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It is very difficult to control the distributed parameter system (DPS) for consistent spatial performance. In this paper, a dual scale spatio‐temporal control is designed for the DPS to achieve a consistent performance across the entire workspace under unknown exogenous disturbances. First, the generalized "spatial observer" should be designed under spectral decomposition/synthesis, to act as the nominal model of the DPS, through which the spatial mismatch between the model and the process could be estimated. Then a distributed disturbance compensator can be constructed to suppress all the undesirable spatial disturbance through the inner loop, and the compensated system will become closer to the nominal model and easier to control. Third, a dual controller will be designed in the outer loop for the final consistent spatial performance. Since the spatial performance is mainly affected by the dominant dynamics on the slow scale, a convex optimization algorithm can be effectively established in terms of nonlinear matrix inequalities for the dual controller. In this way, the nonlinear optimization problem is solved by converting it into the problem of a linear matrix inequalities with constraints. Besides, the spatio‐temporal state variable of the controlled plant is demonstrated to converge in Hilbert space. The feasibility and effectiveness of the proposed control method are verified by temperature control of a catalytic rod, a benchmark in chemical reactors. [ABSTRACT FROM AUTHOR]

A 2-D spatial construction method is proposed for the online modeling of distributed parameter systems (DPSs), such as battery thermal process. The proposed method can combine the advantages of spectral method and Karhunen–Loève decomposition (KLD) method. First, the continuous spatial basis functions are designed by the 2-D spatial construction to keep the information between sensing locations. With the 2-D space-time separation and recursive learning, the derived model can preserve the couplings between spatial dimensions and update over time. The radial basis function network is utilized to identify the low-dimensional temporal dynamics. After the space-time synthesis, the constructed spatiotemporal model can provide continuous modeling of the DPS with satisfactory performance. Convergence analysis has been carried out, which proves that the proposed method can guarantee bounded errors. Finally, simulations and experiments on a pouch-type lithium-ion battery with unknown partial differential equations prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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]

Modeling high-spatial dimensional (high-D) distributed parameter systems (DPSs) is very difficult because of the spatially distributed characteristic and complex spatiotemporal coupling. In this article, a new framework based on high-order singular vector decomposition (HOSVD) is proposed to model a high-D DPS. A modified HOSVD is designed for separation of time and multiple spatial variables. It can well preserve the spatially distributed nature of the high-D DPS. It also considers the interaction across different spatial modes, which is an important part of spatiotemporal coupling in the high-D DPS. Experiments of a two-spatial dimensional thermal curing process are used to verify the effectiveness of the proposed method. An average prediction error near 1% of the curing temperature can be achieved. [ABSTRACT FROM AUTHOR]

Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to the category of inverse source problems. It is important in industrial applications but seldom studied. In this article, we make the first attempt to investigate the abnormal spatio-temporal (S-T) source identification for a class of DPSs. An inverse S-T model for abnormal source identification is developed for the first time. It consists of an adaptive state observer for source identification and an adaptive source estimation algorithm. One major advantage of the proposed inverse S-T model is that only the system output is utilized, without any state measurement. Theoretic analysis is conducted to guarantee the convergence of the estimation error. Finally, the performance of the proposed method is evaluated on a heat transfer rod with an abnormal S-T source. [ABSTRACT FROM AUTHOR]

For complex distributed parameter systems (DPSs) with strong nonlinearities and time-varying dynamics, the conventional spatiotemporal modeling methods become ill-suited since the elementary assumption that the process data follow a unimodal Gaussian distribution usually becomes invalid. In this paper, a multimode method is proposed for modeling of such systems. First, the original operating space is partitioned along the time dimension into several subspaces via modified dissimilarity analysis. Each subspace represents the local spatiotemporal characteristics of the original system. Second, the Karhunen–Loève decomposition (KLD)-based spatiotemporal modeling approach is applied to approximate the local dynamics of each subspace. Finally, an ensemble model is obtained using the soft weighting sum of the local ones, where the corresponding weights are calculated by principal component regression. By properly decomposing the original space into several local parts, the ensemble model is capable of handling the strong nonlinearities and time-varying dynamics of the system. The validity and efficiency of the proposed method are verified on two representative applications: 1) a one-dimensional parabolic catalytic rod and 2) a two-dimensional curing thermal process. The experimental results show that the proposed method provides a superior performance regarding modeling accuracy compared to several baselines. [ABSTRACT FROM AUTHOR]

