Fault-Tolerant Control of Stochastic High-Order Fully Actuated Systems.

In recent years, high-order fully actuated (HOFA) systems, founded by Prof. GR Duan, have recorded rapid progress for deterministic systems. However, the control issue of stochastic fully actuated systems is still an open problem. This study develops a novel stochastic HOFA system model that complements the existing HOFA methodology. Notably, stochastic signals can be considered in the proposed model, different from the case in the deterministic model. By adopting a high-order operator, equivalent control and stabilization control laws are realized to guarantee the global asymptotic stability in probability of the closed-loop system. For the system with sensor gain faults, an observer-based fault-tolerant control law is designed. Finally, the simulation results validate the effectiveness of the proposed control schemes.

Adaptive Cointegration Analysis and Modified RPCA With Continual Learning Ability for Monitoring Multimode Nonstationary Processes.

This study investigates nonstationary process monitoring under frequently varying modes, where new modes are allowed to emerge constantly. However, in current multimode process monitoring methods, generally, data are required from all possible modes and mode identification is realized by prior knowledge for multimode nonstationary processes. In contrast, recursive methods update a monitoring model based on the successive data. However, they forget the learned knowledge gracefully and fail to track drastic variations. Aimed at nonstationary data in each mode, this article proposes an adaptive cointegration analysis (CA) to distinguish real faults from normal variations, which updates a model once a normal sample is encountered and adapts to the gradual change in the cointegration relationship. Then, a modified recursive principal component analysis (RPCA) with continual learning ability is developed to deal with the remaining dynamic information, wherein elastic weight consolidation is adopted to consolidate the previously learned knowledge when a new mode appears. The preserved information is beneficial for establishing a more accurate model than traditional RPCA and avoiding drastic performance degradation for future similar modes. In addition, novel statistics are proposed with prior knowledge and thresholds are calculated by recursive kernel density estimation to enhance the performance. An in-depth comparison with recursive CA and recursive slow feature analysis is conducted to emphasize the superiority, in terms of the algorithm accuracy, memory properties, and computational complexity. Compared with state-of-the-art recursive algorithms, the effectiveness of the proposed method is shown by studying on a numerical case and a practical industrial system.

A Deep Quality Monitoring Network for Quality-Related Incipient Faults.

Although quality-related process monitoring has achieved the great progress, scarce works consider the detection of quality-related incipient faults. Partial least square (PLS) and its variants only focus on faults with larger magnitudes. In this article, a deep quality monitoring network (DQMNet) for quality-related incipient fault detection is developed. DQMNet includes the feature input layer, feature extraction layers, and the output layer. In the feature input layer, collected variables are divided according to quality variables, and then, features are extracted, respectively, through base detectors. For the feature extraction layers, singular values (SVs) of sliding-window patches and principal component analysis (PCA) are adopted to mine the hidden information layer by layer. For the output layer, statistics are constructed from quality-related/unrelated feature matrix through Bayesian inference. The superiority of DQMNet is demonstrated by a numerical simulation and the benchmark data of Tennessee Eastman process (TEP).

Multimodal Continual Learning for Process Monitoring: A Novel Weighted Canonical Correlation Analysis With Attention Mechanism.

Aimed at sequential dynamic modes, a novel multimodal weighted canonical correlation analysis using an attention (MWCCA-A) mechanism is introduced to derive a single model for process monitoring, by integrating two ideas of replay and regularization in continual learning. Under the assumption that data are received sequentially, subsets of data from past modes with dynamic features are selected and stored as replay data, which are utilized together with the current mode data for continual model parameter estimation. The weighted canonical correlation analysis (WCCA) is introduced to achieve appropriate weightings of past modes' replay data so that the latent variables are extracted by maximizing the weighted correlation with its prediction via the attention mechanism. Specifically, replay data weightings are obtained via the probability density estimation from each mode. This is also beneficial in overcoming data imbalance among multiple modes and consolidating the significant features of past modes further. Alternatively, the proposed model also regularizes parameters based on its previous modes' importance, which is measured by synaptic intelligence (SI). Meanwhile, the objective is decoupled into a regularization-related part and a replay-related part, to overcome the potentially unstable optimization trajectory of SI-based continual learning. In comparison with several multimode monitoring methods, the effectiveness of the proposed MWCCA-A approach is demonstrated by a continuous stirred tank heater (CSTH), Tennessee Eastman process (TEP), and a practical coal pulverizing system.

Hybrid Variable Monitoring Mixture Model for Anomaly Detection in Industrial Processes.

Effective process monitoring is both a prerequisite and a guarantee for high system reliability. In modern industrial processes, binary variables may appear together with continuous variables, making process monitoring more intractable. Recently, a model named hybrid variable monitoring (HVM) has been proposed to conduct anomaly detection with both continuous and binary variables. Although the performance of HVM has been significantly improved after using the information of binary variables, it assumes that every continuous variable obeys a single Gaussian distribution and each binary variable obeys a single Bernoulli distribution. It is difficult for practical processes to satisfy such strict assumptions. To overcome this problem, this study proposes an improved algorithm called HVM mixture model (HVMMM). The HVMMM contains multiple components with the assumption of an HVM for every component. Compared with the HVM, the HVMMM is suitable for more general situations and has a more accurate characterization of the data features. Subsequently, the expectation-maximization (EM) algorithm is adopted for parameter learning for multiple components. The mathematical expressions of the parameters are derived in detail. In addition, the improvement on the monitoring performance caused by multiple components is analyzed. Finally, a numerical example and a practical case are used to demonstrate the effectiveness and efficiency of HVMMM. After multiple components are considered, the fault detection rate increases by 5.49% in the numerical example and the false alarm rate reduces by 1.6% in the practical case.

