arXiv subjects

Xi-Ming Sun

Publications and source records attributed to Xi-Ming Sun.

11 recordsLinked to original sources

arXiv2026

Integrated Flight and Propulsion Control for Fixed-Wing UAVs via Thrust and Disturbance Compensation

This paper investigates the position-tracking control problem for fixed-wing unmanned aerial vehicles (UAVs) equipped with a turbojet engine via an integrated flight and propulsion control scheme. To this end, a hierarchical control framework with thrust and disturbance compensation is proposed. In particular, we first propose a perturbed fixed-wing UAV model with turbojet engine dynamics, accounting for both unmodeled dynamics and external disturbances. Second, a versatile extended observer is designed to handle both unmeasurable thrust dynamics and external disturbances. Third, a hierarchical control framework is implemented using three observer-based controllers to guarantee position-tracking performance. With the proposed control strategy, we prove that the closed-loop system asymptotically converges to the desired trajectory. Finally, a comparative simulation is performed to illustrate the proposed control strategy.

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arXiv2024

Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction

Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the nonlinear mapping relationship of time series data but generally ignore the adequate spatiotemporal features contained in aero-engine data. To this end, we propose a brain-inspired spike echo state network (Spike-ESN) model for aero-engine intelligent fault prediction, which is used to effectively capture the evolution process of aero-engine time series data in the framework of spatiotemporal dynamics. In the proposed approach, we design a spike input layer based on Poisson distribution inspired by the spike neural encoding mechanism of biological neurons, which can extract the useful temporal characteristics in aero-engine sequence data. Then, the temporal characteristics are input into a spike reservoir through the current calculation method of spike accumulation in neurons, which projects the data into a high-dimensional sparse space. In addition, we use the ridge regression method to read out the internal state of the spike reservoir. Finally, the experimental results of aero-engine states prediction demonstrate the superiority and potential of the proposed method.

cs.LG↗

arXiv2024

Physically Informed Synchronic-adaptive Learning for Industrial Systems Modeling in Heterogeneous Media with Unavailable Time-varying Interface

Partial differential equations (PDEs) are commonly employed to model complex industrial systems characterized by multivariable dependence. Existing physics-informed neural networks (PINNs) excel in solving PDEs in a homogeneous medium. However, their feasibility is diminished when PDE parameters are unknown due to a lack of physical attributions and time-varying interface is unavailable arising from heterogeneous media. To this end, we propose a data-physics-hybrid method, physically informed synchronic-adaptive learning (PISAL), to solve PDEs for industrial systems modeling in heterogeneous media. First, Net1, Net2, and NetI, are constructed to approximate the solutions satisfying PDEs and the interface. Net1 and Net2 are utilized to synchronously learn each solution satisfying PDEs with diverse parameters, while NetI is employed to adaptively learn the unavailable time-varying interface. Then, a criterion combined with NetI is introduced to adaptively distinguish the attributions of measurements and collocation points. Furthermore, NetI is integrated into a data-physics-hybrid loss function. Accordingly, a synchronic-adaptive learning (SAL) strategy is proposed to decompose and optimize each subdomain. Besides, we theoretically prove the approximation capability of PISAL. Extensive experimental results verify that the proposed PISAL can be used for industrial systems modeling in heterogeneous media, which faces the challenges of lack of physical attributions and unavailable time-varying interface.

cs.LG↗

arXiv2024

Emulation-based Stabilization for Networked Control Systems with Stochastic Channels

This paper studies the stabilization problem of networked control systems (NCSs) with random packet dropouts caused by stochastic channels. To describe the effects of stochastic channels on the information transmission, the transmission times are assumed to be deterministic, whereas the packet transmission is assumed to be random. We first propose a stochastic scheduling protocol to model random packet dropouts, and address the properties of the proposed stochastic scheduling protocol. The proposed scheduling protocol provides a unified modelling framework for a general class of random packet dropouts due to different stochastic channels. Next, the proposed scheduling protocol is embedded into the closed-loop system, which leads to a stochastic hybrid model for NCSs with random packet dropouts. Based on this stochastic hybrid model, we follow the emulation approach to establish sufficient conditions to guarantee uniform global asymptotical stability in probability. In particular, an upper bound on the maximally allowable transmission interval is derived explicitly for all stochastic protocols satisfying Lyapunov conditions that guarantee uniform global asymptotic stability in probability. Finally, two numerical examples are presented to demonstrate the derived results.

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arXiv2023

Multiple Control Functionals for Interconnected Time-Delay Systems

Safety is essential for autonomous systems, in particular for interconnected systems in which the interactions among subsystems are involved. Motivated by the recent interest in cyber-physical and interconnected autonomous systems, we address the safe stabilization problem of interconnected systems with time delays. We propose multiple control Lyapunov and barrier functionals for the stabilization and safety control problems, respectively. In order to investigate the safe stabilization control problem, the proposed multiple control functionals are combined together via two methods: the optimization-based method and the sliding mode based method. The resulting controllers can be of either explicit or implicit forms, both of which ensure the safe stabilization objective of the whole system. The derived results are illustrated via a reach-avoid problem of multi-robot systems.

