Nonlinear Thermal Process Research Articles

Gain scheduling model predictive controller design for tankless gas water heaters with time-varying delay

• Thermal model with time-varying delay is developed for a tankless gas water heater. • Feedback, feedforward and model predictive controllers are designed. • Controllers are assessed for cold start and water flow rate sudden disturbances. • Impact of temperature overshoots and undershoots are diminished. • Gain scheduling MPC presents superior performance than classic controllers. One of the most significant drawbacks of tankless gas water heaters is the difficulty of controlling the outlet hot water temperature as changes in water demand cannot be predicted. These sudden changes in hot water flow rate, are associated with the system inhered delays, and are responsible for temperature overshoots and undershoots that severely affect the comfort perception of the user. In the present work, a linear model predictive controller (MPC) design is employed for the stabilization of a constrained nonlinear thermal process with time-varying delay, however, control performance degrades significantly when operating far from the linearization operating point. The idea is to design multiple linear MPC controllers, each with its linear state-space model and constant time delay, describing process dynamics at a specific level of operation. Gain scheduling MPC presents improved performance when compared with classic PID and combined feedforward-feedback controllers, reducing the settling time up to two thirds.

Tuning Rules for Active Disturbance Rejection Controllers via Multiobjective Optimization—A Guide for Parameters Computation Based on Robustness

A set of tuning rules for Linear Active Disturbance Rejection Controller (LADRC) with three different levels of compromise between disturbance rejection and robustness is presented. The tuning rules are the result of a Multiobjective Optimization Design (MOOD) procedure followed by curve fitting and are intended as a tool for designers who seek to implement LADRC by considering the load disturbance response of processes whose behavior is approximated by a general first-order system with delay. The validation of the proposed tuning rules is done through illustrative examples and the control of a nonlinear thermal process. Compared to classical PID (Proportional-Integral-Derivative) and other LADRC tuning methods, the derived functions offer an improvement in either disturbance rejection, robustness or both design objectives.

Effect of Heating Spacing on Deformation Distribution of Line Heating Process

Ship hull plate forming with line heating technique is an important process in the shipbuilding industry. An automatic line heating control system needs an accurate database of various processing parameters. In this study, a numerical model, calibrated and verified using experiment results, was used to study the deformation distribution of a plate corresponding to different heating line spacing. This paper presents the results based on a single-torch and multiple-torch process. The deformation distributions in the heating and nonheating regions were analyzed. The impacts of coupling effect among heating lines were deliberated. 1. Introduction Line heating is an efficient plate forming process for many applications, such as ship hull forming, molding car body, and other curved objects. In the shipbuilding industry, this method is mainly used to create double-curved ship hull plates, especially for those plates near the bow and stern. Line heating is a complex nonlinear thermal process that involves thermodynamics, elastic-plastic mechanics, and metallurgy. Many researchers have conducted studies on the mechanism of line heating process to predict the final shape of the metal plate according to the heating conditions and mechanical properties of the plate (Moshaiov & Vorus 1987; Ueda et al. 1994a, 1994b, 1994c; Ji & Liu 2001); others performed numerical analysis with thermal elastic-plastic models to determine the parameters in line heating process (Jang et al. 1997; Yu et al. 1999,2001; Yu 2000; Wang et al. 2006). Many experimental works were carried out to verify the thermal elastic-plastic models (Moshaiov & Shin 1991; Liu et al. 2006; Zhou et al. 2014a). The effects of parallel and cross heating lines were discussed recently (Zhou et al. 2014b; Vega et al. 2015). Residual stress among the steel plate was studied to reveal the relationship of different heating conditions. Different heat sources have been used in line heating process, and they include gas torch(Tomita et al. 2001; Jong & Jong 2003), laser beam (Kyrsanidi et al. 1999), welding arc (Kitamura et al. 1996), and high-frequency induction heating (Ogawa et al. 1994; Lee & Hwang 2014). In all heat sources, gas torch is most frequently used as it was and still is the cheapest and most readily available heating source in most shipyards.

