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The distributed thermal processes are widely present in various industrial operations, but the presence of time delay effect and unknown model information make achieving fast and timely fault detection challenging. Furthermore, the involvement of spatiotemporal coupled data in measurement processes further exacerbates the situation. To solve these problems, this paper proposes a data-driven fault detection and location method for thermal processes described by distributed parameter systems (DPS). Firstly, a Time/Space separation is employed to decoupling the spatio-temporal data into spatial basis functions and temporal coefficients. Subsequently, dynamic partial least squares (DPLS) is employed to obtain the temporal information of the model with fault-free data. Monitoring statistics in both the temporal and spatial domains are then constructed, and their thresholds are estimated by kernel density function. Finally, an online strategy for fault detection and location is presented. The method was validated on the catalytic rod, and an experimental snap curing oven. • Dynamic partial least squares (DPLS) as a data-driven method for fault detection and location in distributed thermal processes. • The proposed method can fast complete the detection and solves the problem of time delay effect in fault diagnosis. • The proposed method does not require model information and can independently accomplish fault detection and location. • Compared to traditional methods, DPLS offers shorter response time and higher accuracy. [ABSTRACT FROM AUTHOR]

Many industrial thermal processes can be classified as complex distributed parameter systems (DPS). The traditional control method has become ill-suited for complex DPS since it ignores the time-varying dynamics and spatially distributed behavior. Moreover, the controller incorporating a nonlinear model imposes substantial computational demands, hindering the timely acquisition of control strategies in practical scenarios. In this study, an adaptive spatial-model-based predictive controller with real-time linearization for complex DPS is proposed. Initially, an adaptive spatiotemporal modeling method is developed to characterize the time-varying dynamics of DPS more accurately. Based on this developed spatiotemporal model, a spatial model predictive control strategy with real-time linearization is further designed to reduce the computational burden of the controller. In addition, a weighted terminal cost is embedded to replace terminal constraints in the controller, ensuring stability while improving calculation efficiency. Theoretical analysis confirms the stability of the proposed controller. Finally, furnace simulation and oven experiments are performed to comprehensively evaluate the performance of the proposed controller. It shows that the proposed method significantly improves tracking performance and robustness while reducing computational load. [ABSTRACT FROM AUTHOR]

• Spatial multivariable SVR based 3-D fuzzy modeling methodology is proposed for distributed parameter systems. • Multivariable SVR with spatial kernel functions is proposed to cope with a set of spatiotemporal data. • Spatial multivariable SVR builds up a complete 3-D fuzzy rule-base. • 3-D fuzzy model is constructed in the form of data-driven design. Many industrial processes have significant spatiotemporal dynamics and they are usually called distributed parameter systems (DPSs). Modeling such system is challenging due to its nonlinearity, time-varying dynamics, and spatiotemporal coupling. Using model reduction techniques, traditional DPS modeling methods usually reduce an infinite-dimensional system to a finite-dimensional system, which leads to unknown nonlinearity and unmodeled dynamics. The modeling method and the established model are hard to understand. Here, we propose a spatial multivariable support vector regression (SVR) based three-domain (3-D) fuzzy modeling method for complex nonlinear DPSs. The proposed 3-D modeling method integrates the time-space separation and time-space synthesis into a 3-D fuzzy model. Therefore, it does not require model reduction and owns the capability of linguistic interpretability. A spatial multivariable SVR with spatial kernel functions is proposed to deal with spatiotemporal data. The spatial fuzzy basis functions from a 3-D fuzzy model are spatial kernel functions for a spatial multivariable SVR, which satisfy Mercy theorem. Hence, the spatial multivariable SVR can be directly employed to build up a complete 3-D fuzzy rule-base of the 3-D fuzzy model. The proposed modeling method integrates the merits of learning ability from a spatial multivariable SVR and fuzzy space processing and fuzzy linguistic expression from a 3-D fuzzy model. The proposed 3-D fuzzy modeling method is successful applied to a simulated rapid thermal processing system. In comparison with several newly developed modeling methods for DPSs, the simulation results validate the superiority of the proposed modeling method. [ABSTRACT FROM AUTHOR]

