearity, such as hysteresis and creep characteristics, in the piezo-actuated stage impacts its positioning precision severely. To eliminate the impact of the nonlinearity of a piezo-actuated stage, a data driven adaptive control method with hysteresis input is proposed in this paper. An unknown nonlinear function with hysteresis input is adopted to depict the piezo-actuated stage. Then, the undetermined parameters of the controller are adjusted online by using the input and output measured data. Finally, the availability of the proposed control method have been demonstrated by a series of experiments compared with a hysteresis inverse compensation-based model reference adaptive control method and a classical model free adaptive control method.

Hysteresis is a nonlinear characteristic ubiquitously exhibited by smart material sensors and actuators, such as piezoelectric actuators and shape memory alloys. The Prandtl–Ishlinskii (PI) operator is widely used to describe hysteresis of smart material systems due to its simple structure and the existence of analytical inverse. A PI operator consists of a weighted superposition of play (backlash) operators. While adaptive estimation of the weights for PI operators has been reported in the literature, rigorous analysis of parameter convergence is lacking. In this article, we establish persistent excitation and thus parameter convergence for adaptive weight estimation under a rather modest condition on the input to the PI operator. The analysis is further supported via simulation, where a recursive least square (RLS) method is adopted for parameter estimation.

The magnetic shape memory alloy (MSMA)-based actuator is a promising candidate in the micro positioning field by virtues of its large stroke and small volume. However, the inherent hysteresis nonlinearity between the input current and the output displacement seriously limited the application of the MSMA-based actuator. In this paper, the hysteresis, which is related to the input frequency and working condition (such as load), is analyzed. Then a mathematical modeling method using Duhem model (DM) and Takagi-Sugeno fuzzy neural network (TSFNN) is introduced to describe the hysteresis behavior. The mathematical expression of the DM is explicit and simple; and the TSFNN, which has the advantages of both fuzzy system and NN structure, is used to identify the DM parameter. Hence, the proposed TSFNN-DM method has the merits of self adjustment and clear expression. To certify the validity of the developed model, comparative experiments with the modeling methods in other literatures are executed. Experimental results confirm that the TSFNN-DM has the better modeling performance to depict the hysteresis under the different input frequencies and loads than other modeling methods in previous studies.

As a kind of smart material-based product, the magnetic shape memory alloy based actuator (MSMABA) is widely used in nano-positioning applications. However, the serious hysteresis in the MSMABA severely impacts its application in the field of micro/nanotechnology. To model the hysteresis of the MSMABA which possesses the multi-valued mapping characteristics and the rate-dependent behavior, a nonlinear auto-regressive moving average with exogenous inputs structure (NARMAXS) model is developed. In this method, the Bouc-Wen (BW) model as an exogenous variable function (EVF) is used to construct the NARMAXS model, and the proposed model can be applied to describe the behavior of hysteresis with multi-valued mapping. Based on the powerful function approaching capability, a wavelet neural network (WNN) is used to replace the nonlinear function of the NARAMXS model. To verify the effectiveness of the proposed method, the NARAMX model based on the radial basis function neural network (RBFNN) is used for comparison. The experiment results illustrate that the proposed NARMAXS model presents excellent modeling ability for hysteresis of the MSMABA.

In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: 1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the “explosion of complexity” problem in backstepping method and the complexities introduced by SVC are overcome; 2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small $\mathcal {L}_{\infty }$ a tracking performance; 3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly bounded b and can be made arbitrarily small. Simulation results show the validity of the proposed method. a Here, the $\mathcal {L}_{\infty }$ norm is defined as $\left \Vert{ x}\right \Vert _{\infty }\mathop{=}\limits^{\Delta } \sup \limits_{t\geq 0}\left \vert{ x(t)}\right \vert $ and we say $x\in \mathcal {L}_{\infty }$ when $\left \Vert{ x}\right \Vert _{\infty }$ exists. b Here, we say $x(t)$ is ultimately uniformly bounded if there exist positive constants $b$ and $c$ , independent of $t_{0}\geq 0$ , and for every $a\in (0,c)$ , there is $T=T(a,b)$ , independent of $t_{0}$ , such that $\left \Vert{ x(t_{0})}\right \Vert \leq a\Rightarrow \left \Vert{ x(t)}\right \Vert \leq b$ , $\forall t\geq t_{0}+T$ .

This paper proposes an integral sliding mode control with a novel model-based iterative feedforward compensating the hysteresis to finish high-precision positioning control of the piezoactuated stage. The Bouc-Wen model is established to design a novel iterative feedforward control (IFC) method. The IFC reduces the effects of inherent hysteresis on the positioning precision of the piezo-actuated stage. The positioning error of the IFC is inevitable because of the model uncertainty. Subsequently, an integral sliding mode control algorithm is implemented to further decrease the tracking error and enhance the anti-disturbance capability of the IFC. To reduce the chattering of the proposed controller, a boundary layer method based on Fal-function is designed. The stability of the integral sliding mode control is analyzed via Lyapunov stability theory. The feasibility of the proposed controller is certified by a series of simulation results. Compared with the IFC and conventional sliding mode control (CSMC) method, the proposed controller has higher performance in terms of the positioning precision of piezo-actuated stages.