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This study addresses adaptive fuzzy control for an axially moving slung-load cable system (AMSLCS) of a helicopter in the presence of an actuator fault, system uncertainty, and disturbances with the aid of a fuzzy logic system (FLS). The actuator fault considered is depicted by a more general faulty plant that includes an unknown actuator gain fault and a fault deviation vector. First, to compensate for system uncertainty and the fault deviation vector, a fuzzy control technique is adopted. Then, under the introduced FLS, a novel adaptive fuzzy control law is developed by employing a rigorous Lyapunov derivation. The closed-loop system of the AMSLCS is proved to be uniformly bounded even when considering the actuator fault, system uncertainty, and disturbances. Finally, a simulation is executed to expound the performance of the developed controller. [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]

Due to the lightly damped resonance and intrinsic nonlinearities, it is difficult for the piezoactuated nanopositioning stage to realize high-bandwidth and high-accuracy control. To handle these limitations, in this work, a dual-loop control scheme based on state-feedback-based modal method is designed to both actively damp and stiffen the resonant mode and to suppress the effects of nonlinearities of the piezoactuated nanopositioning stage. In this scheme, the state-feedback-based modal controller is first designed in the inner loop to enlarge both the damping ratio and natural frequency of the first resonant mode. Then, a proportional–integral (PI) controller is utilized in the outer loop for eliminating the tracking errors caused by other disturbances and nonlinearities including hysteresis and creep. To maximize the control bandwidth of system under the proposed dual-loop scheme, an optimization method is thus proposed for simultaneously tuning the parameters of the inner and the outer loop controllers. Finally, to validate the proposed dual-loop control scheme, comparative experiments are carried out on a piezoactuated nanopositioning stage. Results demonstrate that the proposed control scheme improves the bandwidth of the system from 497 Hz (with PI control) and 1543 Hz (with a commonly used positive acceleration, velocity, and position damping control and a PI controller) to 6546 Hz, which is 664 Hz larger than the first resonant frequency of the original system, validating the effectiveness of the proposed dual-loop scheme on high-bandwidth control. Note to Practitioners—The demand of high-bandwidth and high-accuracy piezoactuated nanopositioning stages increases rapidly. However, the lightly damped resonance of the mechanism and the intrinsic nonlinearities of the piezoelectric actuator limit the tracking performance of the stage. A dual-loop control structure is adopted in this work to improve the tracking performance of the nanopositioning stage. Different from most of the vibration control methods proposed in the literature which aimed only at improving the damping ratio, a state-feedback-based modal controller is designed in the inner-loop for improving both the damping ratio and the stiffness of the system. This task is realized by re-placing the resonant poles of the system to the optimized location. The outer-loop controller adopts the high-gain PI control for eliminating the tracking errors. More importantly, in order to realize the high-bandwidth and high-accuracy control, a numerical optimization method is proposed for simultaneously tuning the parameters of the controllers in inner and outer loops. The controller design is simple, and it can be applied to other systems with second or higher order in which the first resonant mode dominates the system dynamics. [ABSTRACT FROM AUTHOR]

Fault detection for distributed parameter processes (heat processes, fluid processes, etc.) is vital for safe and efficient operation. On one hand, the existing data-driven methods neglect the evolution dynamics of the processes and cannot guarantee that they work for highly dynamic or transient processes; on the other hand, model-based methods reported so far are mostly based on the backstepping technique, which does not possess enough redundancy for fault detection since only the boundary measurement is considered. Motivated by these considerations, we intend to investigate the robust fault detection problem for distributed parameter processes in a model-based perspective covering both boundary and in-domain measurement cases. A real-time fault detection filter (FDF) is presented, which gets rid of a large amount of data collection and offline training procedures. Rigorous theoretic analysis is presented for guiding the parameters selection and threshold computation. A time-varying threshold is designed such that the false alarm in the transient stage can be avoided. Successful application results on a hot strip mill cooling system demonstrate the potential for real industrial applications. [ABSTRACT FROM AUTHOR]