In this paper, a dual least squares support vector machines (LS-SVM) is proposed to model the thermal process. The infinite-dimensional system is first transformed into a finite-dimensional system through space-time separation. Then, the dual LS-SVM model is to approximate the two nonlinearities embedded in the system. Through space-time synthesis, the dual LS-SVM based spatiotemporal model is able to approximate the complex DPS with inherent coupled nonlinearities. The generalization performance of the proposed model is discussed using Rademacher complexity. Finally, simulations on a curing process demonstrate the effectiveness of the proposed modeling method. [ABSTRACT FROM AUTHOR]

A new multiple periodic disturbance rejection control for casting process is proposed in this paper. Firstly, a high-precision multiple periodic disturbance detection method is designed. The accurate frequencies of the disturbance can be obtained. By using the disturbance observer and a group of filters with additional delay items, multiple periodic disturbance can be suppressed under time-delay environment. Then systematic control design is achieved based on the robust stability analysis. Finally, the efficiency of the proposed method is shown by comparisons between different methods and time-varying disturbance rejection in the consistency control of mold level. [ABSTRACT FROM AUTHOR]

The safety of the electric vehicle is highly dependent on the safe operation of the battery system, where the thermal behavior becomes important since any abnormality could be an early sign of potential damage. Some fault diagnosis methods have been proposed for abnormality detection and health management of battery systems. However, the existing approaches heavily depend on the governing equations or many sensors for data collection, which limits the applications of these methods in practice. In this paper, a multiscale dynamic analysis method is proposed to detect and localize thermal abnormalities occurring in battery cells using fewer sensors. First, a two-dimensional spatial construction is designed to model the temperature field in the battery cell under sparse sensing. The abnormal detection is performed separately on spatial and temporal scales, and then integrated into a more comprehensive detection criterion. After probabilistic processing under kernel density estimation, the final decision can be made more reliably in practical situations with stochastic uncertainty. Furthermore, the location of the abnormality is figured out with the constructed spatial basis functions. Simulations and experiments are carried out on pouch-type Li-ion battery cells, demonstrating that the proposed method can effectively detect and locate internal short circuit (ISC) abnormalities before they develop into thermal runaway. This study makes the first effort to consider both spatial and temporal information for designing the detection index in battery systems. The optimality of the proposed spatial construction method is proved by theoretical analysis. • A multiscale dynamic analysis method is proposed for diagnosis of battery systems. • The proposed method considers spatial and temporal information for fault detection. • Theoretical analysis proves that the reconstruction error is convergent. • Experiments demonstrate the proposed method has good performance in ISC detection. [ABSTRACT FROM AUTHOR]

A sliding mode control strategy is developed for temperature control of a rapid thermal processing (RTP) system to enhance temperature accuracy and uniformity when heating in this work. Temperature dynamics of the RTP is nonlinear and infinite-dimensional while order of the controller should be finite due to implementation limitations. Developing a nonlinear low-order controller for the system is important if accurate and uniform control of the temperature is required. This work addresses this issue by developing a sliding mode controller based on the dominant modes of the system. The schemes are applied to a RTP system and excellent performances are achieved. [ABSTRACT FROM AUTHOR]

Robust design is crucial for the satisfactory performance of manufacturing systems. Robust solutions are difficult to obtain when a system is strongly nonlinear or has large parameter variations. In this work, a novel design approach is proposed to ensure stability and robustness of a nonlinear system under large parameter variations. A sector nonlinearity method is first employed to model a nonlinear system. A stability design is then developed to ensure the stability of this nonlinear system under parameter variations. The influence of parameter variations on the eigenvalues of the system is minimized to maintain the system robustness. The proposed design involves a complex nonlinear optimization that is difficult to solve mathematically. A particle swarm optimization algorithm will be used to formulate a two-loop optimization that can obtain an optimal design solution. A simulation study on the practical system is conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Many industrial processes belong to nonlinear distributed parameter systems (DPS) with significant spatio-temporal dynamics. They often work at multiple operating points due to different production and working conditions. To obtain a global model, the direct modeling and experiments in a large operating range are often very difficult. Motivated by the multi-modeling, a fuzzy-based spatio-temporal multi-modeling approach is proposed for nonlinear DPS. To obtain a reasonable operating space division, a priori information and the fuzzy clustering are used to decompose the operating space from coarse scale to fine scale gradually. To reduce the dimension in the local spatio-temporal modeling, the Karhunen–Loève method is used for the space/time separation. Both multi-modeling and space/time separation can reduce the modeling complexity. Finally, to get a smooth global model, a three-domain (3D) fuzzy integration method is proposed. Using the proposed method, the model accuracy will be improved and the experiments become easier. The effectiveness is verified by simulations. [ABSTRACT FROM AUTHOR]

