Unknown Sign Research Articles

Distributed adaptive fault-tolerant supervisory control for leader-following systems with actuator faults

This paper investigates an adaptive cooperative fault-tolerant supervisory control problem for nonlinear strict-feedback leader-following systems with unknown control coefficients and actuator faults under the fixed directed graph. Radial basis function neural networks are used to approximate system uncertainties. The Nussbaum gain technique is introduced to address unknown signs of control gains. Then, based on the dynamic surface control method, a distributed adaptive fault-tolerant control scheme is presented to compensate for the actuator faults of followers. For neural networks approximation theory, the unknown smooth function can only be approximated on a compact set due to the approximation errors. By theoretic analysis, the supervisory-based adaptive controllers are proposed to guarantee that system state signals can converge to compact sets and the leader-following systems can achieve the practical output consensus. Finally, the simulation results are provided to show the validity of the proposed consensus scheme.

Adaptive asymptotic fault-tolerant tracking of uncertain nonlinear systems with unknown control directions

In the article, the issues of asymptotic adaptive tracking control for the uncertain nonlinear systems in the presence of actuator faults and unknown control directions are investigated. By using the properties of the Nussbaum function and backstepping technique, the problems resulting from the unknown signs of the nonlinear control functions are circumvented successfully. Moreover, a new adaptive asymptotic tracking control method is presented with the fault-tolerant control framework, which is capable of realising zero-tracking performance. The stability of the controlled system is ensured through fractional Lyapunov stability analysis. Finally, the validity of the raised scheme is verified by a simulation example.

Uncertainties in the18F(p,α)15O reaction rate in classical novae

Context.Direct observation ofγ-ray emission from the decay of18F ejected in classical nova outbursts remains a major focus of the nuclear astrophysics community. However, modeling the abundance of ejected18F, and thus the predicted detectability distance of aγ-ray signal near 511 keV emitted from these transient thermonuclear episodes, is hampered by significant uncertainties in our knowledge of the key18F(p,α) reaction rate.Aims.We analyze uncertainties in the most recent nuclear physics experimental results employed to calculate the18F(p,α) reaction rate. Our goal is to determine which uncertainties have the most profound influence on the predicted abundance of18F ejected from novae in order to guide future experimental works.Methods.We calculated a wide range of18F(p,α) reaction rates using theR-Matrix formalism, which allowed us to take all interference effects into account. Using a selection of 16 evenly spaced rates over the full range, we performed 16 new hydrodynamic nova simulations.Results.We have performed one of the most thorough theoretical studies of the impact of the18F(p,α) reaction in classical novae to date. The18F(p,α) rate remains highly uncertain at nova temperatures, resulting in a factor of ∼10 uncertainty in the predicted abundance of18F ejected from nova explosions. We also found that the abundance of18F may be strongly correlated with that of19F.Conclusions.Despite numerous nuclear physics uncertainties affecting the determination of the18F(p,α) reaction rate, dominated by unknown interference signs between 1/2+and 3/2+resonances, future experimental work should focus on firmly and precisely determining the directly measurable quantum properties of the subthreshold states in the compound nucleus19Ne near 6.13 and 6.29 MeV.

A Proposed Geobiology-Driven Nomenclature for Astrobiological In Situ Observations and Sample Analyses.

As the exploration of Mars and other worlds for signs of life has increased, the need for a common nomenclature and consensus has become significantly important for proper identification of nonterrestrial/non-Earth biology, biogenic structures, and chemical processes generated from biological processes. The fact that Earth is our single data point for all life, diversity, and evolution means that there is an inherent bias toward life as we know it through our own planet's history. The search for life “as we don't know it” then brings this bias forward to decision-making regarding mission instruments and payloads. Understandably, this leads to several top-level scientific, theoretical, and philosophical questions regarding the definition of life and what it means for future life detection missions. How can we decide on how and where to detect known and unknown signs of life with a single biased data point? What features could act as universal biosignatures that support Darwinian evolution in the geological context of nonterrestrial time lines? The purpose of this article is to generate an improved nomenclature for terrestrial features that have mineral/microbial interactions within structures and to confirm which features can only exist from life (biotic), features that are modified by biological processes (biogenic), features that life does not affect (abiotic), and properties that can exist or not regardless of the presence of biology (abiogenic). These four categories are critical in understanding and deciphering future returned samples from Mars, signs of potential extinct/ancient and extant life on Mars, and in situ analyses from ocean worlds to distinguish and separate what physical structures and chemical patterns are due to life and which are not. Moreover, we discuss hypothetical detection and preservation environments for extant and extinct life, respectively. These proposed environments will take into account independent active and ancient in situ detection prospects by using previous planetary exploration studies and discuss the geobiological implications within an astrobiological context.