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Identifying reliable information from the information ocean is a natural talent of human being, which can be hardly formalized by machines. In this paper, we show how to measure the degree of uncertainty in terms of interpoint similarity. For applications under complex uncertainty, it is desirable that we provide a systematic way for users to identify reliable information. In our approach, the similarity uncertainty for different kinds of data sets is defined according to the Shannon entropy theory. Then, similarity constrained models are designed to guarantee superior learning performance under uncertainty. Experiments using both simulation data set and several public data sets, can clearly demonstrate significant improvements of the proposed method under large uncertainty. [ABSTRACT FROM AUTHOR]

The thermal effect has a significant impact on the performance of a lithium-ion battery. Thus, modeling the thermal process, which always involves unknown boundary heat exchange, is significant to battery management. Two critical issues should be addressed for the thermal process modeling: 1) the nominal model, which is constructed offline, can be updated efficiently to compensate for any online disturbances; and 2) the influence of previous and recent spatiotemporal dynamics may be varying and should be handled properly. Bearing these in mind, in this paper, a spatial correlation-based incremental learning technique is designed for spatiotemporal modeling. First, the incremental learning technique is developed to update the dominant spatial basis functions of the nominal model, which is constructed by a time/space separation-based method. Then, a forgetting factor is incorporated into the incremental learning technique to handle time-varying dynamics. Additionally, the popular approximator, that is, the radial basis function neural network, is utilized to identify the low-dimensional temporal model. Simulations and experiments on a pouch type battery with boundary heat exchange have demonstrated the accuracy and efficiency of the proposed modeling method. [ABSTRACT FROM AUTHOR]

An incremental spatiotemporal learning scheme is proposed for online modeling of distributed parameter systems (DPSs). A novel incremental learning method is developed to recursively update the spatial basis functions and the corresponding temporal model based on the Karhunen–Loève decomposition for time-space separation. The time-space synthesis continually evolves by adding new increment data with more updated information and revising the existing parameters of the dynamic system. In this way, the spatiotemporal structure is inherited and updated efficiently as output data increases over time. The adaptive nature of this evolving structure makes it promising for online modeling of DPSs under streaming data environment. The proposed incremental modeling scheme is evaluated on the classical benchmark of a catalytic rod problem. The simulation results demonstrate the viability and efficiency of the proposed method for online modeling of DPSs. [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]

In this paper, the exponential stabilization problem is addressed for a class of nonlinear parabolic partial differential equation (PDE) systems via sampled-data fuzzy control approach. Initially, the nonlinear PDE system is accurately represented by the Takagi–Sugeno (T–S) fuzzy PDE model. Then, based on the T–S fuzzy PDE model, a novel time-dependent Lyapunov functional is used to design a sampled-data fuzzy controller under spatially point measurements such that the closed-loop fuzzy PDE system is exponentially stable with a given decay rate. The stabilization conditions are presented in terms of a set of linear matrix inequalities (LMIs). Finally, simulation results on the control of the diffusion equation and the FitzHugh–Nagumo (FHN) equation to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is developed to detect fault and identify its spatial location for a class of parabolic distributed parameter systems (DPSs) with limited sensors. The normal situation of the DPSs is first modeled under limited sensors. Then, the spatio–temporal dynamics of DPSs is decoupled under time/space separation. After the temporal coefficients are further decomposed using the independent component analysis method, the dominant temporal components are taken and then the spatial residual errors are used to form two monitoring statistics. Using the kernel density function, the confidence bounds of these two statistics (fault free) can be established as the reference signals. Unlike model-based fault detection methods that require explicit mathematical models of the processes, the proposed method is data driven and only utilizes the separable characteristic of parabolic DPSs. Experiments on a typical parabolic process and a snap curing oven are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Distributed parameter systems widely exist in many industrial thermal processes. Estimation of their temperature distribution in the entire operating area is not easy as the dynamics are time/space coupled, and there are only a few sensors available for measurement. In this paper, an effective spatiotemporal model is proposed for prediction of the temperature distribution. After the dominant spatial basis functions are obtained by the Karhunen–Loève method under the time/space separation, a kernel-based random vector functional-link network is developed for learning unknown temporal dynamics. After time/space synthesis, the spatiotemporal model can be constructed to effectively estimate the temperature distribution in high learning speed. The generalization performance of this model is discussed using Rademacher complexity. Simulations on two typical industrial thermal processes show that the proposed method has superior model performance than neural networks and least square support vector machine. [ABSTRACT FROM AUTHOR]

