In this paper, functional prediction is carried out for spatio-temporal systems in which the spatial data is irregularly sampled. We propose a novel method called Kalman Filter Radial Basis Function (KF-RBF) for such ...
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(纸本)9781424451920
In this paper, functional prediction is carried out for spatio-temporal systems in which the spatial data is irregularly sampled. We propose a novel method called Kalman Filter Radial Basis Function (KF-RBF) for such a purpose. It casts the problem into a Reproducing Kernel Hilbert Space (RKHS) defined by some continuous, symmetric and positive definite Radial Basis Function (RBF), thereby allowing for irregular sampling in the spatial domain. A Functional Auto-Regressive (FAR) model describing the system evolution in the temporal domain is further assumed. The FAR model is then formulated as a Vector Auto-Regressive (VAR) model embedded into a Kalman Filter (KF). This is achieved by projecting the unknown functions onto a time-invariant functional subspace. Subsequently, the weight vectors obtained become inputs into a Kalman Filter (KF). In this way, nonstationary functions can be forecasted by evolving these weight vectors.
In this paper, we will overview various control approaches in Fluid Flow fields. Facing the problems in 1D, 2D or 3D fluid flow, in the past, people proposed lots of control approaches for solving different cases. Now...
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In this paper, we will overview various control approaches in Fluid Flow fields. Facing the problems in 1D, 2D or 3D fluid flow, in the past, people proposed lots of control approaches for solving different cases. Now, some previous works in dealing with Burgers equations and Navier Stokes equations' problems is discussed in this paper. And most of the approaches are useful to inspire researchers to make further study in this area.
This paper presents an investigation into application of controller tuning using improved bacterial foraging algorithm (BFA) namely exponentially adaptive BFA (EABFA). The objective of the work is to evaluate the perf...
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This paper presents an investigation into application of controller tuning using improved bacterial foraging algorithm (BFA) namely exponentially adaptive BFA (EABFA). The objective of the work is to evaluate the performance of EABFA in the controller tuning of a joint-based collocated (JBC) proportional-derivative (PD) control system. A simulation model of a single-link flexible manipulator system that incorporates hub inertia, structural damping and payload at the end-point of flexible arm is used as a test bed. JBC tuned by BFA is used to control the hub angular movement. An adaptable chemotactic step size mechanism that incorporates exponential function of the nutrient value is introduced to improve the original BFA. The developed EABFA has faster convergence with better or comparable optimum results than that of the original BFA. The performance of EABFA is assessed based on its convergence, optimum nutrient value and time-domain hub-angular response of the manipulator system.
The use of active control technique has intensified in various control applications, particularly in the field of aircraft systems. A laboratory set-up system which resembles the behaviour of a helicopter, namely twin...
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The use of active control technique has intensified in various control applications, particularly in the field of aircraft systems. A laboratory set-up system which resembles the behaviour of a helicopter, namely twin rotor multi-input multi-output system (TRMS) is used as an experimental rig in this research. This paper presents an investigation using inverse model control for the TRMS. The control techniques embraced in this work are direct inverse-model control, augmented PID with feedforward inverse-model control and augmented PID with feedback inverse-model control. Particle swarm optimization (PSO) method is used to tune the parameter of PID controller. To demonstrate the applicability of the methods, a simulated hovering motion of the TRMS, derived from experimental data is considered. The proposed inverse model based controller is shown to be capable of handling both systems dynamic as well as rigid body motion of the system, providing good overall system performance.
Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in mo...
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Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in modelling of highly nonlinear components in the system. A laboratory set-up that resembles the behaviour of a helicopter, namely twin rotor multi-input multi-output system (TRMS) is used as an experimental rig in this research. An adaptive neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm is developed in search for non-parametric model for the TRMS. The antecedent parameters of the ANFIS are optimized by a PSO algorithm and the consequent parameters are updated using recursive least squares (RLS). The results show that the proposed technique has better convergence and better performance in modeling of a nonlinear process. The identified model is justified and validated in both time domain and frequency domain.
This paper presents investigations of modeling the flexible plate structures using particle swarm optimization (PSO) and active vibration control (AVC) of such structures. The optimization technique is utilized to obt...