Prescribed Performance Synchronization Control of Chaotic Systems with Unknown Control Gain Signs

For a class of uncertain nonlinear chaotic systems with unknown control gain signs and saturated input, by means of Nussbaum function, a scheme of finite-time prescribed performance synchronization control is proposed. Here, Nussbaum function is used to eliminate the influence of unknown control gain signs, and fuzzy logic systems are used to estimate unknown functions. Lyapunov theory is used to prove that all synchronization errors converge to a predefined small performance range under the designed control method. Finally, simulation results are provided to illustrate the feasibility of the proposed method.

Fuzzy Adaptive Control of Uncertain MIMO Chaotic Systems with Unknown Control Direction

This paper presents an adaptive controller for MIMO chaotic systems with system uncertainties and unknown control direction. In the controller design, the matrix decomposition theory is used, and we decompose the control gain matrix into a positive matrix, a diagonal matrix whose diagonal entries are +1 or −1, and a unity upper triangular matrix. To handle the unknown control direction (i.e., the unknown sign of the control gain matrix), we use the Nussbaum-type function. In addition, we propose an adaptation law named proportional integral (PI) law to update the parameters of the fuzzy system. The stability of the controlled system is proven strictly. Finally, simulation results are presented.

Asymptotic consensus via switching adaptive for nonlinear multi‐agent systems subject to atypical disturbances

SummaryThe paper is devoted to the asymptotic output consensus for a class of uncertain nonlinear multi‐agent systems. The disturbance that the system suffered is said to be “atypical" since, unlike the related literature, it is unnecessarily continuously differentiable (e.g., sawtooth wave), and even permitted to be discontinuous (e.g., square wave), despite requiring it to be of unknown integer multiples of certain period. Typically, the multi‐agent systems admit more than one ingredient causing heterogeneities: (i) No identical requirement is made on the system nonlinearities and input directions (the unknown signs of the input coefficients). (ii) The orders of agent dynamics can be different (the dynamics are first‐order, second‐order, or even high‐order). Nevertheless, in the context, we still pursue the asymptotic output consensus, and unlike some related literature, instead of seeking a time‐varying strategy which means the introduction of infinitely large gains, we combine a refined switching strategy with continuous adaptive one together to propose a new consensus protocol. It turns out that under the proposed protocol, all the agent outputs reach a common value while all the closed‐loop signals are bounded. Simulation examples are given to demonstrate the effectiveness of the proposed protocol.

Neuro-learning-based adaptive control for state-constrained strict-feedback systems with unknown control direction

A neural networks (NNs)-based learning policy is proposed for strict-feedback nonlinear systems with asymmetric full-state constraints and unknown gain directions. A state-constrained function is introduced such that the proposed adaptive control policy works for systems with constraints or without constraints in a unified structure. Furthermore, the unified state-constrained function can also deal with symmetric and asymmetric constraints without changing adaptive structures, which also avoids discontinuous actions. With Nussbaum gain technique and NNs-based approximation technique, the proposed control method can also effectively deal with the unknown signs of control gains, and matched and mismatched uncertainties are also solved by NN approximation technique. According to the Lyapunov theory, the tracking errors can be proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally the effectiveness of the proposed scheme is validated by numerical simulations.