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arXiv2023

Motion Control of Two Mobile Robots under Allowable Collisions

This letter investigates the motion control problem of two mobile robots under allowable collisions. Here, the allowable collisions mean that the collisions do not damage the mobile robots. The occurrence of the collisions is discussed and the effects of the collisions on the mobile robots are analyzed to develop a hybrid model of each mobile robot under allowable collisions. Based on the effects of the collisions, we show the necessity of redesigning the motion control strategy for mobile robots. Furthermore, impulsive control techniques are applied to redesign the motion control strategy to guarantee the task accomplishment for each mobile robot. Finally, an example is used to illustrate the redesigned motion control strategy.

cs.RO↗

arXiv2023

PDE Discovery for Soft Sensors Using Coupled Physics-Informed Neural Network with Akaike's Information Criterion

Soft sensors have been extensively used to monitor key variables using easy-to-measure variables and mathematical models. Partial differential equations (PDEs) are model candidates for soft sensors in industrial processes with spatiotemporal dependence. However, gaps often exist between idealized PDEs and practical situations. Discovering proper structures of PDEs, including the differential operators and source terms, can remedy the gaps. To this end, a coupled physics-informed neural network with Akaike's criterion information (CPINN-AIC) is proposed for PDE discovery of soft sensors. First, CPINN is adopted for obtaining solutions and source terms satisfying PDEs. Then, we propose a data-physics-hybrid loss function for training CPINN, in which undetermined combinations of differential operators are involved. Consequently, AIC is used to discover the proper combination of differential operators. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-AIC for soft sensors. The proposed CPINN-AIC is a data-driven method to discover proper PDE structures and neural network-based solutions for soft sensors.

cs.LG↗

arXiv2023

Solving PDEs with Unmeasurable Source Terms Using Coupled Physics-Informed Neural Network with Recurrent Prediction for Soft Sensors

Partial differential equations (PDEs) are a model candidate for soft sensors in industrial processes with spatiotemporal dependence. Although physics-informed neural networks (PINNs) are a promising machine learning method for solving PDEs, they are infeasible for the nonhomogeneous PDEs with unmeasurable source terms. To this end, a coupled PINN (CPINN) with a recurrent prediction (RP) learning strategy (CPINN- RP) is proposed. First, CPINN composed of NetU and NetG is proposed. NetU is for approximating PDEs solutions and NetG is for regularizing the training of NetU. The two networks are integrated into a data-physics-hybrid loss function. Then, we theoretically prove that the proposed CPINN has a satisfying approximation capability for solutions to nonhomogeneous PDEs with unmeasurable source terms. Besides the theoretical aspects, we propose a hierarchical training strategy to optimize and couple NetU and NetG. Secondly, NetU-RP is proposed for compensating information loss in data sampling to improve the prediction performance, in which RP is the recurrently delayed outputs of well-trained CPINN and hard sensors. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-RP for soft sensors.

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arXiv2022

Safe Stabilization for Stochastic Time-Delay Systems

This paper addresses the safe stabilization problem of stochastic nonlinear time-delay systems. Based on theKrasovskii approach, we first propose a stochastic control Lyapunov-Krasovskii functional to guarantee the stabilization objective and a stochastic control barrier-Krasovskii functional to ensure the safety objective. Both functionals are developed respectively for each control objectives for the first time. Since the optimization problem is not easy to be resolved for stochastic time-delay systems, we derive a sliding mode based approach to combine the proposed two functionals and to mediate stabilization and safety objectives, which allows to achieve the stabilization objective under the safety requirement. The proposed approach is illustrated via a numerical example.

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arXiv2020

Analysis of a Nonlinear Opinion Dynamics Model with Biased Assimilation

This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual's degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate, and strong bias. The opinions of the individuals lie between 0 and 1. It is shown that for strongly connected networks, the equilibria with all elements equal identically to the extreme value 0 or 1 is locally exponentially stable, while the equilibrium with all elements equal to the neutral consensus value of 1/2 is unstable. Regions of attraction for the extreme consensus equilibria are given. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is unstable for all strongly connected networks if individuals all have weak bias, becomes locally exponentially stable for complete and two-island networks if individuals all have strong bias, and its stability heavily depends on the network topology when individuals have intermediate bias. Analysis on star graphs and simulations show that additional equilibria may exist where individuals form clusters.

math.OC↗

arXiv2017

On Convergence Rate of a Continuous-Time Distributed Self-Appraisal Model with Time-Varying Relative Interaction Matrices

This paper studies a recently proposed continuous-time distributed self-appraisal model with time-varying interactions among a network of $n$ individuals which are characterized by a sequence of time-varying relative interaction matrices. The model describes the evolution of the social-confidence levels of the individuals via a reflected appraisal mechanism in real time. We first show by example that when the relative interaction matrices are stochastic (not doubly stochastic), the social-confidence levels of the individuals may not converge to a steady state. We then show that when the relative interaction matrices are doubly stochastic, the $n$ individuals' self-confidence levels will all converge to $1/n$, which indicates a democratic state, exponentially fast under appropriate assumptions, and provide an explicit expression of the convergence rate.

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