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In this paper, the issues of both fault estimation and accommodation are studied for a class of continuous-time Markov jump linear systems under actuator fault, sensor fault and external disturbance in the framework of finite frequency domain. The imprecise statistic of modes transitions are considered here, which means that the transition rates are uncertain. Firstly, the sensor fault is defined as a new state, and then an adaptive observer is developed to estimate the actuator fault and the newly defined state with the aid of the descriptor system approach. By using the obtained fault estimation, a novel fault accommodation scheme is proposed based on the sample point controller design approach to satisfy the stochastic stability requirement of the closed-loop system with a prescribed H ∞ performance bound. Moreover, for the convenience of design, the presented sample point controller design is successfully transformed into resolving a class of input-delay control issues. Finally, two numerical examples, including a practical F-404 aircraft engine system, are illustrated to show the effectiveness and applicability of the developed design methods.

This paper proposes an adaptive neural network-based finite-time impedance control method for constrained robotic manipulators with disturbance observer. Firstly, by combining barrier Lyapunov functions with the finite-time stability control theory, the control system has a faster convergence rate without violating the full state constraints. Secondly, the adaptive neural network is introduced to approximate the unmodeled dynamics and a disturbance observer is designed to compensate for the unknown time-varying disturbances. Then, the command filtered control technique with error compensation mechanism is used to deal with the explosion of complexity of traditional backstepping and improve the control accuracy. The simulation results show the effectiveness of the proposed control method.

This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the “explosion of complexity” problem in the backstepping method is overcome and the $ {L}_{\infty }$ tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm.

Focusing on the nonlinear time-delay systems actuated by smart-material-based actuators, an adaptive dynamic surface estimated inverse motion control scheme is proposed in this article. The asymmetric hysteresis nonlinearity is counteracted by constructing its evaluated inverse compensator. A state estimator is constructed to estimate the unmeasurable states and at the same time to cope with external disturbances. The assumptions on time-delay functions are removed, and the unknown time-delay function is online evaluated by combining the fuzzy logic system with a finite covering lemma. Also, the prespecified tracking performance is achieved by utilizing an error-transformed function. Experimental results of the proposed controller are performed on the piezoelectric positioning stage to verify the effectiveness of the control scheme.

It has been shown that the closed-loop transfer function representation of the unconstrained model predictive control (MPC) for single input single output systems is similar to a two-degree-of-freedom controller, which is capable of improving the tracking performance of positioning systems. Conventionally, the optimal parameters of the above-mentioned transfer function are result of a quite complicated MPC tuning. This paper proposes a new approach to obtain the parameters directly instead, where the input/output constraints are not considered. The method combines conventional feedback and adaptive feedforward techniques to minimize the tracking error as well as mitigate the influence of the load disturbance. Experiments on an ultra precision positioning system actuated by piezoelectric actuator show that the proposed method achieves much better tracking performance over a well-tuned conventional MPC.

Aims: There is a spatially resolved star-forming main sequence (rSFMS) and mass-metallicity relation (rMZR) of galaxies in local universe. We know that the global mass-metallicity relation (MZR) results from the integral of rMZR, and it will evolve with the redshift. However, the evolution of rMZR with redshift is still unclear due to the low spatial resolution and signal-to-noise ratio. There are currently too few observations beyond local universe, and only simulations can reproduce the evolution of rMZR with redshift. Methods: In this work, we select ten emission-line galaxies with an average redshift of $z\sim 0.26$ from MUSE-Wide DR1. We obtain the spatially resolved star formation rate (SFR) and metallicity from the integral field spectroscopy (IFS), as well as the stellar mass surface density from the 3D-HST photometry. We derive the rSFMS and rMZR at $z\sim 0.26$ and compare them with local galaxies. Results: We find the rSFMS of galaxies at $z\sim 0.26$ has a slope of $\sim$0.771. The rMZR exists at $z\sim 0.26$, showing a similar shape to the local universe but a lower average metallicity about $\sim$0.11 dex than the local one. In addition, we also study their spatially resolved fundamental metallicity relation (rFMR). However, there is no obvious evidence that rFMR exists at $z\sim$0.26 and it is not an extension of rMZR at a high SFR. Conclusions: Similar to their global versions, the rSFMS and rMZR of galaxies also evolve with redshift. Given the fixed stellar mass, galaxies at higher redshift show higher SFR and lower metallicity. These suggest that the evolution of the global galaxy properties with redshift may result from integrating the evolution of spatially resolved properties of galaxies., Comment: 9 pages, 8 figures, accepted for publication in A&A

Low-mass compact stellar systems (CSSs; $M_{\star}$ $, 8 pages, 5 figures, published in ApJL

This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small ${L}_{{\infty }}$ tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.

We use 789 disk-like, star-forming galaxies (with 596 HI detections) from HI follow-up observations for the SDSS-IV MaNGA survey to study the possible role of inner HI gas in causing secondary dependences in the mass-gas-phase metallicity relation. We use the gas-phase metallicity derived at the effective radii of the galaxies. We derive the inner HI mass witHIn the optical radius, but also use the total HI mass and star formation rate (SFR) for a comparison. We confirm the anticorrelation between the total HI mass and gas-phase metallicity at fixed stellar mass, but the anticorrelation is significantly strengthened when the total HI mass is replaced by the inner HI mass. Introducing a secondary relation with the inner HI mass can produce a small but noticeable decrease (16%) in the scatter of the mass-gas-phase metallicity relation, in contrast to the negligible effect with the SFR. The correlation with the inner HI mass is robust when using different diagnostics of metallicity, but the correlation with SFR is not. The correlation with the inner HI mass becomes much weaker when the gas-phase metallicity is derived in the central region instead of at the effective radius. These results support the idea that the scatter in the mass-metallicity relation is regulated by gas accretion, and not directly by the SFR, and stress the importance of deriving the gas mass and the metallicity from roughly the same region. The new relation between inner HI mass and gas-phase metallicity will provide new constraints for chemical and galaxy evolution models., 14 pages, 8 figures; accepted for publication in ApJ