Magnetic ordering of Ho and its role in the magnetization reversal and coercivity double peaks in the Ho3Fe5O12 garnet

Rare-earth based garnet system, Ho3Fe5O12 shows magnetization compensation phenomenon at Tcomp = 138 K and a magnetic transition at 50 K. In the present work, we reveal the physics of these low temperature magnetic transitions by carrying out detailed dc magnetization, ac susceptibility, neutron depolarization, and neutron diffraction studies. Interestingly, our dc magnetization study under low (≲ 50 Oe) magnetic fields has shown a previously unknown sign reversal of magnetization (below the Tcomp) that persists down to 5 K, the lowest measured temperature. Magnetic measurements under moderate fields between 50 Oe and 1 kOe reveal two compensation temperatures (in the range of 105 – 150 K depending upon the applied field value) leading to double magnetization reversals. Interestingly, for magnetic fields ≥ 1 kOe, these two compensation temperatures merge to a single Tcomp (=138 K). A modified Stoner-Wohlfarth model has been used to explain the asymmetrical nature of double peak around the Tcomp in the temperature dependent coercivity data. The ac susceptibility study supports the existence of both the transitions with an evidence of deep minimum at the Tcomp (138 K) and a broad peak at 50 K. Mesoscopic neutron depolarization study has revealed a zero domain-magnetization state at the Tcomp. Temperature dependent neutron diffraction study has revealed that Ho carries an induced moment above the Tcomp due to polarizing effect under the internal magnetic field of the two magnetically ordered Fe sublattices at 567 K, whereas a single umbrella type ordering of Ho sublattice moments appears below the Tcomp, resulting in the distortion of magnetic unit cell from cubic to rhombohedral symmetry. However, below 50 K, Ho3+ sites are divided into two inequivalent magnetic sublattices, having different moments and canting angles, and it leads to double umbrella type magnetic structure. Neutron diffraction study has revealed an asymmetric variation of the tetrahedral, octahedral, and dodecahedral sublattice moments resulting in a magnetization reversal at ~ 138 K which is very well supported by the mean field theory calculations. In the present study, we have resolved the ambiguity of magnetic ordering of the rare-earth (Ho3+) sublattice and its role in multiple magnetic transitions. The utility of such compensated ferrimagnetic materials in spin polarizers/analyzers and fast switching memory devices has been emphasized.

Piezoresponse amplitude and phase quantified for electromechanical characterization

Piezoresponse force microscopy (PFM) is a powerful characterization technique to readily image and manipulate the ferroelectric domains. PFM gives an insight into the strength of local piezoelectric coupling and polarization direction through PFM amplitude and phase, respectively. Converting measured arbitrary units into units of effective piezoelectric constant remains a challenge, and insufficient methods are often used. While most quantification efforts have been spent on quantifying the PFM amplitude signal, little attention has been given to the PFM phase, which is often arbitrarily adjusted to fit expectations. This is problematic when investigating materials with unknown or negative sign of the probed effective electrostrictive coefficient or strong frequency dispersion of electromechanical responses, because assumptions about the PFM phase cannot be reliably made. The PFM phase can, however, provide important information on the polarization orientation and the sign of the effective electrostrictive coefficient probed by PFM. Most notably, the orientation of the PFM hysteresis loop is determined by the PFM phase. Moreover, when presenting PFM data as a combined signal, the resulting response can be artificially lowered or asymmetric if the phase data have not been correctly processed. Here, we explain the PFM amplitude quantification process and demonstrate a path to identify the phase offset required to extract correct meaning from the PFM phase data. We explore different sources of phase offsets including the experimental setup, instrumental contributions, and data analysis. We discuss the physical working principles of PFM and develop a strategy to extract physical meaning from the PFM amplitude and phase.

Comfort and health evaluation of live mutton sheep during the transportation based on wearable multi-sensor system

The uncertainty of transport environment threatens the comfort and health of live mutton sheep and even affects the quality of meat after slaughter. Using modern information technology to solve the deficiencies of live animal centralized transport is of great significance for the stable development of animal husbandry. In this paper, the wearable multi-sensor system was specially designed, and the system test experiment and transport monitoring experiment were carried out. According to the continuous and real-time data of environmental and physiological parameters obtained from the transport monitoring experiment, the optimization extraction, data collection analysis, correlation analysis and simulation analysis of the internal environment in the carriage were carried out, and the prediction model between environmental and physiological parameters based on generalized regression neural network (GRNN) and the prediction model of comfort and health evaluation based on back propagation neural network (BPNN) were established. The results show that: (1) The wearable multi-sensor system had high accuracy and stability of data collection, and the power consumption and communication performance can meet the monitoring requirements. (2) The observation values of internal environmental parameters increased gradually with the accumulation of transport time, and finally reached the state of dynamic balance. The heart rate fluctuated greatly and was higher than the normal range. The blood oxygen saturation showed a gradual decrease trend, but the overall was in the normal range. The body temperature gradually increased, and was affected by discomfort. (3) The correlation analysis showed that there was a significant correlation between environmental and physiological parameters. (4) The area with larger value of environmental parameters was close to the middle and front of the carriage, which was mainly caused by the high density and poor air circulation. (5) The prediction model between environmental and physiological parameters based on GRNN and the prediction model of comfort and health evaluation based on BPNN had high prediction accuracy, and the combination of the two models can map the impact of environmental factors on the change of vital signs to the relationship between comfort and health. Therefore, in the case of unknown vital signs information, only obtaining environmental information can also predict the health levels, which can provide decision-making basis for relevant practitioners.