Most of industrial processes have dead-time phenomenon that will affect the process performance. Though there are some disturbance rejection methods for the process with dead-time, however, most of them may not work well for multiple periodic disturbance. In this paper, a novel periodic disturbance rejection controller is designed based on the Smith predictor platform for process with long dead-time. By adding two feedback loops and the online spectrum analysis, multiple periodic disturbances can be suppressed effectively in existence of long dead-time. The rigorous analysis is conducted to prove that the robust stability can be maintained. Finally, the effectiveness of the proposed controller is demonstrated in the process simulations and industrial experiment. [ABSTRACT FROM AUTHOR]

Highlights: [•] Point out the drawbacks of the crossover operator and the parameter settings of differential evolution (DE). [•] Propose a novel DE variant based on covariance matrix learning and bimodal distribution parameter setting, named CoBiDE. [•] Verify the effectiveness of CoBiDE by many experiments. [ABSTRACT FROM AUTHOR]

Abstract: A linear or second-order model is usually used to approximate a practical nonlinear system in probabilistic robust designs. This approximation often makes these designs less effective when the system has strong nonlinearity as well as large uncontrollable variation. In this paper, a novel probabilistic design approach is proposed to design a nonlinear system to be robust under circumstance of large random variation. First, a variable sensitivity approach is employed to place the nonlinear influence under the sensitivity matrix of the covariance model. On this basis, a robust design approach is proposed to minimize the influence of random variation in relation to the performance covariance. Since this proposed approach considers both nonlinear influence and probabilistic distribution in a large uncertain region, it can effectively ensure robustness of nonlinear systems even if large random variation exists. [Copyright &y& Elsevier]

Abstract: In this paper, an inherent saturation of the fuzzy proportional–integral–derivative (PID) controller is revealed due to the finite fuzzy rules used. An equivalent structure and model of the fuzzy rule base is derived to show a saturation property. The bandwidth of the fuzzy PID control system can be adjusted by changing saturation parameter. Parameters of the fuzzy PID controller can be designed based on the inherent saturation. Compared with the conventional PID controller, the fuzzy PID controller has two advantages because of the inherent saturation: (1) without the additional filter, it can prevent impulse signal effectively; (2) without the additional anti-windup structure, a robust performance can be maintained when the input saturation occurs. The fuzzy PID controller is applied to an integrated circuit curing process. The simulation and experiment results demonstrate these effects of the inherent saturation, and its influence to the robustness of fuzzy PID controller. [Copyright &y& Elsevier]

Abstract: A robust adaptive neural observer design is proposed for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities and bounded disturbances. The modal decomposition technique is initially applied to the PDE system to formulate it as an infinite-dimensional singular perturbation model of ordinary differential equations (ODEs). By singular perturbations, an approximate nonlinear ODE system that captures the dominant (slow) dynamics of the PDE system is thus derived. A neural modal observer is subsequently constructed on the basis of the slow system for its state estimation. A linear matrix inequality (LMI) approach to the design of robust adaptive neural modal observers is developed such that the state estimation error of the slow system is uniformly ultimately bounded (UUB) with an ultimate bound. Furthermore, using the existing LMI optimization technique, a suboptimal robust adaptive neural modal observer can be obtained in the sense of minimizing an upper bound of the peak gains in the ultimate bound. In addition, using two-time-scale property of the singularly perturbed model, it is shown that the resulting state estimation error of the actual PDE system is UUB. Finally, the proposed method is applied to the estimation of temperature profile for a catalytic rod. [Copyright &y& Elsevier]

