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Modeling of the parabolic distributed parameter system (DPS) with the Karhunen-Loeve (KL) method under sparse sensing will become very difficult because the information from the measurements is inc...

We demonstrate a novel, innovative photothermal nanofibrous composite membrane for ultraviolet light-driven membrane distillation (UVMD) in which the photothermal nanoparticles, i.e., silver nanopa...

A new method for fabrication of thin film nanofibrous composite (TFNC) ultrafiltration (UF) membrane consisting of an ultrathin poly(acrylonitrile-co-acrylic acid) (PAN-AA) barrier layer based on a polyacrylonitrile (PAN) nanofibrous support layer was proposed in this study. First, a thin PAN-AA nanofibrous layer was electrospun and deposited on a thicker PAN nanofibrous substrate. Then, the as-prepared PAN-AA nanofibers were swollen in the alkaline buffer solution and merged imperceptibly as an integrated nonporous hydrogel layer on the PAN substrate. The PAN-AA hydrogel layer was cross-linked with different bivalent metal cations (Ca2+, Mg2+) to form an ultrathin barrier layer, of which the thickness and porosity were optimized by controlling the depositing time of PAN-AA nanofibers and pH value of buffer solution. Proteins with different molecular weights were used to evaluate the ultrafiltration performance of the resultant composite membranes. Due to its hydrophilic and negative charged barrier layer...

Many industrial processes are nonlinear distributed parameter systems (DPS) that have significant spatiotemporal dynamics. Due to different production and working conditions, they often need to work at a large operating range with multiple working points. However, direct global modeling and persistently exciting experiment in a large working region are very costly in many cases. The complex spatiotemporal coupling and infinite-dimensional nature make the problem more difficult. In this study, a kernel-based spatiotemporal multimodeling approach is proposed for the nonlinear DPS with multiple working points. To obtain a reasonable operating space division, an iterative approach is proposed where the operating space division and local modeling are performed iteratively. The working range of the current local model will help to determine the next operating point required for modeling. Utilizing the potential of each local model, the number of regions can be reduced. In the local modeling, the Karhunen–Loeve ...

Many industrial processes are nonlinear distributed parameter systems (DPSs). Data-based spatiotemporal modeling is required for analysis and control when the first-principles model is unknown. Because a DPS is infinite-dimensional and time–space coupled, a low-order model is necessary for prediction and control in practice. For low-order modeling, traditional principal component analysis (PCA) is often used for dimension reduction and time–space separation. However, it is a linear method and leads to only one set of fixed spatial basis functions. Therefore, it might not be always effective for nonlinear systems. In this study, a spatiotemporal multimodeling approach is proposed for unknown nonlinear DPSs. First, multimodel decomposition is performed, where probabilistic PCA (PPCA) is used to obtain multiple sets of spatial basis functions from the experimental data by maximizing a likelihood function. Using these multiple sets of PCA spatial bases for time–space separation, the high-dimensionality spatio...

Hammerstein modeling with structure identification for multi-input multi-output (MIMO) nonlinear industrial processes is investigated in this study. The structure identification of the Hammerstein model is very challenging because the model terms are vectors, and some model terms are inputs of other model terms (i.e., model term coupling). An efficient model structure selection algorithm for the Hammerstein model is proposed with the multi-output locally regularized orthogonal least-squares (LROLS), A-optimality design, and a vector model term selection. To enhance the well-posedness of the regressors, estimation robustness, and model adequacy, the A-optimality criterion is integrated into the model error reduction criterion in the multi-output LROLS. To handle the vector model term coupling problem, a vector model term selection rule is synthesized into the multi-output LROLS. After the model structure is determined, to improve the robustness of the parameter estimation, the regularized least-squares met...

Modeling of distributed parameter systems (DPSs) is difficult because of their infinite-dimensional spatiotemporal nature and complex nonlinearities. Data-based modeling is necessary because there ...

In this paper, a novel robust design method is proposed for the process design under uncertainty. The key idea is to integrate the robust optimal design with the robust eigenvalue assignment to design the process with the desirable steady-state design performance and the satisfactory dynamic performance. The robust optimal design is developed to achieve the desirable steady-state design performance through minimizing the steady-state objective function and its variation caused by uncertainty. The robust eigenvalue assignment is proposed to maintain the system eigenvalues in the desirable domain and make these eigenvalues less sensitive to uncertainty so that the system has the satisfactory dynamic response. The particle swarm optimization is proposed to solve the nonconvex and nondifferential integration problem. Finally, a continuously stirred tank reactor (CSTR) example is applied to demonstrate the effectiveness of the proposed integration method.

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.

In this article, an incremental modeling approach is proposed to model nonlinear distributed parameter systems, with the help of the newly constructed spatiotemporal Volterra kernels. The complex s...

An internal model control (IMC) based tuning method is proposed to autotune the fuzzy proportional integral derivative (PID) controller in this paper. An analytical model of the fuzzy PID controller is first derived, which consists of a linear PID controller and a nonlinear compensation item. The nonlinear compensation item can be considered as a process disturbance, and then parameters of the fuzzy PID controller can be analytically determined on the basis of the IMC structure. The stability of the fuzzy PID control system is analyzed using the Lyapunov stability theory. The simulation results demonstrate the effectiveness of the proposed tuning method.

The spatio-temporal modeling problem from the input and output measurements for distributed parameter processes under unknown circumstances is investigated. The traditional Wiener modeling is extended to nonlinear distributed parameter systems with the help of the Karhunen−Loeve (KL) decomposition. The input is a finite-dimensional temporal variable, whereas the spatio-temporal output of the system is measured at a finite number of spatial locations. First, the measured output is used to construct a finite dimensional approximation of the system output which is expanded in terms of KL spatial basis functions. Subsequently, the temporal coefficients are used to identify a Wiener model. The identification algorithm is based on the least-squares estimation and the instrumental variables method. The simulations for parabolic and hyperbolic systems are presented to show the effectiveness of this spatio-temporal modeling method.

In the nonlinear model predictive control (NMPC) field, it is well-known that the multistep control approach is superior to the single-step approach when examining high-order nonlinear systems. In the multistep control approach, however, the online minimization of a 2-norm square objective function over a control horizon of length M always requires solving a set of complex polynomial equations, for which no definite solution exists so far. Moreover, the complex nature of the receding horizon optimization also causes additional problems to its closed-loop stability analysis. With these two serious challenges in mind, using a Volterra−Laguerre model-based NMPC for discussion, we propose a general technique to extend the control horizon with the assistance of Groebner basis, which transforms the set of complex polynomial equations to a much simpler form. We prove the closed-loop stability of the algorithm in the sense that the input and output series are both mean-square-bounded. Finally, the efficiency of t...

The Smith predictor is the most effective control scheme for processes with dead time while the proportional−integral−derivative (PID) controller is the most widely used controller in industry. This paper presents a control scheme which combines their advantages. The proposed controller is inherently a PID-type controller in which the integral action is implemented using a delay unit rather than a pure integrator while retaining the advantage of the Smith predictor (Smith principle). The setpoint response and the disturbance response are decoupled from each other and can be designed separately. Another advantage of this control scheme is that the robustness is easy to analyze and can be guaranteed explicitly, compromising between the robustness and the disturbance response. Examples show that this controller is very effective in the control of processes with dead time.