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This paper presents investigations of modeling the flexible plate structures using particle swarm optimization (PSO) and active vibration control (AVC) of such structures. The optimization technique is utilized to obtain a dynamic model of a flexible plate structure based on auto-regressive with exogenous (ARX) input structure. The structure is subjected to two different disturbance signal types, namely random, and finite duration step. The fitness function for the PSO is the mean-squared error (MSE) between the measured and estimated outputs of the plate. The validation of the algorithm is presented in both time and frequency domains. The developed PSO modeling approach is used for AVC system design to suppress the vibration of the flexible plate. The performance of the controller is assessed in terms of level of attenuation achieved in the power spectral density (PSD) of the observed signal.
A new optimal design method and a systematic scheduling approach for a laboratory-scale Hot-Rolling Mill are presented. The proposed design is based upon 1. metallurgical principles, which sufficiently consider the be...
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A new optimal design method and a systematic scheduling approach for a laboratory-scale Hot-Rolling Mill are presented. The proposed design is based upon 1. metallurgical principles, which sufficiently consider the behaviour of workpiece material and the mechanics of the manufacturing process and 2. a modified version for multi-objective optimization of a Population Adaptive based Immune Algorithm (PAIA), physically-based models and symbiotic modelling approach to carry-out an optimal search for the best microstructural parameters. This methodology possesses adequate capabilities for finding effective and best microstructural parameters such as the ferrite grain size and the volume fraction of pearlite that satisfy the requirements for mechanical properties. Hence, the overarching aim of this research work is to integrate knowledge about both the stock and the rolling process to find optimal hot-deformation profiles that will be used as information in order to compute the most suitable rolling schedule and systemise the optimal route for processing and achieve a `right-first-time' production of the desired properties.
This paper presents a global monitoring and fault detection approach considering the different operation points, start-ups and transitory states that can appear during plant operation. Stationary states have been moni...
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This paper presents a global monitoring and fault detection approach considering the different operation points, start-ups and transitory states that can appear during plant operation. Stationary states have been monitored using the classical PCA approach and start-ups, while grade transition due to, for example changes in the set-point, are monitored using batch and semi-batch PCA-based methods. These methods have been adapted to deal with this type of problems. Thus, theoretical aspects of PCA and unfolded PCA will be presented and explained. The method developed has been tested in a laboratory pilot plant, using different protocols and grade transitions to examine the method's possibilities with good results.
This paper reports on an efficient algorithm for locating the `optimal' solutions for multi-objective optimization problems by combining a state-of-the-art optimizer with a fitness model-estimate. This hybrid fram...
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This paper reports on an efficient algorithm for locating the `optimal' solutions for multi-objective optimization problems by combining a state-of-the-art optimizer with a fitness model-estimate. This hybrid framework is introduced to illustrate how to make sufficient use of an approximate model, which includes a `controlled' process and an `uncontrolled' process during the search. With the inclusion of such approximate model in the optimization block, a global reseeding strategy based on previous data is also applied to improve the ability of the multi-objective optimizer to find global set of solutions (`pareto' solutions). To this effect, the popular algorithm, NSGA-II, and a Multi-Layer Perceptron Neural Network (MLP) are combined synergetically to show details of such processing. Furthermore, a simple (but no simpler) method for selecting the `training' data necessary for eliciting the fitness landscape model is suggested to address what are now a common engineering problems, in particular those associated with sparse data distributions and objectives converging at significantly different speeds. To test the validity of the proposed multi-objective scheme, a series of simulation experiments, using well-know benchmark functions, are conducted and are compared to those carried-out while using the original NSGA-II and SPEA-2, under similar conditions. The proposed method is also applied to the `optimal' design of alloy steels in terms of chemical compositions and processing conditions and is shown to perform very well.
Reverse Osmosis (RO) is the most common technique to produce drinkable water in arid regions. The desalination plants are usually designed for a short number of constant operation points. A dynamic strategy of control...
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Reverse Osmosis (RO) is the most common technique to produce drinkable water in arid regions. The desalination plants are usually designed for a short number of constant operation points. A dynamic strategy of control would help to decrease the operation expense and the equipments cost. However, the lack of dynamic simulation tools for this kind of plants, avoids the optimal design, which would use a continuously changeable operation point. For this, a new dynamic simulation library of RO plants was presented elsewhere. Now, the present paper deals with the validation of that library, using experimental data from a real RO plant.
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