Abstract: Low back pain (LBP) affects a large proportion of the population and is the main cause of work disabilities worldwide. The mechanism of LBP remains largely unknown and many existing clinical treatment of LBP may be not effective to individual patients. Thus the diagnosis and treatment evaluation is crucial for LBP patients. Probabilistic support vector machine (PSVM) decision system is proposed in this article to deal with the diagnosis and treatment evaluation of LBP. The decision system consists of qualitative knowledge model and quantitative model. Expert knowledge and clinical experience are integrated into the design. To deal with the uncertainties in patients samples, PSVM is employed to learn the decision rules from data. The proposed decision system is applied to LBP patients and achieves better performance than the original system. [Copyright &y& Elsevier]

Abstract: In this paper, the adaptive backstepping design is proposed for the full state hybrid projective synchronization between two different chaotic systems with fully unknown parameters. Based on the design, the synchronization of two uncertain chaotic systems is realized only by using one controller, and the unknown parameters are identified through the corresponding parameter update laws. The uncertain Genesio–Tesi chaotic system and Lorenz system are chosen as examples for detailed description of the method. Finally, some numerical simulations are given to illustrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, a PSO-based intelligent integration of design and control is proposed for one kind of nonlinear curing process. This method combines the merits of both fuzzy modeling/control and PSO method, where fuzzy modeling/control is proposed to approximate/control the nonlinear process in a large operating region and the PSO-based intelligent optimization method is developed to solve non-convex and non-differential integration problem with design and control optimized simultaneously. Finally, the proposed method is compared with the traditional sequential method on controlling the temperature profile of a nonlinear curing process. [ABSTRACT FROM AUTHOR]

Abstract: Many industrial processes belong to distributed parameter systems (DPS) that have strong spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control and optimization. The first-principle modeling for known DPS often leads to the partial differential equation (PDE). Because it is an infinite-dimensional system, the model reduction (MR) is very necessary for real implementation. The model reduction often works with selection of basis functions (BF). Combination of different BF and MR results in different approaches. For unknown DPS, system identification is usually used to figure out unknown structure and parameters. Using various methods, different approaches are developed. Finally, a novel kernel-based approach is proposed for the complex DPS. This paper provides a brief review of different DPS modeling methods and categorizes them from the view of time–space separation. [ABSTRACT FROM AUTHOR]

Abstract: This paper mainly investigates adaptive generalized function projective synchronization of two different uncertain chaotic systems, which is a further extension of many existing projection synchronization schemes, such as modified projection synchronization, function projective synchronization and so on. On the basis of Lyapunov stability theory, an adaptive controller for the synchronization of two different chaotic systems is designed, and some parameter update laws for estimating the unknown parameters of the systems are also gained. This technique is applied to achieve synchronization between Lorenz and Rössler chaotic systems. The numerical simulations demonstrate the validity and feasibility of the proposed method. [Copyright &y& Elsevier]

Abstract: Many chemical processes are nonlinear distributed parameter systems with unknown uncertainties. For this class of infinite-dimensional systems, the low-order model identification from process data is very important in practice. The dimension reduction with a principal component analysis (PCA) is only a linear approximation for nonlinear problem. In this study, a nonlinear dimension reduction based low-order neural model identification approach is proposed for nonlinear distributed parameter processes. First, a nonlinear principal component analysis (NL-PCA) network is designed for the nonlinear dimension reduction, which can transform the high-dimensional spatio-temporal data into a low-dimensional time domain. Then, a neural system can be easily identified to model this low-dimensional temporal data. Finally, the spatio-temporal dynamics can be reproduced using the nonlinear time/space reconstruction. The simulations on a typical nonlinear transport-reaction process show that the proposed approach can achieve a better performance than the linear PCA based modeling approach. [Copyright &y& Elsevier]

Abstract: Modeling of distributed parameter systems (DPSs) is very difficult because of their infinite-dimensional, spatio-temporal nature and nonlinearities. A low-order, simple nonlinear and parsimonious model is often required in real applications. In this study, a time/space separation-based Hammerstein modeling approach is proposed for unknown nonlinear DPS. Firstly, the Karhunen-Loève (KL) method is used for the time/space separation, where the spatio-temporal output is decomposed into a few dominant spatial basis functions with temporal coefficients. Secondly, a low-order parsimonious Hammerstein model is identified from the low-dimensional data to reconstruct the system dynamics, where the parsimonious model structure is determined by the orthogonal forward regression and the parameters are estimated using the least squares estimation and the singular value decomposition. The algorithm does not require nonlinear optimization and it is numerically robust. This modeling is very suitable for control design. The simulations are presented to show the effectiveness of the proposed modeling method. [Copyright &y& Elsevier]