Численно-аналитическое моделирование и идентификация нестационарного теплового процесса с учетом радиационного теплообмена

Численно-аналитическое моделирование и идентификация нестационарного теплового процесса с учетом радиационного теплообмена

STRUCTURAL IDENTIFICATION OF A THERMAL PROCESS USING THE VOLTERRA MODEL

This paper proposes a new method to estimate, from input/output measurements, the structural parameters (order and memory) of the Volterra models used for describing a nonlinear thermal process, the Trainer PT326. The proposed estimation method is an extension of the recent work in which a new algorithm for estimating the memory of the Volterra model was proposed. The structure parameters identification method, proposed in this paper, is based on the definition of a specific matrix whose components are lagged inputs and lagged outputs. We prove that this matrix becomes singular once the parameter value exceeds its exact value. The estimated values of the order and the memory are used to provide a suitable Volterra model for the Process Trainer PT326. The performance of the model and the identification method that uses experimental data are evaluated.

Active Disturbance Rejection Control of Nonlinear Thermal Process

The main contribution of this paper is to design active disturbance rejection control for a nonlinear thermal process to track reference signals while operating under the influence of disturbances. A linear active disturbance rejection controller is designed through an extended state observer to estimate and compensate the uncertainties of the system. The simulation result demonstrates very good performance of the controller in the absence of an accurate mathematical model of the process.

Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression

The paper discusses the development of reliable multi-objective optimization based on Gaussian process regression (GPR) to optimize the high-speed wire-cut electrical discharge machining (WEDM-HS) process, considering mean current, on-time and off-time as input features and material remove rate (MRR) and Surface Roughness (SR) as output responses. In order to achieve an accurate estimation for the nonlinear electrical discharging and thermal erosion process, the multiple GPR models due to its simplicity and flexibility identify WEDM-HS process with measurement noise. Objective functions of predictive reliability multi-objectives optimization are built by probabilistic variance of predictive response used as empirical reliability measurement and responses of GPR models. Finally, the cluster class centers of Pareto front are the optional solutions to be chosen. Experiments on WEDM-HS (DK7732C2) are conducted to evaluate the proposed intelligent approach in terms of optimization process accuracy and reliability. The experimental result shows that GPR models have the advantage over other regressive models in terms of model accuracy and feature scaling and probabilistic variance. Given the regulable coefficient parameters, the experimental optimization and optional solutions show the effectiveness of controlling optimization process to acquire more reliable optimum predictive solutions.

Loop shaping for iterative control of batch processes

The article formulates an iterative learning control (ILC) design problem and modal analysis approach in the frequency domain that uses the Fourier transform. Following that, the controller design specifications are formulated in a frequency domain form. Next, the LQ/LTR controller design procedure is presented where the quadratic penalty weights are used to recover the design specifications. Finally, a simulated example of ILC in a highly nonlinear batch thermal process illustrates an application of the developed approach.

Identification of Interconnected MIMO Systems Application to Nonlinear Thermal Process

Abstract A new algorithm for estimation of the parameters of MIMO systems with strong interconnections between inputs and output. The proposed algorithm uses a new transformation of the input-output difference equation. A modified Kalman filter is employed to estimate recursively and sequentially parameters of the MIMO system and the unmeasurable partial outputs. The convergence analysis will be studies. To illustrate the performances of the proposed technique, we will proposed two examples. The first one is a simulation example and the second one is an application to nonlinear thermal process.

Weighted multi-model control

A multi-model control which combines the basic controls of the different models, according to the quality of representation of each model, is proposed. A real-time application of this technique to a non-linear thermal process is carried out and the results are given compared to the results of the same application of the other technique where one model only is selected, under the same conditions. This comparison shows the advantages of the combined control signal.

Application of tracking multimodel control to a non-linear thermal process

This paper presents one of the very important applications of the multimodel con-trot technique. This type of control enables us to extend the use of linear control theory to applications in non-linear complex processes. The process under control is modelled by several linear models around different points and modes of operation. During the application, the right model is selected among the available set of models and is used to generate suitable control signals which enable the process to track certain desired trajectories. This controller is built by the use of a microcomputer interfaced with a non-linear thermal process where the inside temperature and humidity are the controlled variables. The results of a real-time application to this process are given.

Multivariable Direct Adaptive Control of Thermal Mixing Processes

Multivariable direct adaptive control is tested on a nonlinear thermal mixing process and is compared with state space based nonadaptive controllers. The linear quadratic optimal control approach is used to design two nonadaptive controllers: one without integral action (ordinary LQ) and the other with integral action (LQI). The operating point is changed over a wide region in the experiment. The adaptive controller is verified to perform most consistently under the tested conditions.