In this study, we propose an adaptive fuzzy event-triggered control scheme for an autonomous aerial refueling hose system involving uncertainty, an event-triggered mechanism, and actuator failures. The unknown nonlinear function is approximated using the designed fuzzy logic systems. Through introduction of the adaptive compensation scheme, the problem of an infinite number of actuator failures, including partial and complete failures, is solved. In addition, the event-triggered control strategy is designed to achieve vibration suppression while decreasing the communication burden between the controllers and actuators. The stability of the closed-loop system is demonstrated via the Lyapunov direct method. Finally, simulation examples are presented to confirm the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

A spatial-construction-based fault diagnosis method is proposed to detect and locate the abnormality for unknown distributed parameter systems (DPSs). To accurately locate the abnormality, the continuous spatial basis functions (SBFs) are derived by the proposed spatial construction method from empirical data. Theoretical analysis proves that the B-spline curve is a proper solution to the spatial construction problem. Two new statistics are constructed based on the derived continuous SBFs and the improved independent component analysis algorithm. The abnormality can be timely detected according to the reference signals derived by the central limit theorem and hypothesis testing. With the continuous SBFs, the probability distribution of statistic contribution can be constructed to reveal the actual position of the abnormality. The proposed method can timely detect and locate the abnormality under fewer sensors without the knowledge of PDE and boundary conditions. The internal short circuit experiment on a lithium-ion battery demonstrates the effectiveness and superiority of the proposed method. [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]

This article investigates the adaptive robust spatial vibration control for a flexible Timoshenko manipulator subject to input dead-zone nonlinearity characteristic. The “disturbance-like” terms and dead-zone nonlinearity are first incorporated into the context of control design, and the new boundary robust adaptive control laws are constructed to reduce the shear deformation and elastic oscillation, ensure the expected angle orientation, handle the input dead-zone, and estimate the upper bound of compound disturbances. The convergence of states and the stability of the system are analyzed and proven without simplifying the infinite dimensional dynamics. In the end, the effectiveness of the presented scheme is demonstrated by the result of simulation research. [ABSTRACT FROM AUTHOR]

The thermal effect has a significant impact on the performance and durability of lithium-ion batteries. This article proposes a systematic approach for fast modeling of the distributed battery thermal process. In this method, the time/space (T/S) separation is adopted to decompose the spatio-temporal thermal dynamics. Under the T/S separation, an incremental-learning-based regulator is first employed for the recursive update of spatial basis functions, which can represent the most recent spatial complexity. Then, a corresponding temporal model with incremental adaptive characteristics is developed to capture the temporal nonlinearity. Under such a fully adaptive modeling pattern, the desired temperature distribution can be reconstructed with high efficiency and flexibility. Experimental studies indicate that the proposed method can achieve satisfactory modeling performance while its computational efficiency is outstanding compared to peer methods. [ABSTRACT FROM AUTHOR]

This article considers the synchronization problem of delayed reaction–diffusion neural networks via quantized sampled-data (SD) control under spatially point measurements (SPMs), where distributed and discrete delays are considered. The synchronization scheme, which takes into account the communication limitations of quantization and variable sampling, is based on SPMs and only available in a finite number of fixed spatial points. By utilizing inequality techniques and Lyapunov–Krasovskii functional, some synchronization criteria via a quantized SD controller under SPMs are established and presented by linear matrix inequalities, which can ensure the exponential stability of the synchronization error system containing the drive and response dynamics. Finally, two numerical examples are offered to support the proposed quantized SD synchronization method. [ABSTRACT FROM AUTHOR]