Adaptive fuzzy fault-tolerant control using Nussbaum-type function with state-dependent actuator failures

This paper presents an adaptive fuzzy fault-tolerant tracking control for a class of unknown multi-variable nonlinear systems, with external disturbances, unknown control sign, and actuator faults. By employing fuzzy logic systems, the unknown nonlinear dynamics and the state-dependent actuator faults are approximated, and by utilizing a Nussbaum-type function, the issue of unknown control sign is solved. The proposed control scheme is based on two forms, an adaptive fuzzy controller along with a robust controller that is equipped with a Nussbaum-type gain function, which guarantees stability with the boundedness of all signals involved in the closed-loop system. To prove the accuracy, and the effectiveness of the proposed control scheme, a simulation example on two-inverted pendulums system is carried out.

Regularized variational data assimilation for bias treatment using the Wasserstein metric

AbstractThis article presents a new variational data assimilation (VDA) approach for the formal treatment of bias in both model outputs and observations. This approach relies on the Wasserstein metric, stemming from the theory of optimal mass transport, to penalize the distance between the probability histograms of the analysis state and an a priori reference dataset, which is likely to be more uncertain but less biased than both model and observations. Unlike previous bias‐aware VDA approaches, the new Wasserstein metric VDA (WM‐VDA) treats systematic biases of unknown magnitude and sign dynamically in both model and observations, through assimilation of the reference data in the probability domain, and can recover the probability histogram of the analysis state fully. The performance of WM‐VDA is compared with the classic three‐dimensional VDA (3D‐Var) scheme for first‐order linear dynamics and the chaotic Lorenz attractor. Under positive systematic biases in both model and observations, we consistently demonstrate a significant reduction in the forecast bias and unbiased root‐mean‐squared error.

Novel prescribed performance-tangent barrier Lyapunov function for neural adaptive control of the chaotic PMSM system by backstepping

This paper investigates a neural adaptive output tracking prescribed performance control of the chaotic permanent magnet synchronous motor system with output-constrained, time-delays, parameter perturbations and external disturbances. Firstly, a novel prescribed performance-tangent barrier Lyapunov function is created to tackle the prespecified constraints of tracking error and output. Subsequently, backstepping controllers have been suggested. In specific steps, Chebyshev neural networks and the Nussbaum-type function are exploited to solve unknown nonlinearities and the unknown gain sign. Time-delays are tackled by constructing Lyapunov–Krasovskii functions (LKFs). Meanwhile, the “explosion of complexity” caused by the differentiation of virtual input is removed by plugging tracking differentiator into the controller. Furthermore, it is demonstrated that all the closed-loop signals are ultimate boundedness and predefined constraints are not violated. Finally, simulation studies were performed to confirm the validities and robustness of the raised scheme.

Effective nonlinearity of the new quaternary chalcogenide crystal BaGa2GeSe6.

The unknown magnitude and relative signs of the trigonal crystal ${\rm BaGa}_{2}{\rm GeSe}_{6}$BaGa2GeSe6 nonlinear coefficients have hindered defining an optimum orientation for maximum frequency conversion efficiency. This issue is resolved in the present work, obtaining ${d_{11}}= + {23.6}$d11=+23.6, ${d_{22}}= - {18.5}$d22=-18.5, and ${d_{31}}= + {18.3}\;{\rm pm/V}$d31=+18.3pm/V for frequency doubling of the 10.591 µm radiation.

