Experimental investigation on plasma jet deflection with magnetic fluid control based on PIV measurement

This paper is devoted to experimentally investigating the influence of magnetic field intensity and gas temperature on the plasma jet deflection controlled by magneto hydrodynamics. The catalytic ionization seed CS_2CO_3 is injected into combustion gas by artificial forced ionization to obtain plasma fluid on a high-temperature magnetic fluid experimental platform. The plasma jet was deflected under the effect of an external magnetic field, forming a thrust-vector effect.Magnesium oxide was selected as a tracer particle, and a two-dimensional image of the jet flow field was collected using the particle image velocimetry(PIV) measurement method. Through image processing and velocity vector analysis of the flow field, the value of the jet deflection angle was obtained quantitatively to evaluate the thrust-vector effect. The variation of the jet deflection angle with the magnetic field intensity and gas temperature was studied under different experimental conditions. Experimental results show that the jet deflection angle increased gradually with a rise in gas temperature and then increased substantially when the gas temperature exceeded 2300 K. The jet deflection angle also increased with an increase in magnetic induction intensity. Experiments demonstrate it is feasible to use PIV test technology to study the thrust vector under magnetic control conditions.

Asymptotic attenuation of a class of nonlinear systems with unknown sinusoidal disturbances

In this paper,nonlinear observers are incorporated into the adaptive control to synthesize controllers for a class of uncertain nonlinear systems with unknown sinusoidal disturbances which are presented in matched and unmatched forms.In addition to magnitudes and phases,frequencies of the sinusoidal disturbances need not be known as well,so long as the overall order is known.Nonlinear observers are constructed to eliminate the effect of unknown sinusoidal disturbances to improve the steady-state output tracking performance-asymptotic output tracking is achieved.The adaptation law is used to obtain the estimate of all unknown parameters.The presented disturbance decoupling algorithms can deal with matched and unmatched unknown sinusoidal disturbances.

Few-shot node classification via local adaptive discriminant structure learning

Node classification has a wide range of application scenarios such as citation analysis and social network analysis.In many real-world attributed networks,a large portion of classes only contain limited labeled nodes.Most of the existing node classification methods cannot be used for few-shot node classification.To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node classification.LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlargingginter-classdifferences.Extensiveexperiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

Immersion and invariance adaptive control of a class of continuous stirred tank reactors

An immersion and invariance （l＆l） manifold based adaptive control algorithm is presented for a class of continuous stirred tank reactors （CSTR） to realize performance-oriented control in this paper. The nonlinear contraction method is combined into the control law design to render the closed-loop CSTR system globally asymptotically stable, firstly. Then, the l＆l method is used to form the adaptation law such that the off-the-manifold coordinate （the parameter estimation error） converges to zero using P-monotone property enforced by selecting tuning function in manifold. As a result, the state of the closed-loop CSTR converges to its desired value asymptotically. The simulation is given to illustrate the effectiveness of the presented algorithm.

Robust immersion and invariance adaptive coordinated control for a class of biaxial gantry systems

This paper proposes a robust Immersion and Invariance(I&I)adaptive coordinated controller for a class of uncertain linear-motor-driven biaxial gantry system subject to external disturbances for high-accuracy contour tracking.Firstly,the dynamic model of the gantry system is transformed into task coordinate frame,through which the contour tracking can be regarded as a regulation problem.Based on the transformed system dynamics,an I&I-based adaptation law with smooth projection is proposed to estimate the unknown parameters.Different from traditional adaptive control,the proposed robust I&I adaptive control introduces a new term called tuning function in adaptation law to shape the dynamic behaviour of the estimation vector.Then the stability of the closed-loop system is proved by Lyapunov theory.Finally,comparative experiments are executed on an industrial biaxial gantry system with two different cases to verify the effectiveness of the proposed control law.

A hybrid guidance law of LOS acceleration feedback for missile against maneuvering targets

A quantised backstepping robust guidance law of three-dimension(3-D)space for compound control of reaction control jets and aerodynamic surfaces is proposed to attenuate the unknown target manoeuvre in this paper.The presented guidance law guarantees that the rates of line of sight converge to a residual circle centred at the origin whose radius is depended on the target manoeuvres,quantised length and guidance law gains.The simulations are given to illustrate the effectiveness of the proposed guidance law.