Generalized synchronization of complex networks.

We consider generalized synchronization of complex networks, which are unidirectionally coupled in the drive-response configuration. The drive network consists of linearly and diffusively coupled identical chaotic systems. By choosing suitable driving signals, we can construct the response network to generally synchronize the drive network in a predefined functional relationship. This extends both generalized synchronization of chaotic systems and synchronization inside a network. Theoretical analysis and numerical simulations fully verify our main results.

Matrix-measure criterion for synchronization in coupled-map networks.

We present conditions for the local and global synchronization in coupled-map networks using the matrix measure approach. In contrast to many existing synchronization conditions, the proposed synchronization criteria do not depend on the solution of the synchronous state and give less limitation on the network connections. Numerical simulations of the coupled quadratic maps demonstrate the potentials of our main results.

Enhanced synchronizability in scale-free networks.

We introduce a modified dynamical optimization coupling scheme to enhance the synchronizability in the scale-free networks as well as to keep uniform and converging intensities during the transition to synchronization. Further, the size of networks that can be synchronizable exceeds by several orders of magnitude the size of unweighted networks.

Synchronization in the Kuramoto model: a dynamical gradient network approach.

We propose a dynamical gradient network approach to consider the synchronization in the Kuramoto model. Our scheme to adaptively adjust couplings is based on the dynamical gradient networks, where the number of links in each time interval is the same as the number of oscillators, but the links in different time intervals are also different. The gradient network in the (n+1)th time interval is decided by the oscillator dynamics in the n th time interval. According to the gradient network in the (n+1)th time interval, only one inlink's coupling for each oscillator is adjusted by a small incremental coupling. During the transition to synchronization, the intensities for all oscillators are identical. Direct numerical simulations fully verify that the synchronization in the Kuramoto model is realized effectively, even if there exist delayed couplings and external noise.

Synchronization in small-world networks.

In this paper we consider complete synchronization in small-world networks of identical Rössler oscillators. By applying a simple but effective dynamical optimization coupling scheme, we realize complete synchronization in networks with undelayed or delayed couplings, as well as ensuring that all oscillators have uniform intensities during the transition to synchronization. Further, we obtain the coupling matrix with much better synchronizability in a certain range of the probability p for adding long-range connections. Direct numerical simulations fully verify the efficiency of our mechanism.

Coevolution of Environmental Perception and Cooperative Behavior in Evacuation Crowd.

For the evacuation crowd of social agents, environment plays a big effect on the behavior and decision of the agents. When facing the uncertain environment, the behavior and decision of agents depend heavily on the perception of environment. Therefore, the cooperation between agents and their perception of environment may coexist during evacuation. Here we establish a mechanism to analyze the coevolution between the cooperation of agents and the perception of environment. In detail, we use a regular square lattice with periodic boundaries, where two payoff matrices are used to describe two kinds of games between neighbors in the safe and dangerous environments. For individual agent, its perception can be adjusted by interacting with neighboring agents. When the environment is generally considered dangerous, the fraction of cooperative agents keeps at a high level, even if the value of b is very large. When all the agents think that the environment is safe, the fraction of cooperation will decrease as the value of b increases.

Synchronization in time-varying networks: a matrix measure approach.

Synchronization in complex networks has attracted lots of interest in various fields. We consider synchronization in time-varying networks, in which the weights of links are time varying. We propose a useful approach--i.e., the matrix measure approach--to derive some analytically sufficient conditions for synchronization in time-varying networks. These conditions are less conservative than many existing synchronization conditions. Theoretical analysis and numerical simulations of different networks verify our main results.

Chaos synchronization and parameter estimation from a scalar output signal.

We propose an observer-based approach for chaos synchronization and parameter estimation from a scalar output signal. To begin with, we use geometric control to transform the master system into a standard form with zero dynamics. Then we construct a slaver to synchronize with the master using a combination of slide mode control and linear feedback control. Within a finite time, partial synchronization is realized, which further results in complete synchronization as time tends to infinity. Even if there exists model uncertainty in the slaver, we can also estimate the unknown model parameter by a simple adaptive rule.

Synchronization of time-delayed systems.

In this paper we study synchronization in linearly coupled time-delayed systems. We first consider coupled nonidentical Ikeda systems with a square wave coupling rate. Using the theory of the time-delayed equation, we derive less restrictive synchronization conditions than those resulting from the Krasovskii-Lyapunov theory [Yang Kuang, (Academic Press, New York, 1993)]. Then we consider a wide class of nonlinear nonidentical time-delayed systems. We also propose less restrictive synchronization conditions in an approximative sense, even if the coefficients in the linear time-delayed equation on the synchronization error are time dependent. Theoretical analysis and numerical simulations fully verify our main results.

Synchronization in uncertain complex networks.

We consider the problem of synchronization in uncertain generic complex networks. For generic complex networks with unknown dynamics of nodes and unknown coupling functions including uniform and nonuniform inner couplings, some simple linear feedback controllers with updated strengths are designed using the well-known LaSalle invariance principle. The state of an uncertain generic complex network can synchronize an arbitrary assigned state of an isolated node of the network. The famous Lorenz system is stimulated as the nodes of the complex networks with different topologies. We found that the star coupled and scale-free networks with nonuniform inner couplings can be in the state of synchronization if only a fraction of nodes are controlled.