Compared to the fault detection problem, fault localization for spatially distributed processes is physically intuitive but was seldom studied. The main challenge lies in the contradiction between the spatially distributed nature of the processes and a limited number of sensors that can be placed in the space for applications. In this article, we make the first attempt to systematically study the fault localization problem for a class of nonlinear spatially distributed processes without knowing any prior information of the spatial distribution function of the spatio‐temporal fault. The main contribution is that a spatial mapping filter is first designed to generate a distributed residual signal which contains spatial information while using limited sensor measurements. The residual evaluation procedure is also conducted in a distributed manner. With these two parts, a systematic methodology framework is constructed and analyzed rigorously. Finally, the performance of the proposed method is evaluated on a faulty catalytic rod, which widely exists in chemical engineering. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used as power sources in industrial applications. Electrochemical models and simulations are crucial to disclose many details that cannot be directly measured through experiments. Parameter identification of an accurate electrochemical model is much more cost-effective than direct and destructive measurement methods. However, the complex structure and strong nonlinearity of electrochemical models will make the parameter identification very difficult. Additionally, time-consuming electrochemical simulations can significantly limit the identification efficiency. This article proposes a surrogate-model-based scheme to achieve high-efficiency parameter identification of an electrochemical battery model. To be specific, the proposed method is implemented by the close integration of an evolutionary algorithm and a surrogate model. A sensitivity-based identification strategy is first designed to alleviate the difficulty of optimization. Then, a surrogate model is developed from historical data to gradually approach the objective function used for parameter evaluations. Finally, an evolutionary algorithm is employed to find promising solutions by minimizing the output of the surrogate model. Simulations and experimental studies demonstrate the effectiveness and high efficiency of the proposed method. [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]

The resonant controller (RC), as a promising candidate for high-speed nonraster nanopositioning applications, can track the sinusoidal reference with zero steady-state error. This article presents a controller composed of several RCs in parallel for tracking nonraster sequential scanning trajectories. The selection for each RC in the parallel array is based on two considerations: one is the spectrum of the reference signal and the other is the harmonics caused by the nonlinearities of the nanopositioning stage. The performance of RC is highly dependent on the accurate placement of the resonant poles, but unfortunately, many existing digital implementation methods could cause a deviation of the resonant poles from their initial locations. To address this problem, a modified Tustin (MTus) method is proposed in this article to implement the controller with better accuracy. Furthermore, the fractional-order (FO) calculus is introduced to improve the transient performance of the RCs. To validate the proposed methods, a comprehensive examination of several types of the nonraster sequential scanning trajectories with a wide frequency range has been carried out on a nanopositioning stage. The results have been compared with other methods, showing that the tracking errors are reduced significantly under the controller implemented by the MTus method especially in high-frequency conditions and that the application of the FO calculus reduces the settling time of the controller by more than 30% in most cases. Note to Practitioners—The demand for high-speed atomic force microscope (AFM) increases rapidly. However, the commonly used raster trajectory limits the achievable scan speed of the AFM. An effective way to improve the scanning and imaging speed of the AFM is the application of the sequential nonraster scanning methods. The trajectories of sequential nonraster scanning patterns mainly composed of few sinusoid signals with different frequencies. Therefore, the resonant controller (RC) is introduced in this article as it is capable to track the sinusoidal reference with zero steady-state error. Several RCs are selected first based on the spectrum analysis of the reference and the consideration of the harmonics caused by the system nonlinearities, and then, they are connected in parallel to form the controller for precise tracking of the reference. In order to realize the digital implementation of the designed controller, a modified Tustin discretization method is proposed, which ensures the accurate resonant pole placement of the RC and thus maintains the tracking performance of the RC. In addition, the fractional-order (FO) calculus is introduced to speed up the convergence of the designed controller while preserving the tracking accuracy, and this parallel-structure FO RC (PSFORC) design can be implemented to other systems that require high-speed and high-accuracy tracking of the periodic signals. [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]

This article presents a control scheme for stabilizing a vibrating flexible hose used for aerial refueling subject to bounded actuators for the rate and magnitude. A dynamical model of hose systems is captured by partial differential equations (PDEs). Based on the PDE model, a novel boundary control law is proposed to dampen the flexible hose's vibration. The backstepping approach is utilized to devise the control scheme, with the bounded input magnitude and derivative handled by adopting the smooth hyperbolic tangent function. The Lyapunov criterion is exploited to demonstrate the stability of the controlled system. Finally, simulation results are used to evaluate the validity of the derived scheme. [ABSTRACT FROM AUTHOR]