We present a study of the size--mass relation for local post-starburst (PSB) galaxies at $z\lesssim0.33$ selected from the Sloan Digital Sky Survey Data Release 8. We find that PSB galaxies with stellar mass ($M_*$) at $10^9~M_{\odot}, Comment: 16 pages, 7 figures; accepted for publication in ApJ

The scaling relationship is a fundamental probe of the evolution of galaxies. Using the integral field spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory survey, we select 1698 spaxels with significant detection of the auroral emission line \oiii$\lambda$4363 from 52 galaxies to investigate the scaling relationships at the low-metallicity end. We find that our sample's star formation rate is higher and its metallicity is lower in the scaling relationship than the star-forming sequence after removing the contribution of the Fundamental Metallicity Relation.We also find that the stellar ages of our sample are younger ($, Comment: 12 pages, 10 figures, accepted in ApJ

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

We study the relation between the properties of the bulge/disc components and the HI mass fraction of galaxies. We find that at fixed stellar mass, disc colours are correlated with the HI mass fraction, while bulge colours are not. The lack of a correlation between the bulge colour and the HI mass fraction is regardless whether the bulges are pseudo, or whether the galaxies host bars or are interacting with a neighbour. There is no strong correlation between the colours of the discs and bulges either. These results suggest that the current total amount of HI is closely related to the formation of discs, but does not necessarily fuel the formation of (pseudo) bulges in an efficient way. We do not find evidence for the star formation in the discs to be quenched by the bulges., Accepted by MNRAS, 13 pages, 9 figures

For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a self-tuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.

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 $\left \Vert{ x(t_{0})}\right \Vert \leq a\Rightarrow \left \Vert{ x(t)}\right \Vert \leq b$ , $\forall t\geq t_{0}+T$ .

In this paper, the constrained control problem of the prescribed performance control technique is discussed in discrete-time domain for single input-single output dynamical systems. The goal of this design is to maintain the tracking error trajectory in a predefined convergence zone described by a performance function in the presence of the uncertainties. In order to achieve this goal, the discrete-time quasi-sliding-mode control with a new sliding variable is proposed. Additionally, by using a modified convergence zone, the transient response of the system can be adjusted without producing the offset error in the steady state. The effectiveness of the proposed method is confirmed by experiments on a piezo-actuated positioning system.

The piezoelectric actuators (PEAs) with many advantages such as sub-nanometers resolution, low power consumption, quick response, high stiffness, etc., have been widely used in micro/nano positioning systems which play a very important role in many key technologies recently. To achieve high precision in tracking applications based on PEAs, advanced control techniques are needed to cope with the nonlinearity exhibiting between the input voltage and output displacement of the PEAs. This paper proposes a new approach to get high tracking performance by combining discrete-time quasi-sliding mode control (DQSMC) and prescribed performance control (PPC) techniques. The effectiveness of the proposed method is verified by experiments on a piezo-actuated positioning system.

This paper considers the problem of global adaptive stabilization for strict feedback nonlinear systems with time delay and parameter uncertainty. By designing a delay-independent reduced-order observer with two dynamic gains, we propose a universal adaptive control strategy to reduce the conservatism of the growth conditions imposed on nonlinearities. With the help of Lyapunov–Krasovskii functionals, stability analysis is subtly conducted. Finally, the proposed controller is applied to a chemical reactor system to illustrate the usefulness of our results.

The 11 papers in this special section focus on sensing and perception systems for intelligent manufacturing. This new industrial revolution calls for solutions to embedded intelligence, networked environment, interaction, autonomous, and high performance computing. The manufacturing industry is at the center of this wave of innovation with sensing and perception in focus. The papers included in this section examine the methodologies developed are all based on processing data generated by complex sensing devices acting on certain kind of physical fields, such as vision and temperature field, in order to render 2-D or 3-D information of fields. This kind of field sensing and perception development is the key to facilitate the intelligent operation of manufacturing systems in practices.

Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5\sigma$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}., Comment: published in ApJS

This paper discusses the modeling and control of the ionic polymer-metal composite (IPMC) actuators which have many promising applications in biomechatronics. A novel mathematical model in continuous-time domain of the IPMC actuator, being a stable second-order dynamical system preceded by a nonlinear hysteresis representation, is proposed. An adaptive controller is formulated for the IPMC actuator based on the proposed model. The proposed adaptive control law ensures the global stability of the controlled IPMC system, and the position error of the IPMC actuator can be theoretically guaranteed to converge to zero. The effectiveness of the proposed model and the superiority of the proposed control to the traditional proportional-integral-derivative control are verified by experimental results.

The micro/nano positioning system discussed in this paper includes a piezoelectric actuator (PEA) and flexure-hinge-based positioning mechanism. Due to the existence of the hysteretic nonlinearity in the PEA and the friction in the system, the accurate positioning of the piezo-actuated positioning system calls applicable control schemes for practical applications. To this end, an implementable adaptive controller is developed in the paper, where a minimized parameterization hysteresis model is employed to reduce the computational load. The formulated adaptive control law guarantees the global stability of the controlled positioning system, and the positioning error can approach to zero asymptotically. The advantages of the proposed method making on-line implementation feasible are that the traditional inversion of the hysteresis does not need to be constructed directly; the real values of the parameters of the positioning system neither need to be identified nor measured; only the parameters in the formulation of the controller are estimated online. Comparison with the feedforward plus proportional-integral feedback control scheme is conducted and experimental results show the effectiveness of the proposed method.