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This article investigates robust non-fragile event-triggered sampled-data fuzzy (ETSDF) control for uncertain nonlinear delayed distributed parameter systems. Initially, a T-S fuzzy delayed partial differential equation model is presented to exactly describe the uncertain nonlinear delayed distributed parameter systems (DPS). secondly, a robust non-fragile ETSDF control strategy is proposed to reduce the unnecessary SD and cope with the uncertainties in controller and system. Then, based on an appropriate Lyapunov functional, the exponential stability conditions of closed-loop nonlinear delayed DPS via linear matrix inequalities are presented. Lastly, one example is presented to illustrate the design method. [ABSTRACT FROM AUTHOR]

In this paper, we consider the robust non-fragile H ∞ fuzzy control for uncertain nonlinear delayed hyperbolic partial differential equation (PDE) systems, which can represent the dynamics of most industrial processes, such as plug-flow reactors, fixed-bed reactors, tubular heat exchangers, traffic flows, and wave equations. Initially, a Takagi–Sugeno (T–S) fuzzy-delayed hyperbolic PDE model is presented to describe the uncertain nonlinear delayed hyperbolic PDE system. Subsequently, based on the T–S fuzzy delayed hyperbolic PDE model, spatial linear matrix inequality (SLMI) based sufficient conditions to ensure exponential stability with an H ∞ performance are obtained via utilizing the Lyapunov direct method. Then, to solve the SLMIs, the robust non-fragile H ∞ fuzzy control problem for uncertain nonlinear delayed hyperbolic PDE systems is formulated as an LMI feasibility problem. Finally, two examples on a Lotka–Volterra PDE system and a non-isothermal plug-flow reactor (PFR) are presented to illustrate the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

This paper addresses mixed H 2 / H ∞ fault-tolerant sampled-data (SD) fuzzy control for nonlinear space-varying parabolic partial differential equation (PDE) system under deception attacks. Firstly, a T–S fuzzy PDE model is given to exactly describe the nonlinear space-varying parabolic PDE system. Secondly, a fault-tolerant SD fuzzy controller via the spatial linear matrix inequalities (SLMIs) is developed based on a Lyapunov functional which is continuous at sampling times but not necessary to be positive definite in sampling intervals such that the closed-loop PDE system is exponentially stable with a mixed H 2 / H ∞ performance. Then, to solve the SLMIs, the fault-tolerant SD fuzzy control problem for space-varying parabolic PDE system is formulated as linear matrix inequality feasibility problem. Furthermore, the design condition of the suboptimal mixed H 2 / H ∞ fault-tolerant SD controller subject to deception attacks can be derived by considering the property of membership functions. Lastly, two examples are given to illustrate the design method. [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]

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]

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 modeling of the piezoelectric actuator is very important for the fast and accurate nano-positioning control. However, the complex creep, vibration and hysteresis dynamics make the modeling very difficult. Because there are often model uncertainties in the first-principle model, the model identification from the experimental data is very necessary. Most identification approaches only consider partial dynamics of the creep, vibration and hysteresis. Though they can be combined together after they are individually identified from different experiments, it is difficult to design the completely decoupled experiment. In this study, an incremental Hammerstein-like modeling approach is proposed to identify the creep, vibration and hysteresis dynamics from one experiment. The model is called nonlinear-linear-linear-Hammerstein-like model, where one dynamic nonlinear system is used to model the hysteresis and two dynamic linear systems are used to model the creep and vibration. The creep and vibration are assumed to be decoupled, and there is a known upper bound on the order of the creep model. A two-stage incremental modeling approach is proposed to reduce the modeling complexity, where the slow dynamics of the hysteresis and creep are estimated first and then the residual of this model is used to estimate the fast dynamics of the vibration. In each stage, the model structure and order are determined by a locally regularized orthogonal least-squares-based model term selection algorithm and the parameters are estimated using a regularized least-squares method. The effectiveness of the proposed modeling approach is verified by the simulations and experiments on typical piezoelectric actuators. [ABSTRACT FROM AUTHOR]

