Publications

The problem of adaptive sliding mode control for a class of continuous-time Takagi–Sugeno fuzzy systems regulated by the event of switching rules relying on neural network estimation method is put forward in this paper, where the plant suffers from state delay, internal structure uncertainty, and unknown nonlinearity. By proposing a switching surface in integral type, it obtains a sliding motion with desired property. In addition, to compensate the plant unknown nonlinearity and to meet the reaching condition, a radial basis function neural-network-based adaptive law is designed to ensure the existence of sliding motion in finite time. Furthermore, for the purpose of exponential stabilization of the sliding motion, a linear matrix inequality condition accompanied with switching signal characterized by an average dwell time is put forward. Finally, two numerical examples, one with all subsystems unstable and the other with stable subsystems and unstable systems, are shown to confirm the validity.

Compared to the single-source domain adaptation fault diagnosis methods, the multi-source domain adaptation methods not only can take advantage of the rich and diverse diagnostic information of multiple source domains but also draw on the feature alignment of single-source setting to reduce the domain discrepancy. However, forcing the alignment of feature distributions is challenging and may lead to negative transfer. Meanwhile, labeled data are often scarce and difficult to collect in actual production, which can be mitigated by adequate multi-source information, but the diagnostic performance of the model is degraded by large domain differences. To tackle the above issues, a domain attribute and feature transfer network is proposed to model multi-source information domains in a unified deep network and achieve cross-domain fault diagnosis. In the attribute transfer section, we adopt an attention mechanism to extract transferable latent attributes from multi-source information. In the feature transfer section, we apply the local maximum mean discrepancy metric to adjust the category distribution of single-source information and target domains. Then, intra-class compactness learning and pseudo-labeling learning strategies are utilized to obtain richer feature representations. Finally, we propose the knowledge fusion module to fuse the results of multi-source information classifiers to yield a more reliable diagnosis result. Extensive experiments on three different multi-source information datasets show the superiority of our method compared to the state-of-the-art methods (SOTA) by comparing indicators from various aspects.

This chapter intends to tackle the problem of finite-time projective synchronization of variable-order fractional (VOF) chaotic systems through the sliding mode control (SMC) method. First, for the VOF unperturbed chaotic systems, novel VOF integral- and derivative-type sliding surfaces are designed with the aid of VOF calculus. These surfaces play a crucial role in the control strategy by facilitating the management of system dynamics. Second, VOF control strategies are proposed, relying on the corresponding sliding surfaces to ensure that the projective error systems are asymptotically stable in finite time. Furthermore, by utilizing two transformations of VOF calculus, a novel finite-time stability criterion is also obtained, providing an upper bound of reaching time. This criterion is essential for predicting the system's behavior and ensuring timely synchronization. Finally, a numerical study is conducted to illustrate the superiority of the proposed method, demonstrating its effectiveness and practical applicability in achieving finite-time synchronization in VOF chaotic systems.

In this paper, robust H∞ control for a class of uncertain stochastic Markovian jump systems (SMJSs) with interval and distributed time-varying delays is investigated. The jumping parameters are modelled as a continuous-time, finite-state Markov chain. By employing the Lyapunov-Krasovskii functional and stochastic analysis theory, some novel sufficient conditions in terms of linear matrix inequalities are derived to guarantee the mean-square asymptotic stability of the equilibrium point. Numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results.

In this paper by using the quantized space and time theory we explain that why magnetic momentum of muons in Fermilab National Accelerator Laboratory (FNAL) has anomaly and doesn't compatible with the predicted amounts in the standard model. Also the speed of muons in cyclotron will be calculated precisely.

This paper presents an integrated self-triggered control strategy with convergence guarantees for model-free continuous-time systems using reinforcement learning. To consider the control cost and triggering consumption in the self-triggered scheme simultaneously, an integrated cost function is proposed. With this integrated cost function, the trade-off between the triggering occupation and control performance could be adjusted according to different requirements. Then, the actor-critic framework of reinforcement learning is employed to learn the control inputs and triggering intervals by minimizing the corresponding integrated cost function. Considering the divergent characteristics between the control inputs and triggering intervals, two different actors are utilized to learn the triggering strategy and control policy, respectively. Also, the convergence of the developed model-free self-triggered control learning algorithm is proved to ensure the limited learning duration of both the control policy and triggering strategy. The proposed framework can be used to design self-triggered controllers for a wide range of engineering systems with unknow dynamics, including control of aircraft, robots, chemical processes, and other automated systems. Finally, the effectiveness and superiorities of the proposed method are verified by an illustrative example.

Semiglobal practical integral input-to-state stability (SP-iISS) for a feedback interconnection of two discrete-time subsystems is given. We construct a Lyapunov function from the sum of nonlinearly-weighted Lyapunov functions of individual subsystems. In particular, we consider two main cases. The former gives SP-iISS for the interconnected system when both subsystems are semiglobally practically integral input-to-state stable. The latter investigates SP-iISS for the overall system when one of subsystems is allowed to be semiglobally practically input-to-state stable. Moreover, SP-iISS for discrete-time cascades and a feedback interconnection including a semiglobally practically integral input-to-state stable subsystem and a static subsystem are given. As an application of the results, these can be exploited in controller design for a sampled-data system in the framework proposed in Nešić et al. (1999) and Nešić and Angeli (2002). We illustrate such a controller design via an example.

This article studies the influence of pot temperature or electrolyte temperature in the aluminum reduction production. Specifically, these indexes reflect the distribution of the physical and energy field of the reduction cell, the current efficiency, and the lifespan of the aluminum reduction cell. Therefore, the pot temperature detection and identification are two critical and significant issues in the whole production process of aluminum electrolysis. However, due to the low measurement accuracy and high maintenance costs with the thermocouple sensor in the practical production process, the real-time measurement of pot temperature index is still a major challenge, which motivate us to develop a self-supervised soft sensor method based on deep long-short term memory (LSTM). Under the constraint of the limited samples, the proposed method achieves a competitive performance. First, the input variables are selected according to the expert experience. Then, a deep self-supervised model is built. Finally, the proposed self-supervised LSTM model is applied to real-time detection in an industrial electrolysis production case. The performance in the experiment outperforms other existing methods in terms of both accuracy and robustness aspects.

This paper presents an approach for data‐driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory‐scale three‐tank system is finally utilized. It shows that the proposed data‐driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large‐scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

In this study, a novel two‐step methodology is applied in designing static output‐feedback controllers for a class of vehicle suspension systems. Following this approach, an effective synthesis of static output‐feedback controllers can be carried out by solving two consecutive linear matrix inequality optimisation problems. To illustrate the main features of the proposed design strategy, two different static output‐feedback H ∞ controllers are designed for a quarter‐car suspension system. The first of those controllers uses the suspension deflection and the sprung mass velocity as feedback information, whereas the second one only requires the sprung mass velocity to compute the control actions. Numerical simulations indicate that, despite the restricted feedback information, the proposed static output‐feedback H ∞ controllers exhibit a good behaviour in terms of both frequency and time responses, when compared with the corresponding state‐feedback H ∞ controller.

I am honored to serve as the Editor-in-Chief of the new journal of Designs.[...]