The temperature distribution of a lithium-ion battery (LIB) belongs to a nonlinearly distributed parameter system (DPS), which is infinite dimensional in the space direction. Modeling of such systems often leads to the following challenges: time/space-coupled dynamics; time-varying dynamics; and spatial nonlinearity. For the first two challenges, Karhunen–Loève (KL) decomposition based data-driven spatiotemporal models have been widely researched and successfully applied to industrial thermal processes. However, this method is a global linear model reduction method that ignores the nonlinear properties of measurements. This will prohibit the model performance of a strong nonlinear DPS, which has strong spatial nonlinearity refers to the aforementioned third challenge. To address this problem, a local-properties-embedding-based modeling method is developed for the nonlinear DPS. First, the finite-dimensional nonlinear spatial basis functions that can be the representative of the nonlinear feature of the original space are learned by the local-properties-embedding-based method. Then, the low-dimensional representative can be derived using the time/space separation and the unknown temporal coefficients can be identified through the traditional neural learning algorithm. Finally, the spatiotemporal temperature distribution can be reconstructed by the time/space synthesis. Since the nonlinear structure feature of the spatiotemporal datasets has been considered, the proposed modeling method can be more effective than the traditional KL-based method for modeling the nonlinear DPS. Numerical simulations on the LIB showed that the proposed methods are more robust than the KL-based method in both the time direction and the space direction. [ABSTRACT FROM AUTHOR]

The jetting technology has become popular in microelectronic packaging owing to its high efficiency and the noncontact property. However, high-speed jetting will generate strong oscillation that will affect the jetting consistency. To improve the jetting consistency, the optimal jetting configuration will be designed in this paper. First, the optimal jetting structure is proposed. Then, jetting performance under the optimal design can be analyzed quantitatively and shows that the robust closure of the nozzle can be achieved under disturbance. Finally, the jetting consistency will be studied under the physics-based simulation using data collected from real jetting experiment. The simulation experiments show that the jetting consistency under the optimal design can be greatly improved. [ABSTRACT FROM AUTHOR]

Many disturbance rejection methods have been proposed for processes with dead-time, while these existing methods may not work well under multiple periodic disturbances. In this paper, a multiple periodic disturbance rejection is proposed under the Smith predictor configuration for processes with long dead-time. One feedback loop is added to compensate periodic disturbance while retaining the advantage of the Smith predictor. With information of the disturbance spectrum, the added feedback loop can remove multiple periodic disturbances effectively. The robust stability can be easily maintained through the rigorous analysis. Finally, simulation examples demonstrate the effectiveness and robustness of the proposed method for processes with long dead-time. [ABSTRACT FROM AUTHOR]

Jet dispensing has been widely considered as the next generation of dispensing technology. Since there is no online measurement of the droplet volume in the jetting process, the dispensing usually operates in open loop without any online control. The droplet consistency has been a long-standing challenge. As the jetting process is driven by the needle motion, the dispensed volume will be greatly affected by the needle motion. To monitor the jetting process, a novel measurement mechanism is designed for sensing the needle motion of the jet dispenser. Using the space/time separation method, the complex distributed fluid flow model is approximated by a simple analytical model. With the added motion measurement, this hybrid sensing/model approach can serve for online estimation of the dispensing performance. The model parameters can be identified using the experimental data. The experimental study verifies that the proposed method can satisfactorily estimate the dispensed volume in real-time operation. [ABSTRACT FROM AUTHOR]