This paper introduces a fuzzy control (FC) under spatially local averaged measurements (SLAMs) for nonlinear-delayed distributed parameter systems (DDPSs) represented by parabolic partial differential-difference equations (PDdEs), where the fast-varying time delay and slow-varying one are considered. A Takagi–Sugeno (T–S) fuzzy PDdE model is first derived to exactly describe the nonlinear DDPSs. Then, by virtue of the T–S fuzzy PDdE model and a Lyapunov–Krasovskii functional, an FC design under SLAMs, where the membership functions of the proposed FC law are determined by the measurement output and independent of the fuzzy PDdE plant model, is developed on basis of spatial linear matrix inequalities (SLMIs) to guarantee the exponential stability for the resulting closed-loop DDPSs. Lastly, a numerical example is offered to support the presented approach. [ABSTRACT FROM AUTHOR]

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework. [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 construction of spatial basis functions (BFs) is critical to the time/space separation of the distributed parameter system (DPS). The spatial BFs constructed by traditional Karhunen–Loéve may not work satisfactorily for two spatial-dimensional (2-D) DPS, because of a distorted mapping of the original sensor array in the row-wise vectorization process. In this article, a novel time/space separation based method is proposed to construct dimension embedded BFs (DE-BFs) for modeling 2-D DPS. The DE-BFs are first formulated according to the spatial sensor array structure, and sequentially optimized with alternating least squares by minimizing the reconstruction error. The mapping relationship between the DE-BFs and the spatial sensor array is well preserved. In addition, the coupling across temporal and different spatial dimensions is sufficiently captured. A satisfactory model accuracy can be achieved by the DE-BFs, even with limited training data. Experiments of a 2-D curing thermal process are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers the estimator-based $H_{\infty }$ sampled-data fuzzy control (SDFC) problem of nonlinear parabolic partial differential equation (PDE) systems. First, a Takagi–Sugeno (T–S) fuzzy parabolic PDE model is proposed to represent the nonlinear PDE system. Second, with the aid of the T–S fuzzy PDE model, an estimator-based SDFC design ensuring the exponential stability of the closed-loop fuzzy PDE system with an $H_{\infty }$ performance is developed via a Lyapunov functional. The outcome of the estimator-based $H_{\infty }$ SDFC problem is formulated as a bilinear matrix inequality optimization problem, which is solved by an iterative algorithm on the basis of the linear matrix inequalities. Finally, for demonstrating the effectiveness of the proposed method, simulation results are provided to control the diffusion equation and the FitzHugh–Nagumo equation. [ABSTRACT FROM AUTHOR]

The aim of this article is to establish a novel boundary antidisturbance control to dampen the oscillation of a spatially nonlinear flexible string system influenced by unknown disturbances. To realize this, new disturbance rejection controllers are constructed to effectively suppress the external disturbances for vibration reduction with the help of a newly designed disturbance observer. Adopting Lyapunov direct method, we ensure that the derived control system is uniformly bounded stable. Simulations are also performed to demonstrate the feasibility and validity of the suggested strategy. [ABSTRACT FROM AUTHOR]

A reinforcement learning-based method is proposed for optimal sensor placement in the spatial domain for modeling distributed parameter systems (DPSs). First, a low-dimensional subspace, derived by Karhunen–Loève decomposition, is identified to capture the dominant dynamic features of the DPS. Second, a spatial objective function is proposed for the sensor placement. This function is defined in the obtained low-dimensional subspace by exploiting the time-space separation property of distributed processes, and in turn aims at minimizing the modeling error over the entire time and space domain. Third, the sensor placement configuration is mathematically formulated as a Markov decision process (MDP) with specified elements. Finally, the sensor locations are optimized through learning the optimal policies of the MDP according to the spatial objective function. The experimental results of a simulated catalytic rod and a real snap curing oven system are provided to demonstrate the feasibility and efficiency of the proposed method in solving the combinatorial optimization problems, such as optimal sensor placement. [ABSTRACT FROM AUTHOR]

This article provides a framework of dead zone compensation and robust adaptive vibration control for uncertain spatial flexible riser systems. First, nonsymmetric dead zone nonlinearity is represented in the form of the desired control input with the addition of an extra nonlinear input error. Second, by visualizing those input errors and extrinsic disturbances as an unknown “disturbance-like” term, a new robust adaptive vibration control technology and online updating laws can be constructed for riser systems to guarantee the oscillation reduction and compensation of uncertainties and dead zone. Third, the constructed control ensures and achieves bounded Lyapunov stability in the controlled system. Ultimately, control performances are demonstrated with appropriate design parameters. [ABSTRACT FROM AUTHOR]