Abstract: It is difficult to model a distributed parameter system (DPS) due to the infinite-dimensional time/space nature and unknown nonlinear uncertainties. A low-dimensional and simple nonlinear model is often required for practical applications. In this paper, a spatio-temporal Volterra model is proposed with a series of spatio-temporal kernels for modeling unknown nonlinear DPS. To estimate these kernels, they are expanded onto spatial and temporal bases with unknown coefficients. To reduce the model dimension and parametric complexity in the spatial domain, the Karhunen–Loève (KL) method is used to find the dominant spatial bases. To reduce the parametric complexity in the temporal domain, the Laguerre polynomials are selected as temporal bases. Next, using the Galerkin method, this spatio-temporal modeling becomes a linear regression problem. Then unknown parameters can be easily estimated using the least-squares method in the temporal domain. After the time/space synthesis, the spatio-temporal Volterra model can be constructed. The convergence of parameter estimation can be guaranteed under certain conditions. This model has a low-dimensional and simple nonlinear structure, which is useful for the prediction and control of the DPS. The simulation and experiment demonstrate the effectiveness of the proposed modeling method. [Copyright &y& Elsevier]

The temperature control of the reheat furnace is very difficult due to its complex characteristics. Based on expert knowledge and pyrology mechanism, a hybrid supervisory control system (an optimal setting model) has been developed innovatively to control its temperature. With the help of a statistical process controller, this hybrid supervisory control system can replace the human operator for most of operations in the process. Since an expert compensator is designed to suppress the external disturbance, the proposed model can automatically update the optimal set-point value for the furnace temperature of each zone under the varying boundary conditions. Both simulation and industrial experiment show the viability and effectiveness of the suggested model and its bright application foreground in thermal process. [Copyright &y& Elsevier]

A stable discrete time adaptive control approach using dynamic neural networks (DNNs) is developed in this paper for the trajectory tracking of a robotic manipulator with unknown nonlinear dynamics. By using dynamic inversion constructed by a DNN, the assumption under which the system state should be on a compact set can be removed. This assumption is usually required in neuro-adaptive control. The NN-based variable structure control is designed to guarantee the stability and improve the dynamic performance of the closed-loop system. The proposed control scheme ensures the global stability and desired tracking as well. [Copyright &y& Elsevier]

Abstract: In this work, a dynamic switching based fuzzy controller combined with spectral method is proposed to control a class of nonlinear distributed parameter systems (DPSs). Spectral method can transform infinite-dimensional DPS into finite ordinary differential equations (ODEs). A dynamic switching based fuzzy controller is constructed to track reference values for the multi-inputs multi-outputs (MIMO) ODEs. Only a traditional fuzzy logic system (FLS) and a rule base are used in the controller, and membership functions (MFs) for different ODEs are adjusted by scaling factors. Analytical models of the dynamic switching based fuzzy controller are deduced to design the scaling factors and analyze stability of the control system. In order to obtain a good control performance, particle swarm optimization (PSO) is adopted to design the scaling factors. Moreover, stability of fuzzy control system is analyzed by using the analytical models, definition of the stability and Lyapunov stability theory. Finally, a nonlinear rod catalytic reaction process is used as an illustrated example for demonstration. The simulation results show that performance of proposed dynamic switching based fuzzy control strategy is better than a multi-variable fuzzy logic controller. [Copyright &y& Elsevier]

In this paper, we focus on an event-triggered adaptive neural fault-tolerant control for a two-degree of freedom (2-DOF) helicopter system with actuator failures. In this design, a radial basis function neural network (NN) is exploited to estimate the uncertainty terms present in the system. A new error transformation technique is employed to make the tracking error of the system satisfy a prescribed performance function. Then, an event triggering mechanism is introduced to reduce the communication burden of the system. Smoothing functions and bounded estimation methods are then used to compensate for actuator failures and measurement errors. The helicopter system is proved to be consistently bounded by the direct method of Lyapunov functions. Finally, simulation and experimental results verify the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]