This paper gives a membership-function-dependent approach to solve the design problem of fuzzy pointwise state feedback controller for a class of nonlinear distributed parameter systems modeled by semilinear parabolic partial differential equations (PDEs), where only a few actuators are discretely distributed in space. In the proposed design method, a Takagi–Sugeno (T–S) fuzzy PDE model obtained by using the sector nonlinearity method is first utilized to accurately describe the nonlinear spatiotemporal dynamics of the PDE system. As only the state information at some known specified points in the spatial domain (i.e., the pointwise state information) is available for the controller design, the favorable property offered by sharing all the same premises in the fuzzy PDE plant model and fuzzy controller cannot be employed to develop the fuzzy control design method. To overcome this drawback, a linear matrix inequality (LMI) relaxation technique is developed to enhance the stabilization ability of the fuzzy controller. Based on the T–S fuzzy PDE model, a membership-function-dependent fuzzy pointwise state feedback control design is then proposed by employing the Lyapunov technique, integration by parts, the vector-valued Wirtinger’s inequality and the LMI relaxation technique, and presented in term of standard LMIs. Finally, the satisfactory and better performance of the proposed design method are demonstrated by the extensive numerical simulation results of two numerical examples. [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]

This paper presents a real-time estimation method for the temperature distribution of cylindrical batteries under boundary air cooling. A space-/time-separation-based analytical model is developed using Karhunen–Loève decomposition and Galerkin's method. The model parameters can be identified and optimized using data-based approaches. The developed analytical model demonstrates the robustness to variation of thermal parameters. However, the change of boundary cooling will significantly degrade the performance of the developed analytical model. For the known boundary cooling, the compensation model for cooling effects can be derived to improve the modeling performance. For the unknown boundary cooling in real practice, a dual-extended Kalman filter can be used to simultaneously estimate coupled parameters and convection coefficient in the compensation model. The proposed method can achieve satisfactory performance in the battery duty-cycle experiments. [ABSTRACT FROM PUBLISHER]

This paper presents a deep learning-based model reduction method for distributed parameter systems (DPSs). The proposed method includes three phases. In phase I, numerical or experimental data of the spatiotemporal distribution is reduced into low-dimensional representations using the deep auto-encoder (DAE). In phase II, the low-dimensional representations are used to establish the reduced-order model. In phase III, the reduced model is then used to reconstruct the high-dimensional DPS. Experimental studies are conducted to validate the proposed method. The proposed method is compared with the classical proper orthogonal decomposition method and demonstrates better modeling accuracy and efficiency in the experiments. [ABSTRACT FROM PUBLISHER]

The least squares support vector machine (LS-SVM) has emerged as a popular data-driven modeling method and been extensively studied in the machine learning community. However, the LS-SVM is sensitive to noisy data and may not be effective when the level of noise is high. In this paper, a probabilistic LS-SVM is proposed to have a more reliable performance. First, a distributed LS-SVM is constructed with parameters estimated from data samples. Due to distributed nature of multiple LS-SVM, the stochastic property of parameters can be easily obtained and processed. Using the distribution characteristics of these parameters, the final outcome is derived through the probabilistic inference and thus be evaluated statistically. Its parallel structure is also suitable for parallel computing to reduce computing time. Both simulations and experiments demonstrate the effectiveness of the proposed probabilistic LS-SVM. [ABSTRACT FROM PUBLISHER]

A fuzzy guaranteed cost sampled-data control problem is addressed in this paper for a class of nonlinear coupled systems of an n -dimensional lumped parameter system modeled by ordinary differential equation (ODE) with a scalar reaction–diffusion process represented by parabolic partial differential equation (PDE). A Takagi–Sugeno (T–S) fuzzy coupled ODE–PDE model is initially proposed to accurately represent the nonlinear coupled ODE–PDE system. Based on the T–S fuzzy coupled ODE–PDE model, a fuzzy sampled-data controller is subsequently developed via the Lyapunov's direct method to not only locally exponentially stabilize the nonlinear coupled system, but also provide an upper bound of the given cost function. Moreover, a suboptimal fuzzy sampled-data control design is also addressed in the sense of minimizing the upper bound of the cost function. The main contribution of this study lies in that a new parameterized linear matrix inequality (LMI) technique is proposed to reduce the conservativeness of the method, and an LMI-based fuzzy suboptimal sampled-data control design is developed for the nonlinear coupled ODE–PDE system based on this parameterized LMI technique. Simulation results on the sampled-data control of hypersonic rocket car are provided to illustrate the effectiveness and merit of the design method. [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]