In this paper, a robust distributed control design based on proportional plus second-order spatial derivative (P-sD $$^2$$ ) is proposed for exponential stabilization and minimization of spatial variation of a class of distributed parameter systems (DPSs) with spatiotemporal uncertainties, whose model is represented by parabolic partial differential equations with spatially varying coefficients. Based on the Lyapunov's direct method, a robust distributed P-sD $$^2$$ controller is developed to not only exponentially stabilize the DPS for all admissible spatiotemporal uncertainties but also minimize the spatial variation of the process. The outcome of the robust distributed P-sD $$^2$$ control problem is formulated as a spatial differential bilinear matrix inequality (SDBMI) problem. A local optimization algorithm that the SDBMI is treated as a double spatial differential linear matrix inequality (SDLMI) is presented to solve this SDBMI problem. Furthermore, the SDLMI optimization problem can be approximately solved via the finite difference method and the existing convex optimization techniques. Finally, the proposed design method is successfully applied to feedback control problem of the FitzHugh-Nagumo equation. [ABSTRACT FROM AUTHOR]

A type-2 hierarchical fuzzy system (T2HFS) is presented for the high-dimensional data-based modeling with uncertainties. Type-2 fuzzy logic system (T2FLS) is a powerful tool to handle uncertainties in complex processes. However, the operation of type-reduction has greatly increased the computational burden of T2FLSs. By integrating the T2FLS with hierarchical structure, a systematic design methodology of T2HFS is proposed to avoid the rule explosion and to simplify the computation complexity. The design methodology has included several procedures to establish the T2HFS. Firstly, the PCA-based method is developed to capture the prominent component from training data, and to determine the hierarchical structure of T2HFS. Furthermore, a novel clustering method is proposed to design the basic type-2 fuzzy logic unit (T2FLU) in uncertain environments. Finally, a hybrid-learning method is presented to fine-tune the parameters for the global optimization where the statistical and deterministic optimization methods are developed for the nominal and auxiliary performance, respectively. Simulation results have shown that the proposed T2HFS is very effective for the high-dimensional data-based modeling and control in uncertain environment. [ABSTRACT FROM AUTHOR]

A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. In this work, a regulatory model-based binding energy is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity, regulatory efficiency, and the activity level of transcription factor (TF) are incorporated into a general learning model. The sequence features of the promoter are exploited to derive the binding energy. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than some previous models can do. [ABSTRACT FROM AUTHOR]

As a widely used reinforcement learning method, Q-learning is bedeviled by the curse of dimensionality: The computational complexity grows dramatically with the size of state-action space. To combat this difficulty, an integrated hierarchical Q-learning framework is proposed based on the hybrid Markov decision process (MDP) using temporal abstraction instead of the simple MDP. The learning process is naturally organized into multiple levels of learning, e.g., quantitative (lower) level and qualitative (upper) level, which are modeled as MDP and semi-MDP (SMDP), respectively. This hierarchical control architecture constitutes a hybrid MDP as the model of hierarchical Q-learning, which bridges the two levels of learning. The proposed hierarchical Q-learning can scale up very well and speed up learning with the upper level learning process. Hence this approach is an effective integral learning and control scheme for complex problems. Several experiments are carried out using a puzzle problem in a gridworld environment and a navigation control problem for a mobile robot. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The traditional fuzzy logic system (FLS) can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature. [ABSTRACT FROM AUTHOR]

To improve the consistency of the adhesive amount dispensed by the time-pressure dispenser for semiconductor manufacturing, a non-Newtonian fluid flow rate model is developed to represent and estimate the adhesive amount dispensed in each cycle. Taking account of gas compressibility, an intelligent model-based control strategy is proposed to compensate the deviation of adhesive amount dispensed from the desired one. Both simulations and experiments show that the dispensing consistency is greatly improved by using the model-based control strategy developed in this paper. [ABSTRACT FROM AUTHOR]

In electronics manufacturing the time-pressure dispensing system has been widely used to squeeze adhesive fluid in a syringe onto boards or substrates with pressurised air. For accurate control of the fluid volume, it is vital to have a thorough understanding of the whole process. However, the complexity of the process, which includes air-fluid coupling and their nonlinear uncertainties, makes it difficult to obtain an accurate process model. Using the spectral method, an approximate fluid flow model for both the Newtonian and non-Newtonian fluid can be developed. By taking into account nonlinear flow passing through the valve, attenuation and time delay in the pneumatic lines, syringe chamber dynamics and fluid flow dynamics, a simple but effective model is derived to describe the whole dispensing system with reasonable accuracy. Experiments performed thereafter validate the model. [ABSTRACT FROM AUTHOR]