A novel data-driven approach is proposed to localize the abnormality for distributed parameter systems (DPSs) in this paper. The cross-correlation order of DPSs in the space domain is first obtained by the cumulants-based identification method. Then, a spatial augmented matrix of the spatial–temporal distribution data is formed and a dynamic spatial independent component analysis method is proposed for independent decomposition. The dominant spatial independent components will be extracted and the spatial residuals can be produced for spatial reference statistics. Through the kernel density estimation method, the confidence bounds of these statistics in normal condition (abnormality free) can be established as the spatial references. These unique two references will guarantee the reliable spatial localization of abnormality. Different from model-based methods that rely on an explicit system model of the process, the proposed approach is model free and only uses recorded process data. Experiments on two typical DPSs demonstrate the effectiveness of the proposed approach. [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]

Jetting technology has become popular in microelectronic packaging due to its high efficiency and noncontact property. Its performance is significantly affected by the structure design. However, it is very difficult and time-consuming to conduct the optimal design under the traditional approaches, especially when more than one performance criteria is required. A surrogate model-based design method is proposed to optimize structure parameters under two different performance criteria. The surrogate model can serve for the quick evaluation of the process performance, upon which the process can be optimally designed via iterative search. As two performance indexes are required in the jetting process, a biobjective optimization method is used to design multiple parameters concurrently. Under this new design approach, the design can be easier to complete, and the jetting performance is significantly improved in both simulation study and experiment testing. [ABSTRACT FROM AUTHOR]

Rapid thermal processing (RTP) is an important process in the fabrication of semiconductor devices. It is difficult to achieve temperature uniformity control of the wafer in RTP since the system is a highly nonlinear process with strong spatial distribution. In this study, a transfer learning-based three-dimensional (3D) fuzzy multivariable control scheme is proposed for the temperature uniformity control of an RTP system. In difference to the traditional expert-knowledge based design, a two-level framework of transfer learning methodology is constructed to design the 3D fuzzy multivariable controller (3D FMC) with the help of a multi-output support vector regression (M-SVR). The 3D FMC defines a qualitative spatial fuzzy structure that will be transferred to the M-SVR. On the other hand, the structure parameters of the M-SVR will be learned from data and transferred to design quantitative parameters of the 3D FMC. Under the framework of transfer learning, the control laws (e.g. human control experience) hidden in spatio-temporal data can be extracted and formulated back into multi-output 3D fuzzy rules. The proposed method provides an effective integration of the spatial fuzzy inference and the transfer learning for 3D FLC design. The newly developed method is applied to the temperature uniformity control of a rapid thermal chemical vapor deposition (RTCVD) system at the set temperature 1000K, and the maximum non-uniformity along the wafer radius is close to 1K. [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]

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]

This paper presents a Lyapunov and partial differential equation (PDE)-based methodology to solve static collocated piecewise fuzzy control design of quasi-linear parabolic PDE systems subject to periodic boundary conditions. Two types of piecewise control, i.e., globally piecewise control and locally piecewise control are considered, respectively. A Takagi–Sugeno (T–S) fuzzy PDE model that is constructed via local sector nonlinearity method is first employed to accurately describe spatiotemporal dynamics of quasi-linear PDEs. Based on the T–S fuzzy PDE model, a static collocated piecewise fuzzy feedback controller is constructed to guarantee the locally exponential stability of the resulting closed-loop system. Sufficient conditions for the existence of such fuzzy controller are developed by applying vector-valued Poincaré–Wirtinger inequality and its variants and a linear matrix inequality (LMI) relaxation technique. These sufficient conditions are presented in terms of standard LMIs. Finally, the performance of the suggested fuzzy controller is illustrated by numerical simulation results of a nonlinear PDE system described by quasi-linear FitzHugh–Nagumo equation with periodic boundary conditions. [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]