Electrochemical properties of the battery are described in partial differential equations that are impossible to compute online. These internal states are spatially distributed and thus difficult to measure in the battery operation. A space-time separation method is applied to model the electrochemical properties of the battery with the help of the extended Kalman filter. The model is efficiently optimized by using LASSO adaptation method and can be updated through data-based learning. The analytical model derived is able to offer a fast estimation of internal states of the battery, and thus has potential to become a prediction model for battery management system. [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]

Due to the overwhelming complexity of the electrochemical related behaviors and internal structure of lithium ion batteries, it is difficult to obtain an accurate mathematical expression of their thermal dynamics based on the physical principal. In this paper, a data based thermal model which is suitable for online temperature distribution estimation is proposed for lithium-ion batteries. Based on the physics based model, a simple but effective low order model is obtained using the Karhunen–Loeve decomposition method. The corresponding uncertain chemical related heat generation term in the low order model is approximated using extreme learning machine. All uncertain parameters in the low order model can be determined analytically in a linear way. Finally, the temperature distribution of the whole battery can be estimated in real time based on the identified low order model. Simulation results demonstrate the effectiveness of the proposed model. The simple training process of the model makes it superior for onboard application. [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]

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]

If no external control is employed, the cure performance will be fully dependent on the oven design. Design of such a system is challenging because of its inherent complexity: complex heat transfer, unknown relationship between design variable and curing performance, unknown boundary conditions and large parameter variation, and so on. In this paper, a novel data-based robust design approach is proposed to design a new curing oven with simple structure. First, a modeling method with hybrid singular value decomposition and response surface method is developed to extract the system model from experimental data. The coupling influence between design variable and model parameter is also separated to make the design simpler, because only the part affected by design variable will be considered in the design. A robust design method is then developed to minimize the influence of parameter variation on the performance. Finally, the effectiveness of the proposed method is demonstrated in the design of the new curing oven. [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]

Many industrial processes are nonlinear distributed parametersystems(DPSs). Data-based spatiotemporal modeling is required for analysisand control when the first-principles model is unknown. Because aDPS is infinite-dimensional and time–space coupled, a low-ordermodel is necessary for prediction and control in practice. For low-ordermodeling, traditional principal component analysis (PCA) is oftenused for dimension reduction and time–space separation. However,it is a linear method and leads to only one set of fixed spatial basisfunctions. Therefore, it might not be always effective for nonlinearsystems. In this study, a spatiotemporal multimodeling approach isproposed for unknown nonlinear DPSs. First, multimodel decompositionis performed, where probabilistic PCA (PPCA) is used to obtain multiplesets of spatial basis functions from the experimental data by maximizinga likelihood function. Using these multiple sets of PCA spatial basesfor time–space separation, the high-dimensionality spatiotemporaldata can be reduced to multiple sets of low-dimensionality temporalseries. Then, multiple low-order neural models can be easily establishedto model these local dynamics. Finally, the original spatiotemporaldynamics can be reconstructed by multimodel synthesis. Because theproposed spatiotemporal modeling approach involves a multimodelingmechanism, it can achieve better performance than the traditionalPCA-based single-modeling for nonlinear DPSs, which is demonstratedby numerical simulations. [ABSTRACT FROM AUTHOR]

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]

A novel integration of design and control is proposed for the nonlinear process under uncertainty. The fuzzy modeling method is first employed to approximate the process, upon which fuzzy control rules are developed to achieve the stability, robustness and feasibility. Then, the steady-state economic design and the control system design are integrated into a unified objective function, which can guarantee the desirable economic and dynamic performances. Finally, the proposed method is compared with the traditional sequential method and an existing integration method on controlling the temperature profile of a nonlinear curing process. The comparison demonstrates that the proposed method will have the better performances than the other two methods. [ABSTRACT FROM AUTHOR]

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]

This paper investigates the generalized synchronization issue for two different dimensional chaotic systems with unknown parameters. Based on Lyapunov stability theory and adaptive control theory, an adaptive controller is derived to achieve the generalized synchronization whether the dimension of drive system is greater than the one of the response system or not. Meanwhile, corresponding parameter updating laws can be obtained so as to exactly identify uncertain parameters. This technique has been successfully applied to two examples, the generalized synchronization of hyperchaotic Rössler system and chaotic Lorenz system, chaotic Chen system and generalized Lorenz system. Numerical simulations are finally shown to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]