Cooperative Neuro Adaptive Control of Leader Following Uncertain Multi-Agent Systems with Unknown Hysteresis and Dead-Zone

In this paper, a cooperative adaptive control of leader-following uncertain nonlinear multiagent systems is proposed. The communication network is weighted undirected graph with fixed topology. The uncertain nonlinear model for each agent is a higher-order integrator with unknown nonlinear functions, unknown disturbances and unknown input actuators. Meanwhile, the gains of input actuators are unknown nonlinear functions with unknown sign. Two most common behaviors of input actuators in practical applications are hysteresis and dead-zone. In this paper, backlash-like hysteresis and dead-zone are used to model the input actuators. Using universal approximation theorem proved for neural networks, the unknown nonlinear functions are tackled. The unknown weights of neural networks are derived by proposing appropriate adaptive laws. To cope with modeling errors and disturbances an adaptive robust structure is proposed. Considering Lyapunov synthesis approach not only all the adaptive laws are derived but also it is proved that the closed-loop network is cooperatively semi-globally uniformly ultimately bounded (CSUUB). In order to investigate the effectiveness of the proposed method, it is applied to agents modeled with highly nonlinear mathematical equations and inverted pendulums. Simulation results demonstrate the effectiveness and applicability of the proposed method in dealing with both numerical and practical multi-agent systems.

Adaptive iterative learning consensus control for second-order multi-agent systems with unknown control gains

We respond to the consensus control algorithms for second-order non-linear MAS with unknown control gains in the finite time interval by adopting iterative learning control (ILC) methods in this paper. Compared to the current findings, the control gains in the proposed method are unknown functions with unknown and non-identical signs. The topology graph between the follower agents is an undirected connected graph. By referencing the Nussbaum-type function and the neural networks, the adaptive control algorithms are intended to cope with the consensus control between the agents on the limited time interval. Simulation results illustrate the efficacy of the raised algorithms.

Minimax Adaptive Control for State Matrix with Unknown Sign

For linear time-invariant systems having a state matrix with uncertain sign, we formulate and solve a minimax adaptive control problem as a zero sum dynamic game. Explicit expressions for the optimal value function and the optimal control law are given in terms of a Riccati equation. The optimal control law is adaptive in the sense that past data is used to estimate the uncertain sign for prediction of future dynamics. Once the sign has been estimated, the controller behaves like standard H∞ optimal state feedback.

A Robust Sensorless Controller-Observer Strategy for PMSMs with Unknown Resistance and Mechanical Model

In this work, we present a mixed sensorless strategy for Permanent Magnet Synchronous Machines, combining a torque/current controller and an observer for position, speed, flux, and stator resistance. The proposed co-design is motivated by the need for an appropriate signal injection technique to guarantee full state observability. Neither the typical constant or slowly-varying speed assumptions, nor a priori mechanical model information are required. Instead, the rotor speed is modeled as an unknown input disturbance with constant (unknown) sign and uniformly non-zero magnitude. With the proposed architecture, we show that the torque tracking and signal injection tasks can be achieved and asymptotically decoupled. Because of these features, we refer to this strategy as a sensorless controller-observer with no mechanical model. Employing a gradient descent resistance/back-EMF estimation, combined with the unit circle formalism to describe the rotor position, we prove regional practical asymptotic stability of the overall scheme. In particular, the domain of attraction can be arbitrarily large, without including a lower-dimensional manifold. The effectiveness of this design is further validated with numerical simulations, related to a challenging application of UAV propellers control.

A Semi-global Hybrid Sensorless Observer for Permanent Magnet Synchronous Machines with Unknown Mechanical Model

In this paper, we present a hybrid sensorless observer for Permanent Magnets Synchronous Machines, with no a priori knowledge of the mechanical dynamics and without the typical assumption of constant or slowly-varying speed. Instead, we impose the rotor speed to have a constant (unknown) sign and a non-zero magnitude at all times. For the design of the proposed scheme, meaningful Lie group formalism is adopted to describe the rotor position as an element of the unit circle. This choice, however, leads to a non-contractible state space, and therefore it introduces topological constraints that complicate the achievement of global/semi-global and robust results. In this respect it is shown that the proposed observer, which employs a clock to periodically reset the estimates, is semi-globally practically asymptotically stable, and thus it improves a continuous-time version designed under the same assumptions. As highlighted in the simulation results, the novel hybrid strategy leads to enhanced transient performance, notably without any modification of the gains employed in the continuous-time solution. These features motivate to augment the observer with a discrete-time identifier, leading to significantly faster rotor flux reconstruction.