Time/space separation based spatiotemporal modeling methods have been proven to be effective and efficient for modeling a class of distributed parameter systems (DPSs). However, these conventional methods may not work satisfactorily for DPSs with time-dependent boundary conditions. A sliding window based dynamic spatiotemporal modeling method is proposed for this kind of DPSs. First, the sliding window is appropriately designed to capture the most recent spatiotemporal data. Then, the conventional Karhunen–Loève method can be used to construct the analytical model. Besides, a more general sliding window method can be achieved by using a forgetting factor to adjust different influence of the current and previous data. This analytical model can be utilized for online performance prediction. Simulation experiments on a benchmark and a battery with unknown boundary cooling have demonstrated the superior performance of the proposed method on the DPSs with time-dependent boundary conditions. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an H∞ sampled‐data control problem is addressed for semilinear parabolic partial differential equation (PDE) systems. By using a time‐dependent Lyapunov functional and vector Poincare's inequality, a sampled‐data controller under spatially averaged measurements is developed to stabilize exponentially the PDE system with an H∞ control performance. The stabilization condition is presented in terms of a set of linear matrix inequalities. Finally, simulation results on the control of the diffusion equation and the FitzHugh‐Nagumo equation are given to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

In this paper, a novel incremental learning algorithm is presented for reinforcement learning (RL) in dynamic environments, where the rewards of state-action pairs may change over time. The proposed incremental RL (IRL) algorithm learns from the dynamic environments without making any assumptions or having any prior knowledge about the ever-changing environment. First, IRL generates a detector-agent to detect the changed part of the environment (drift environment) by executing a virtual RL process. Then, the agent gives priority to the drift environment and its neighbor environment for iteratively updating their state-action value functions using new rewards by dynamic programming. After the prioritized sweeping process, IRL restarts a canonical learning process to obtain a new optimal policy adapting to the new environment. The novelty is that IRL fuses the new information into the existing knowledge system incrementally as well as weakening the conflict between them. The IRL algorithm is compared to two direct approaches and various state-of-the-art transfer learning methods for classical maze navigation problems and an intelligent warehouse with multiple robots. The experimental results verify that IRL can effectively improve the adaptability and efficiency of RL algorithms in dynamic environments. [ABSTRACT FROM AUTHOR]

Data-based spatio-temporal modeling has rapidly developed over the past ten years. However, traditional spatio-temporal modeling methods will encounter some uncertainty caused by model reduction. In addition, the established model is complicated and short of linguistic interpretability. In this study, a novel three-dimensional (3-D) fuzzy modeling framework without model reduction is proposed and a new 3-D fuzzy modeling method based on clustering and support vector regression is developed. This method is based on a 3-D fuzzy system that naturally fuses time/space separation and time/space synthesis into a unified framework. Utilizing the machine learning algorithms (clustering and support vector regression), a 3-D fuzzy system is constructed for modeling an unknown nonlinear distributed parameter system. The advantages of the proposed modeling method over the traditional spatio-temporal modeling method are linguistic interpretability and no reliance on model reduction. The proposed modeling method consists of three steps. First, the nearest neighborhood clustering algorithm is used to learn initial structure model of antecedent sets of 3-D fuzzy rules. Then, similarity measure is used to simplify the initial structure via combining similar fuzzy sets and similar fuzzy rules. Finally, a support vector regression algorithm is applied to calculate spatial functions in the consequent sets of 3-D fuzzy rules. The simulation results demonstrate the effectiveness of the proposed modeling method. [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]

This paper employs a Takagi–Sugeno (T-S) fuzzy partial differential equation (PDE) model to solve the problem of sampled-data exponential stabilization in the sense of spatial $L^\infty$ norm $\Vert \cdot \Vert _\infty$ for a class of nonlinear parabolic distributed parameter systems (DPSs), where only a few actuators and sensors are discretely distributed in space. Initially, a T-S fuzzy PDE model is assumed to be derived by the sector nonlinearity method to accurately describe complex spatiotemporal dynamics of the nonlinear DPSs. Subsequently, a static sampled-data fuzzy local state feedback controller is constructed based on the T-S fuzzy PDE model. By constructing an appropriate Lyapunov–Krasovskii functional candidate and employing vector-valued Wirtinger's inequalities, a variation of vector-valued Poincaré–Wirtinger inequality in one-dimensional spatial domain, as well as a vector-valued Agmon's inequality, it is shown that the suggested sampled-data fuzzy controller exponentially stabilizes the nonlinear DPSs in the sense of $\Vert \cdot \Vert _\infty$ , if sufficient conditions presented in term of standard linear matrix inequalities (LMIs) are fulfilled. Moreover, an LMI relaxation technique is utilized to enhance exponential stabilization ability of the suggested sampled-data fuzzy controller. Finally, the satisfactory and better performance of the suggested sampled-data fuzzy controller are demonstrated by numerical simulation results of two examples. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM) has been extensively studied in the machine learning field and has been widely implemented due to its simplified algorithm and reduced computational costs. However, it is less effective for modeling data with non-Gaussian noise or data containing outliers. Here, a probabilistic regularized ELM is proposed to improve modeling performance with data containing non-Gaussian noise and/or outliers. While traditional ELM minimizes modeling error by using a worst-case scenario principle, the proposed method constructs a new objective function to minimize both mean and variance of this modeling error. Thus, the proposed method considers the modeling error distribution. A solution method is then developed for this new objective function and the proposed method is further proved to be more robust when compared with traditional ELM, even when subject to noise or outliers. Several experimental cases demonstrate that the proposed method has better modeling performance for problems with non-Gaussian noise or outliers. [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]

A new consistency control method for jet dispensing is proposed. First, the working parameters, namely, viscosity, supply pressure and supply time, are experimentally investigated. Then, the glue viscosity is approximated by a polynomial model using the least square method. Based on this model and temperature control implemented using the Dahlin principle, the viscosity of the glue can be maintained at a constant value. Then, the viscosity model of the glue is applied to deriving the droplet mass as the nominal model of the temperature controller. The robustness of the temperature controller is analyzed by applying the small gain theory. The glue supply pressure controller is designed using the consistency control strategy, and the robustness is analyzed. Finally, simulations and experiments are conducted using a jet dispensing control system. The results show that the proposed control strategy can significantly improve the droplet consistency. [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]

Differential evolution (DE) is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms. [ABSTRACT FROM AUTHOR]

The real-time monitoring of temperature distribution in lithium-ion batteries (LIBs) is crucial for their safety and optimal operation in electrical vehicles. An accurate and effective thermal model is needed for online temperature monitoring since limited sensors are available in vehicle application. In this paper, a data-based spatiotemporal modeling method is researched for online estimation of temperature distribution of LIBs. First, Isometric Mapping (ISOMAP) method is used for time/space separation and model reduction. Then, the low-dimensional representation can be obtained in terms of ISOMAP based mapping functions. The unknown temporal dynamics in the low-dimensional space can be approximated using neural network model with parameters trained using extreme learning machine (ELM) algorithm. Finally, the spatiotemporal model of the thermal process can be reconstructed by integrating the neural network model and the mapping functions. The generalization bound of the proposed spatiotemporal model can be analyzed using Rademacher complexity. Simulation results showed that the proposed modeling method can model the LIB thermal process very well. [ABSTRACT FROM PUBLISHER]

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]

The classification problem in the real-world applications always involves uncertainties in both stochastic and fuzzy nature. This paper proposes a classification framework based on the unified probabilistic fuzzy configuration for data with uncertainties in both stochastic and fuzzy nature. The design and tuning procedures are also developed in terms of probability-based performance measure for working in the complex environment. A theoretical analysis is conducted to derive its quantificational model and disclose the interesting features. In addition to a superior performance than the traditional fuzzy method, the proposed method generates probabilistic fuzzy rules that can help users to better understand how the classifier works. This explainable characteristic is crucial for the decision making. Finally, the effectiveness of the proposed classifier will be demonstrated on its application to the Pima Indians Diabetes data and low back pain diagnosis. The satisfactory classification and the explainable characteristic disclose its potential in classification of data with uncertainties. [ABSTRACT FROM AUTHOR]